Digital Twin Development: An Introduction to Simcenter Amesim 3031256913, 9783031256912

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Frank U. Rückert Michael Sauer Tuomo Liimatainen Dirk Hübner

Digital Twin Development An Introduction to Simcenter Amesim

Digital Twin Development

Frank U. Rückert · Michael Sauer · Tuomo Liimatainen · Dirk Hübner

Digital Twin Development An Introduction to Simcenter Amesim

Frank U. Rückert Fluid Energy Machines University of Applied Sciences Saarbrücken Saarbrücken, Saarland, Germany

Michael Sauer Fluid Machinery and Measurement University of Applied Sciences Saarbrücken Saarbrücken, Saarland, Germany

Tuomo Liimatainen Mechanical Engineering LAB University of Applied Sciences Lappeenranta, Etelä-Suomi, Finland

Dirk Hübner Lightweight Construction University of Applied Sciences Saarbrücken Saarbrücken, Saarland, Germany

ISBN 978-3-031-25691-2 ISBN 978-3-031-25692-9 (eBook) https://doi.org/10.1007/978-3-031-25692-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book is dedicated to our families who gave us time together for writing it. We worked on it from winter semester of 2020 until summer of 2022 at the University of Applied Sciences Saarbrücken in Germany. Additionally, a seminar on this topic was conducted at the LAB University of Applied Sciences in Lappeenranta, Finland, during the summer session in order to get feedback from international English-speaking students. To assess in-course progress, students were asked to provide feedback, suggest outcome improvements, and avoid any possible misunderstandings. The authors have many years of experience in creating sophisticated simulation models for complex, technical solutions in automotive industry and power plant research. In order to improve further development in the field of renewable energies, we added examples of this subject area. For us, it was important that the students could easily start building their own digital twins for complex technical systems. It was important that students explore the various parts of the model or initially develop uncomplicated systems to examine the behavior in a user-friendly environment thereby learning a step-by-step approach. The graphical visualization of the innovation helped to analyze and solve technical problems much faster. The software tool, Simcenter Amesim, was provided free of charge by Siemens for study purposes, increasing time efficiency and minimizing challenges through modeling. Networking and collaboration during the development process facilitated the exchange of information effortlessly. As model-based engineering continues to grow, it is important to start educating young ones early on. Although our book was written for students of natural sciences, engineering, or information technologies, the language is deliberately kept easy to understand for anyone who is interested in the development of digital twins for physical problems and/or wants to start modeling. Especially for students, the early possibility of a free, easy-to-understand simulation environment can enrich physics lessons at school and contribute significantly to the understanding of technical relationships of the models. Our book takes into account the feedback given to us by the students and will help especially novices to find alternative ways to use the simulation tools. We expect that

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the book will help to make the subject easy to understand and that the reader will find it educational and enjoyable when developing new technical solutions and innovations. Saarbrücken, Germany October 2022

Frank U. Rückert

Acknowledgements

It is anticipated that interested readers will have as much fun reading this book and playing with the models as we had during writing the chapters and compiling the simulation examples. Our aim is that students will have fun while modeling easy solutions without struggling because of antiquated and unnecessary mathematical doctrine and quibbles. We would like to thank our long-time industrial companions Stephan Wursthorn, Christian Steinbrecher, Heiko Roth, Gerhard Sünderhauf, Christoph Magel, Thilo Klingel, Martin Katz, and Sibel Yilmaz from Robert Bosch GmbH. We also want to thank Rüdiger Thieman and Rolf Hartge for their support. During many discussions, they taught us a lot, while we spent hours working together with Simcenter Amesim on technical problems and innovations, we drank a cup or two of tea or coffee. We thank our faithful and hardworking assistants and students at the University of Applied Sciences Saarbrücken, namely Philipp Spindler, Benjamin Allweyer, Sebastian Grün, Emile Degro, Xenia Hülsmann, Tarek Khiar, Daniel Lehser-Pfeffermann, Alexander Hamman, Johannes König, Yannick Planta, Badre Ait Amghar, Tim Dennemärker, Barbara Kaiser, Nils Kleiber, Jan Molter, Christian Boiger and Tim Breuer, who created many simulation models and examples for this work. Tim Breuer passed away during the Corona pandemic, and we will always remember him and his service fondly. I want to thank the students who participated in our lectures about Digital Twins during Summer School 2021 and 2022 at LAB University of Applied Sciences. We would like to thank the staff of the International Office in Saarbrücken and Lappeenranta, namely Doris Kollmann, for the financial support within the Erasmus+ program and the European Union, we hope this will be possible again in the near future. We want to thank Michael Sandoval for reading the manuscript. His positive feedback was an enrichment. We would also like to thank Claudio Santarelli and Helge Tielbörger from Siemens AG for their kind support, help, and good advice. Last but not least, a big thank you to our friends at Lake Saimaa in Lappeenranta, Finland, who were such good hosts during wartime. Thanks to Tuomo for giving me the chance for holding my lecture at LAB. It means a lot to me that you gave my mother a moose antler. Finally, we would like to thank Michael Sauer for repairing the boat. vii

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Motivation from History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 What Is a Digital Twin? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 How to Create a Twin? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Adding Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Analysis of Digital Twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 5 8 10 11

2

Mathematics, Signals and Control Library . . . . . . . . . . . . . . . . . . . . . . 2.1 The First Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 How to Build a Simple Calculator . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13 13 13 21 21

3

The Mechanical Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 What Is Mechanics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 The Model of a Bouncing Ball . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 The Mechanical Rocker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 How a Row on a Pulley Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Damper of a Driving Car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 23 23 24 25 27 29 29 29 30 31 32 33 33 34 36 37 37 ix

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3.5.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 How to Use 3D Geometries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38 38 39 41 42 45

4

The Thermal Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Examination of Heat Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Heat Transfer in Electric Generators . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Site Selection for a Solar Collector . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47 47 48 48 49 49 52 52 53 54 55 57 57

5

The Hydraulic Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 What Is Hydraulics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Two Fuel Oil Tanks and a Pump . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 How Does a Hydraulic Jack Work? . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 59 59 60 60 62 63 64 64 65 65 68 69

6

The Pneumatic Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Pneumatic, Fluid Flow, and Turbulence . . . . . . . . . . . . . . . . . . . . . . 6.2 Safety Valve for a Biogas Tank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Ventilation System of a Building . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71 71 72 72 73 74 74 75 75 77 77

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Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 79

7

The Electric Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Permanent Electric Motor with Load . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 How an Asynchronous Motor Works? . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Electric Generator with Resistor and Heat Transfer . . . . . . . . . . . . 7.3.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

81 81 81 83 84 85 85 86 86 86 87 88 88 89 90 92 93

8

Analysis of Complex Technical Systems . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 How Does the Liquid Piston Compressor Work? . . . . . . . . . . . . . . 8.2 Design and Function of a Liquid Piston Compressor . . . . . . . . . . . 8.3 Liquid Piston Compressor with One Cylinder for One Stroke . . . 8.3.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Liquid Piston Compressor with One Cylinder and Multiple Strokes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Liquid Piston Compressor with Two Cylinder and Multiple Strokes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

95 95 95 96 96 97 97

105 105 107 107 109 110

Digital Twins and Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Neural Networks in Nature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Neural Networks and Digital Twins . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 The Artificial Frog Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Submodels and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . .

111 111 111 114 115

9

102 102 103 103

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9.3.2 Optiflow Neural Network (ONN) . . . . . . . . . . . . . . . . . . . . . 9.3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

117 119 119 120

10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Overview of This Textbook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 What You Can Take Away . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Teaching Methods and Gamification . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Our Outlook for the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Important: Disclaimer for Our Work . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

121 121 122 123 124 124 125

Chapter 1

Introduction

1.1 Motivation from History What could early humans have done to plan ahead for their problem-solving to save resources and energy? One first step could be to draw a situation in order to become familiar with different aspects of the problem. Figure 1.1 shows such a situation. It’s a picture painted after early petroglyphs located at Bryce Canyon National Park in Utah (USA). It shows a hunter on his horse and a deer [1]. Similar illustrations can be found all over the world. If you look closely at the scene, you can almost imagine the arrow moving. The purpose of problem visualization was used to describe and solve a specific situation. Additionally, the picture could be used to teach younger hunters with less experience. The petroglyph also depicts technical equipment. Drawing on a wall can be considered as an early kind of simulation or analysis of a specific situation. Today, the first thing students are advised to do is to draw a sketch of a technical device. Visualizing a problem at least helps to analyze and solve it. We believe that the petroglyph scene was for teaching or self-reflection. Knowing what to do is as important today as it was in the past. What we don’t see in Fig. 1.1 is that there are connections between the different objects. They are not painted, but in fact, there is a story and a connection between the different symbols and people. Two larger persons with antlers on their heads could also be identified as supervisors evaluating the situation and the quality of the hunting scene. We want to demonstrate the idea behind simulation models for problem-solving processes and how we can create artificial twins for any kind of problem. The core problem is that today’s technical solutions are becoming more and more complicated. More details are included, leading to unexpected behavior. Simulation models can help understand your work, but—beware—they can also make it worse because they make the system more complex. Too much ingenuity can very quickly lead to failure. An engineer has to understand the aim and purpose of his simulation to answer the question why. After that, answer to question how is often more trivial. At the end of this book, in the Conclusions chapter, we will © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_1

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1 Introduction

Fig. 1.1 Sketch after a scene from petroglyphs in Bryce Canyon National Park, USA [1]

discuss further methods for teaching and introduce an additional new idea on how to teach complex technical issues very quickly by introducing a role play method. During creating the so-called digital twins, we wanted to introduce the simulation tool Simcenter Amesim, which can be used to simulate the behavior of systems. The software we use is from Siemens PLM Software [2]. A free-of-charge demo version can be installed without much effort on Microsoft Windows or Linux systems. Working on Apple operation systems, one can use the program via a virtual private network (VPN) connection. Of course, this is less convenient. The software tool contains a ready-made library, including smaller models to simulate pneumatic, hydraulic, thermal, electrical, and mechanical behavior. It additionally includes a theoretical library for signals, mathematics, and artificial intelligence, e.g. artificial neural networks (ANN) or reduced order modeling (ROM). We will explain the basic functions and how to start modeling with this software tool. It is considered as a multi-domain software, meaning that all the different physical domains can be linked with each other or with the signal library. The software offers the possibility of simulating systems without the need for computer-aided design (CAD) geometry, but 3D geometries can be added and controlled by the models if this is needed. These models can also be printed with a 3D printer. In former times, small models made of wood or clay were built to study the behavior of systems. Today, our students are fascinated by 3D printing, which plays a similar role. The driver is similar to that of the petroglyphs, and is why we use it for a clearer understanding. The usable symbols for the different libraries in Simcenter Amesim are partly standard symbols defined and therefore easy to recognize. The software allows it to interact with other programs such as Excel or MATLAB/Simulink. One big advantage is, if compared, for example, with Simulink. Many industrial models are already finished and can be used as reference cases from the onset.

1.2 What Is a Digital Twin?

3

In order to bring the basics closer in the study, it was necessary to plan a lecture with the contents, e.g. the artificial model construction and the practical application of the used software. As previously described, the fascination lies not only in the manual creation of the simulation model but also in the possibilities that arise when one prototype has to be constructed. With a digital twin, technical, physical, or biological components can be tested long before they are used. The digital twin must reproduce certain properties of its realistic model to allow its functions and behavior to be scrutinized. The earlier in the development process the engineer has the opportunity to make quantitative statements about the function of a component in the system, the more effective a later design or development process can be. The digital twin is the image of its real counterpart and represents the first step in the development process. In our daily work experience, there is a difference between creating and using a numerical model. Even though the creation of the twin requires an expert who must know the process to be mapped down to the smallest detail, the user of the digital twin does not need to understand everything in detail in order to work with the twin. We use the example of the light switch. You do not have to understand how it works to use it. The gap between creation and use should be bridged by this book. Possibly this was also one idea behind the petroglyph scene in Fig. 1.1. The relevance of this topic in today’s world is obvious. Also in the most diverse areas of product development. Today, the topic of sustainability also encompasses the behavior of a product over its lifetime cycle. This means that a simulation model should not only make a statement about how a technical product works and which functionality in the various phases of its life but also how large an effect really is and what its consequences are is only known to a very limited extent. Today, for example, a very small amount of a powder containing anthrax virus can start a war. Quantities of harmless gas that are only in the ppm range (ppm = parts per million) can bring an entire industry to a standstill. On the other hand, sometimes small amounts of a virus are enough to have a very large effect. In such a case, the complexity of the overall system is crucial. When we build systems more complex, they get more susceptible to small quantities.

1.2 What Is a Digital Twin? When creating the model or digital twin from reality, it should be noted that the original cannot be reproduced in its entirety. Rather, certain properties are to be reproduced in isolation, i.e. in digital form as in Fig. 1.2. What is meant by this? We can explain it by the example of a human body or better with a prometheus, as described in [3]. In the case of this human being, like in Fig. 1.2, it can be possible to digitally reproduce only the blood vessels and thus create a kind of hydraulic twin. Such a twin would not provide any information about the bone structure. To represent the statics of the bones of the human being, one would have to create a mechanical twin.

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Fig. 1.2 Digital twin is the counterpart to the real twin, but with limited capabilities

The original can therefore have different digital twin brothers, which only partially reflect the physical or geometric properties of their real counterpart. A thermal twin could, for example, allow conclusions to be drawn about whether the subject has a fever or how quickly he cools down in winter, but not whether there is a fracture in the thigh. The pneumatic twin could, for example, provide conclusions about lung function or movements of other compressible fluids inside the body. For the human nervous system, there is a so-called signal database for neural networks. To control the digital twin, the nervous system is needed, just like in real life. This shows how important the idea of artificial intelligence (AI) is. To explain in detail how to work with it in Amesim, we will first start in the following chapter by rebuilding a simple calculator with the signal database. At the end, we explain what a neural network is and how it can be used to control our twin. As can be seen in Fig. 1.3 the digital twin is a virtual replica consisting of data and algorithms of a real object or product. This replica can be a model, a simulation, or even an algorithm that emulates the behavior of real objects. this offers the advantages of testing and simulating systems and products in advance. Parameters of the virtual image are set and tested, the results can be output and parameters can be adjusted quickly. The goal is to simplify planning and thus save time and costs. Application types of a digital twin can be distinguished depending on the product life phase. In the planning phase, the digital product twin is the preliminary product a computer-aided design (CAD) or 3D model. The model helps to examine the behavior of the product in different phases to make forecasts. A virtual model can be quickly adapted and also respond to customer requests. The digital production twin is used to forecast efficiency and quality in manufacturing. Furthermore, the virtual variant can be used to test deliveries and the availability of materials in advance.

1.3 How to Create a Twin?

5

Fig. 1.3 Different use cases can lead to different digital twins

All digital twins can be divided into several archetypes. Van der Valk et al. divided them into five different categories or so-called archetypes to compare the degree of evolution [4]. In Table 1.1, all archetypes are presented.

1.3 How to Create a Twin? As described above, we provide the reader with the opportunity for an easy introduction to the topic. As shown in Fig. 1.4, various technical systems are, e.g. simple fluid energy machines, which can initially be understood as a system model. Fig. 1.4 illustrates that components from different disciplines can be combined and thus built up into complex, cyber-physical systems. Thus, the technical system can then include thermal, pneumatic, mechanical as well as hydraulic or electric components. Once the system has been set up, it is possible to study the behavior of, for example, a fluid energy machine over a period of time. The goal is to be able to perform a virtual investigation in an isolated space with it.

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Table 1.1 Definitions of the archetypes of digital twins [4] Archetype Description Basic Digital Twin

Enriched Digital Twin

Autonomous Control Twin

Enhanced Autonomous Control Twin

Exhaustive Twin

This digital twin provides an human-machine interface (HMI). It therefore extends the digital twin by a single feature and is therefore considered fundamental Based on basic archetype, this digital twin enriches its database by preprocessed data from supplementary systems. Many objects in this cluster describe the possibility of semi-manual data collection This twin is an advancement from enriched digital twin. It offers autonomous control, but at the same time, it contains an HMI for the option to intervene. As direct communication with another virtual or physical machine, this archetype needs at least interoperability via a translator interface The digital twin offers autonomous control over a physical asset while integrating external, downstream data processing systems. The interoperability needs to be secured at least over a translator as well. On the downside, this archetype does not offer an HMI A digital twin with exhaustive data acquisition options, data processing, and control over a physical asset. This archetype provides the user with all options. The twin is able to work and control autonomously. At each point, humans have the ability to intervene or to enrich the database and, hence, provide a semi-manual data linkage. This archetype demands a fully interoperable data linkage to downstream systems as well as to the physical asset itself

As already described, no programming skills are required for the creation of the twins, and we will not dwell on mathematical subtleties. Mathematics should be subordinated to the development process as an auxiliary science and not pursued as an end in itself. We want to get results as quickly as possible. The creation of the physical model is facilitated in the platform by numerous well-documented examples. A very detailed illustrated documentation of the theoretical basics is also available. The company Siemens decided to make its development tool Simcenter Amesim available free of charge. It gives the possibility to create digital twins as preliminary stage of the design of a new product with little effort. The program can be used by university staff and students free of charge. Integration into the teaching process and promotion of young academics is thus facilitated. A license must be purchased as soon as Amesim is used commercially.

1.3 How to Create a Twin?

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Fig. 1.4 Example of combined models: propeller, mechanical friction, solar collector, and hydraulic pump

Important: You can test the program before you buy it, find a free student version of Simcenter Amesim at the following link: https://www.plm.automation.siemens.com/plmapp/education/simcenter/ en_us/free-software/student

Paths for software can change, and books are usually more persistent than websites on the Internet. Therefore, the specified path may no longer be correct. In this case, try to find the described software using a common search engine. The free-of-charge version from Siemens has some restrictions such as limitation of the library. With this student version, the tasks were designed for a lecture plan of this textbook. Only the available libraries are explained in more detail below and all examples could be done with the free version. The user interface of Simcenter Amesim illustrated in Fig. 1.5 is mostly operated graphically with the mouse. Here, the graphical program interface is illustrated with the digital twin of a bouncing ball. The program offers a tidy interface, which is initially divided into a workbench, as well as different library windows. On the workbench, different items can be dragged from the libraries with the mouse and connected with each other in the so-called sketch mode. The time period over which the digital twin is to be observed in a virtual laboratory is then defined.

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Fig. 1.5 Graphical user interface of Simcenter Amesim offers a clear overview

1.4 Adding Physics All subsequent chapters of this book are structured in the same way for better clarity and how to work with it. The chapters are always divided, first into the section Simulation model where the technical system has to be sketched by the students with the mouse on the workbench. This first step should be followed by a section on the text page Submodels and Parameters where the exact physical quantities for the simulation model must be specified. Here, you need to go into detail. Finally, there is always a section presenting the simulation results and a reference to further work proposals, where modeling, simulation, monitoring and mirroring are essential [5]. To create the model or simulation network, Simcenter Amesim provides an integrated simulation platform for cyber-physical problems and multidisciplinary system simulation at different stages of innovation and life cycle and could be divided into autonomous, federated, and digital twin maturity models. Important: Please do not focus too much on detailed physical quantities and variables at the beginning. The overview of the problem is more important. Parameters and boundary conditions can be changed later. We have several categories of libraries available to share below. Modeling with Simcenter Amesim takes place in four steps. First, a closed model must be created using the components from the existing libraries. For this purpose, it is also important to define used fluids or media. In the Submodel mode, the physical model is selected,

1.4 Adding Physics

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Table 1.2 Definitions of different modes in Simcenter Amesim Mode Description Sketch mode Submodel mode Parameter mode Simulation mode

Drawing the model on the worksheet Selection of the different submodels from the libraries Detailed definition of the physical parameters Run and review the simulation and graph the results

which is assigned to each component. The third step is processed in the Parameter mode as given in Table 1.2. Between the step from Submodel mode to Parameter mode, a compilation of the model takes place. The individual mathematical models become an overall model description. In Parameter mode, the required parameters for the physics of the components are set. Here, you have to take care about the quantity of the models. For the simulation, appropriate settings can be made such as the time, resolution, or whether the calculations should be dynamic or stable behavior. After that, the simulation run can be started in the Simulation mode and the results can be analyzed. To add physics for the technical problem we have to choose the right physical library. Each library has its own color. The signal library is usually illustrated in red color. We want to take the chance to introduce the functions of Amesim in detail. • Signals and Control library The signal library contains different mathematical functions and is used to control the other libraries. Basic mathematical functions are available. We will explain in the next chapter how to use them. This library contains artificial intelligence models. • Mechanical library This category contains linear and rotary motion elements. It is often used to complement other library categories to simulate mechanical ratios. Components such as masses, springs, dampers, friction elements, and also transmission ratios can be found in this section. • Hydraulic library This section contains general hydraulic components such as pumps, pipes, and valves for the simulation of the dynamic behavior of fluids. • Pneumatic library It contains components that are used to model compressed air-based systems. Typical components include pressure and flow sources, valves, or gas properties. • Thermal library With the thermal library, heat states and interactions can be simulated. The thermal components such as convection, conduction, or even radiation elements enable calculations of heat flow and losses. • Electric library In this library are electrical components with which systems can be reproduced, especially for automotive electronics. Components are resistors, inductors, capacitors, batteries, and also already prefabricated motors. The applications of the presented program are described in more detail in further sections. The color of the items can also be changed by right-clicking it with the mouse. But at the beginning, we should leave it in the default color for better understanding.

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1.5 Analysis of Digital Twins Various classical mathematical methods can be used for the analysis of digital twins. A 3D geometry helps to understand the large amount of data through visualization. An example of such a digital twin coupled with a 3D geometry is illustrated in Fig. 1.6. The figure shows the model of a transporter chassis driving over an obstacle. For this purpose, these hydraulic and mechanical models must be further developed and certain skills must be trained by algorithms. The algorithms can be used to control the 3D models in a virtual space, e.g. the transporter has a drive unit and a hydraulic damping system. It has the task of driving over a stone sidewalk which produces a lot of data. It should be mentioned that the amount of data can be high, but not so high that we should call it Big Data [6]. The figure shows how the chassis drives over the stone, and its functionality can be tested under real conditions. Due to its hydraulic suspension and the pneumatic model of the tires, the transporter succeeds in doing so and is able to drive independently from one side of the slab to the other. The physics of the hydraulic damping system is realistically reproduced according to physical principles. Using the data for training certain skills such as structural thinking is covered by the terms machine learning or deep learning. These are two sub-fields of artificial intelligence (AI). This would allow the chassis model to learn how best to drive over the obstacle. While machine learning is the generic term for allowing an algorithm to learn further by feeding it data, deep learning is a method of machine learning that builds on artificial neural networks. We will

Fig. 1.6 3D geometry and damping of a transporter chassis can be tested under realistic conditions

References

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describe this in detail in the last chapter of this book. Machine learning, by feeding structured data, can create an algorithm that can analyze new information based on the training. Deep learning can handle large unstructured data but requires powerful computers to do so. Artificial intelligence technologies offer a wide range from weak AIs that are used to solve limited tasks to strong AIs that emulate human intelligence [7]. Their use is intended to be a support for humans in the industrial, or also in the domestic area, to deal with information overload and to work autonomously. In the production process, for example, there is potential for this in research and development, in-process optimization, or also for maintenance systems for machine maintenance [7]. To create an accurate virtual image and thus minimize subsequent production errors, it is advantageous to enter as much data as possible. Digital twins could assist to generate this data. To deal with a large amount of data, data processing techniques for Big Data will become necessary [6]. It is used to present the information so that it is available quickly and can be used effectively by companies. It must be able to quickly capture and collect the data before it can be processed further.

References 1. Hiser, D.: Records of the Environmental Protection Agency, The Environmental Protection Agency’s Program to Photographically Document Subjects of Environmental Concern, 1972– 1977. https://catalog.archives.gov/id/545671 2. Siemens AG: Simcenter Amesim. https://www.plm.automation.siemens.com/ 3. Shelley Wollstonecraft, M.: Frankenstein, or, The Modern Prometheus. Oxford University Press, New York (1818) 4. Hendrik van der Valk, H., Hasse, H., Möller, F., Otto, B.: Archetypes of digital twins. Bus. Inf. Syst. Eng. 64(3), 375–391 (2022) 5. Kim, Y.-W.: Digital Twin maturity model, WEB 3D 2020 industrial use cases workshop on digital twin visualization. https://doi.org/10.13140/RG.2.2.28750.48967 (2020) 6. Weisberg Business Consulting GmbH: Einfach erklärt: Was ist Big Data? https://weissenbergsolutions.de/einfach-erklaert-was-ist-big-data/ 7. Smith, S.E.: Neuromuscular junction. In: Zaimis, E. (ed.) Handbook of Experimental Pharmacology, vol. 42, p. 593. Springer, Heidelberg (1976)

Chapter 2

Mathematics, Signals and Control Library

2.1 The First Steps Before we get into the interesting, physical issues, let’s briefly revisit the topic of signals and mathematics [1]. This rather dry topic is necessary because we will come across the use of so-called signals again and again in the further course of modeling. To show how Simcenter Amesim works, we will first create a simple calculator. After starting Amesim, the first thing that opens is the program’s interface. The first step to create a model is to drag single items from the model library to the drawing layer of the program shown in Fig. 2.1. In this book, all examples and exercises were created under the operating system Microsoft Windows. But there is also the possibility to run Amesim under the operating system Linux. This can be advantageous if you want to couple the program with other simulation tools or if you want to automate the models. Here, however, we want to operate Amesim exclusively via the graphical user interface (GUI). We start with the first example in the following chapter, to perform a simple calculation like: 1+2=3

(2.1)

We want to build up the model for this mathematical calculation by trying to recreate a calculator with the signal library (red library).

2.2 How to Build a Simple Calculator We start with the model for a simple calculator. To do this, we use the mouse to drag the item for a constant from the Signal library onto the drawing area. Make sure that you are in Sketch mode. Otherwise, you will not be able to add anything to the drawing area shown in Fig. 2.2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_2

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Fig. 2.1 Graphical user interface of Simcenter Amesim after startup—no model is added yet

We can activate this item for the constant with the mouse and then, as is common in Windows, copy it with the key combination Ctrl + C, and paste it again with the key combination Ctrl + P. With the key combination Ctrl + R, we can rotate the items. Further shortcuts will be given by a menu if you press the right mouse button. The equation 1 + 2 = 3 is to be solved with the calculator; therefore, one must drag a sum item with the mouse onto the drawing area from the library shown in Fig. 2.3. After the two constants and the sum item have been copied or dragged on the workbench, they must be connected with the mouse as shown in Fig. 2.4. The items must be connected to each other as shown in Fig. 2.5. To do this, you have to move the mouse pointer to the so-called ports of the items until they are displayed in green. Now, you can connect the ports of the items with each other. When all ports of a model are properly connected, the color of the items changes. The model is then no longer highlighted in dark color and can be used. If the connecting does not work, there is usually a thinking error in the design. This can be checked if you compare the units of the two sides of the connections.

2.2 How to Build a Simple Calculator

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Fig. 2.2 Example: Creating a simple calculator; Step 1: Creating a constant

Fig. 2.3 Example: Creating a simple calculator; Step 2: Copying constants and adding a sum function

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Fig. 2.4 Example: Creating a simple calculator; Step 3: Connection of the two constants and the sum function Fig. 2.5 Connecting an item for constant value with the item for summation

2.2 How to Build a Simple Calculator

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Fig. 2.6 Example: Creating a simple calculator; Step 4: Closing the model with a sink item

Important: Always make sure that the elements are connected correctly and highlighting has been changed. It is also possible that the elements cannot be connected if a physical model is illogical, e.g. if the units do not fit. In this case, you should question if your model is logical. In general, you can connect the elements from the signal library with all other libraries. A sink from the signal library, as shown in Fig. 2.6, must therefore always be compatible with the other libraries and can then be used accordingly if you cannot find a suitable connection or continuation of the model for a particular simulation model in a physical library. Once the model is complete for addition, no item will be darkened. All items should then be displayed in normal color. Now, you can switch from Sketch mode to Submodel mode as shown in Fig. 2.7. Switching to the Submodel mode is possible only if the model has been drawn correctly before. In order to enter individual values in the graphical model, it is then necessary to switch further to the parameter mode as shown in Fig. 2.8. In the mode parameter it is now possible to enter the values to solve our equation 1 + 2 = 3. You can see exactly where to enter the values in Figs. 2.9 and 2.10. Additionally, in Figs. 2.11 and 2.12, you can see how to switch to simulation mode and then read the result of the calculation.

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Fig. 2.7 Example: Creating a simple calculator; Step 5: Change from Sketch mode to Submodel mode

Fig. 2.8 Example: Creating a simple calculator; Step 6: Change from Submodel mode to Parameter mode

2.2 How to Build a Simple Calculator

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Fig. 2.9 Example: Creating a simple calculator; Step 7: Entering a value for the first constant

Fig. 2.10 Example: Creating a simple calculator; Step 8: Entering a value for the second constant

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Fig. 2.11 Example: Creating a simple calculator; Step 9: Switching to Simulation mode and starting the simulation

Fig. 2.12 Example: Creating a simple calculator; Step 10: The simulation is finished and the result can be read off

2.2 How to Build a Simple Calculator

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Problems For better understanding, you can change the signal model now and try out any other calculation [2]. 2.1 Sum up three or four different constants to one result. 2.2 Use another operator like, e.g. multiplication or division. 2.3 Copy your model with Ctrl+C, paste it somewhere on the same workspace, and use two different models in one workspace. 2.4 Plot a diagram of your results. Try to plot two different results from different models in one diagram.

References 1. Günther, M., Velten, K.: Mathematische Modellbildung und Simulation, Wiley-VCH (2014) 2. Bossel, H.: Modellbildung und Simulation. Springer Vieweg, Wiesbaden (1992). https://link. springer.com/book/10.1007/978-3-322-83658-8

Chapter 3

The Mechanical Twin

3.1 What Is Mechanics? The word mechanics is derived from the ancient Greek word mechané. In physics, mechanics is usually understood as the classical mechanics of moving bodies and particles. It deals with the motion of bodies and the forces acting on them. It is needed for the planning of machines or buildings. In engineering, this includes static and dynamic mechanics. Let us solve a easy physical problem first. We want to examine a mechanical system and have chosen the example of a bouncing ball. The necessary mechanical library can be found on the right side of the interface in the library tree. The colour of the mechanical library is green.

3.2 The Model of a Bouncing Ball The idea of what exactly happens when a ball falls to the ground and bounces up again is relatively easy to understand and will not be explained again here. We want to start right away within the symbolism and workflow of Amesim. Therefore we should create the bouncing ball model directly in the Sketch mode. To do this, we first open a so-called simulation model for the mechanical process and create our twin as shown in Fig. 3.1. The sketch already contains all the essential functionalities. When creating the simulation model, however, please do not yet worry too much about how long, large or heavy a body is and exactly what forces are acting. First, draw the model as you imagine it. The details will then be clarified afterwards. After that all other quantities, such as the weight of the ball or the acting forces, are entered in section Submodels and Parameters.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_3

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Fig. 3.1 Mechanical simulation model for a falling ball that is bouncing on the ground

Important: Focus on a correct sketch of the model in the beginning and try if it can be compiled and the simulation runs. For this, estimate the physical parameters and boundary conditions first. After that step you can refer the correct quantities and add them. This will help you to keep track.

3.2.1 Simulation Model So for, it doesn’t really matter how heavy or how big the ball is or from which height it hits the ground. Let’s start with the sketch of the functional model. We represent our sphere with a so-called mass symbol [MAS001]. We drag it with the mouse onto the empty workspace in Sketch mode. Now it gets a bit more complicated. We will need a symbol for the height from which the ball falls to the ground, or the better say the gap between the ball and the ground. The impact of the elastic ball should be damped when it hits the ground. To represent these effect with our twin, we choose an elastic contact symbol from the mechanical library. In addition to the mass item, that represents the ball, we drag a symbol for the elastic contact [LSTP00A] and the ground [V001] onto the drawing area. You can see how the finished simulation model should look like in Fig. 3.1. Please also make sure that all symbols are properly connected and no longer have a dark background. Only then does the model work. When all the symbols are connected, your first bouncing ball twin is ready. Now comes the final touch and we need to look a little closer. Switch to the parameter mode and enter the parameters for our twin.

3.2 The Model of a Bouncing Ball

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Fig. 3.2 Press button to set run parameters—specify simulation times and time step size

3.2.2 Submodels and Parameters Once the graphical creation of the simulation model is complete, the physical quantities for the different Submodels can be selected on the Submodel tab. At first, not much changes in the presentation. The current parameters can be specified under the Parameters tab. Important is, that the parameters are constant quantities, which do not change during calculation, but have a significant influence on the result. The user must know these parameters himself or observe them in reality and specify them to the model. One determines them for example by measuring or weighing the bodies. For the mass representing our ball, the weight (here: 1 [kg]) and the angle with which the ball is dropped down must be entered. In our example the ball should fall vertically downwards with an degree of 90◦ . For the gap between the ball and the ground, the height of 1 [m] as well as the contact stiffness of the ball at impact (here: it is best to enter a very large value) as well as the damping at impact (here: 150 [N/(m/s)]) must be entered. The ball will not be able to penetrate the ground with this damping model, but will bounce off the ground. In the sections Submodels and parameters we have always summarized all parameters in a table (see: Table 3.1). This is the input to Amesim. In Table 3.1 the name of the model or submodel is also given under the Item column. Under this name, you can also search for the corresponding item using the search function in the parameter window. If all parameters are correct, the simulation can be started as shown in Fig. 3.2. After clicking with the mouse on the button to set the Run parameters, you first have to enter how long the simulation should run. In our case you start at 0 [s] and calculate up to the simulation time 4 [s]. Additionally, a reasonable step size is selected (here: 0.01 [s]). This specifies when results are to be written for each time step. The simulation times are given in Table 3.2.

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Table 3.1 Parameters for the bouncing ball simulation model Item Parameter MAS001 LSTP00A

V001

Mass = 1 [kg] Inclination = 90 [degree] Gap or clearance with both displacements zero = 1000 [mm] Contact stiffness = 1e+06 [N/m] Contact damping = 150 [N/(m/s)] Penetration for full damping = 0.001 [mm] Linear displacement = 0 [m]

Table 3.2 Simulation time for the bouncing ball simulation model

Simulation settings Start time = 0 [s] Final time = 4 [s] Print interval = 0.01 [s]

Fig. 3.3 Successfully completed the simulation for the digital twin of a falling ball

When the progress bar has risen to 100% as shown in Fig. 3.3, the simulation was completed successfully. Now the results can be examined. If a simulation does not run successfully, or if it takes a very long time to generate a result you have to change the simulation parameters. There is the possibility to choose the simulation time (Final time) a bit lower and you could also sets the Print interval to a smaller time step size. This allows you to check which possible error could be present in the simulation model. Mostly the error is caused by physical values that do not make sense, e.g. the mass of the ball is much too high or the distance is too large. If you select the time steps too large, it is possible that the simulation cannot be carried out successfully. It is then said that the mathematical model does not converge. But also if you choose the time steps too small it is possible that the simulation cannot be executed successfully.

3.2 The Model of a Bouncing Ball

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Fig. 3.4 Generating diagrams: visualisation of the result by dragging the variable on the work space

This is because the Amesim program writes results to the hard disk for each time step. If too much data is written, problems with disk space may occur. Next, let us take a look at the most important simulation results that result from the simulation for our ball model. We want to analyse them. Drag the desired result variables over the workspace to create a diagram of the value as shown in Fig. 3.4. After that, we will give some suggestions for further developments or further investigations on our digital twin. One thing should be kept in mind when creating the model. If something would not work in reality, e.g. if the mass of the ball or the distance to the ground would be much too high, then this can also lead to an abort and an error message. That’s why simulations should only be done with halfway plausible parameters.

3.2.3 Results and Analysis The program stores a lot of different physical quantities in the background during the simulation run. This could lead to a lot of data if the model is big and time steps are low. However, in this section we will only look at the really interesting quantities and discuss them. We will show you how what this means. To be able to draw and map simulation results in a diagram, one must click on the result variable after the successful simulation, then drag the name of the variable as shown in Fig. 3.4 from the variables window to the workspace with the mouse held

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Fig. 3.5 Size of gap is a measure of the distance between the ball and the ground

down. This will generate a diagram of this result variable over the time course of the simulation. Important: Drag a result variable with the mouse from the menu on the right side to the workbench to plot a diagram in an x,y-chart. What results are of particular importance to us in the bouncing ball? At the moment, there are essentially two quantities in this model. We are interested in how the height of the ball changes and how it bounces back from the ground. We can best visualize this by looking at the gap or clearance between the ball and the ground (see: Fig. 3.5). How should we now read and interpret the diagram in Fig. 3.5? Suppose we drop the ball from a height of 1.0 [m] to the ground. After about one second it hits the ground for the first time at a height of 0.0 [m]. It then jumps up again but does not reach the full height. After several jumps, the distance between the ground and the ball becomes smaller and smaller. Your first simulation for the bouncing ball was successful. After this first trial we look at more advanced problems and work suggestions for this example. Important: If you want to display two result variables in a single diagram, you should simply drag the second variable onto the diagram window in the same way as the first one. This way the second value will also be displayed in the same diagram. To get to know the digital twin of your bouncing ball problem a bit better, let us work on a few more advanced work suggestions. In addition, let’s take a look at the speed of the bouncing ball. For this we drag the variable Velocity at port 1 from

3.3 The Mechanical Rocker

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the variables field onto the workbench to display this variable in a diagram as well. When the ball is released the velocity is still small, towards the end it gets bigger and bigger until the ball hits the ground and is bounced back.

Problems 3.1 Investigate after what time the ball first touches the ground if you set the distance between the ball and the ground at the beginning from the height 1.0, 2.0 and 4 [m]. 3.2 How does the trajectory of the ball change when you increase the weight of the ball from 1.0 to 1.5 [kg] and to 2.0 [kg]? 3.3 How does the trajectory of the ball change if you decrease the contact damping force from 150 to 50 [N/(m/s)]?

3.3 The Mechanical Rocker Now let’s consider another use case that we can recreate with the mechanical library. We want to create a twin for a mechanical rocker. On the right side, we want the bar of the rocker to be pushed down. On the left side of the seesaw, we want a spring that is attached between the left lever arm and the bottom. Let’s start right away and build the model of the rocker as shown in Fig. 3.6.

3.3.1 Simulation Model Our simulation model for the digital twin of the mechanical rocker initially consists of a lever arm on the left side to which the spring is attached. The model lever2 [LML012] is used as the rocker. The length of the left arm is 1 [m]. On the right side, a force pushes the rocker down by a distance of 1 [m]. The right lever arm of the seesaw should also be 1 [m] long. For the time being, it does not matter how great the friction is at the joint of the rocker. Moreover, this digital twin always assumes that we are on Earth and that the gravity of our planet is acting.

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Fig. 3.6 Simulation model for a mechanical rocker with lever arms of equal length

Once you have created the simulation model for the mechanical rocker with its associated spring, you can enter the specific values, such as spring force, length of the lever arms, or the distance by which the right lever arm is pushed down. We will make these specifications in the next section Submodel and parameters.

3.3.2 Submodels and Parameters We want to try to push down the rocker arm on the right side in the model. How can we teach this operation to the twin? To do this, we selected a ramp function from the signal library (red library) in Fig. 3.7 [RAMP0]. For this item, we specify the value for the slope of the ramp function as 1. This means that within one second the right arm is pushed down by one meter. On the left side of the seesaw we attach the spring. In the next step, we have selected an element that shows us how far down to push the right arm of the rocker. In the element for the seesaw we specify how long the two arms of should be. For both sides of the seesaw we should choose lever arms that are 1 [m] long. The values from Table 3.3 must always be entered in the parameter mode. The simulation time is set to 1 [s] according to the given Table 3.4. In this calculation we have to take care that the simulation time is not too long. This is for a very simple reason. If we choose the simulation time too long, then the arms of the rocker on the right side will be pushed down too far, which would lead to nonsensical behavior.

3.3 The Mechanical Rocker

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Fig. 3.7 Ramp function that allows us to push the seesaw down on the right side

Table 3.3 Parameters for the mechanical rocker simulation model Item Parameter RAMP0 XVLC01 LML012

Slope = 1 [] Time constant for derivative of displacement = 0.0001 [s] Distance port 1 to pivot = 1 [m] Distance port 2 to pivot = 1 [m] SPR000A Spring stiffness rate = 1000 [N/m] Spring force with both displacements zero = 0 [N]

Table 3.4 Simulation time for the simulation model of the mechanical rocker

Simulation settings Start time = 0 [s] Final time = 1 [s] Print interval = 0.01 [s]

3.3.3 Results and Analysis We want to evaluate two different events. Of course, with the rocker, we already know what needs to happen. It is always good to check the results for plausibility and to critically question if everything is correct. First, in Fig. 3.8 we have a look how the movement of the lever arms changes. The left arm of the beam moves exactly

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Fig. 3.8 Height by which left lever is lifted up and the right lever arm is pushed down

one meter upwards in a time span of 1 [s], the right lever of the seesaw moves one meter downwards. This is a reasonable behavior. Important: Always check your models for plausibility and logic before you make the models too complicated. Check the dimensions and the units of your results. The fact that the right bar is pushed down is shown by the negative value. If the left bar had a greater length, it would move further up than the right bar would move down. So we see that both lever arms are moved by 1 [m] in the time 1 [s]. We can also look at something else in Fig. 3.9. That is, by what angle the beam of the rocker is rotated around its axis. This can be evaluated by the size angular lever position. Here, the seesaw rotates by about 57◦ . The example is relatively simple now, but we can do the following suggested work to make it a little more interesting and explore our mechanical problem a little further. In the Problems part you find suggestions for further work.

Problems Why don’t they do the following investigations with your rocker model? 3.4 How do the results change when we press the seesaw for more than one second? At what point do the results become nonsensical and why?

3.4 How a Row on a Pulley Works

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Fig. 3.9 Angle in degrees by which the beam of the rocker rotates around its axis

3.5 Check how the height to which the weight is pulled changes when the mass hanging at the bottom of the rope changes. Does the length of the rope also change during the process? 3.6 What happens if we extend the right arm of the bar, e.g. to 3 [m]? Interpret your new results. 3.7 What changes if we increase the spring force on the left arm of the seesaw? Interpret these results as well.

3.4 How a Row on a Pulley Works We will build a mechanical twin for a pulley. With the rope we will lift a weight. The rope is to run over a pulley. We will also investigate whether the pulley runs without friction or not. When modeling, you will notice that the length of the lower end of the rope also has an influence on the result. This is because this part of the rope can still stretch when it is pulled. Please always remember this when you want to practice bungee jumping.

3.4.1 Simulation Model Our simulation model in Fig. 3.10 for the mechanical twin of the rope on a pulley consists initially on the left side of a force F with the unit Newton [N]. This can be

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Fig. 3.10 Simulation model for a rope to lift a weight running over a pulley

used to pull on the rope. Then the rope runs over the pulley and at the hanging end of the rope there is the weight. A pulley never runs completely without friction, so you have to add a model for the friction (rotary load) to the pulley. Again, it doesn’t matter how big the friction is, the force on the left side or the weight hanging on the bottom of the rope is. You should first understand the basic structure of the simulation model for the problem. Then you assemble it and test it for function. Only after that they should enter the exact parameters. In the following step, one can then enter the concrete values, e.g. the weight or length of the rope. In addition, the gravity of the earth is still specified with the apple symbol. We discuss later, if this makes sense. If you want, you can remove the apple and check what happens. In the following section Submodel and parameters we enter the parameters of the model.

3.4.2 Submodels and Parameters Let’s take a closer look at the parameters again. We are pulling on the left side of the rope with a force of 50 [N]. Since we are pulling on the rope to the left, we need to set the force to −50 [N], otherwise we would push the rope, which makes no sense. So, the value is given with a negative sign. In addition, we must now also specify the size of the pulley we want to use and we must also choose the length of the rope. The weight of the body we want to lift is also crucial, and we must specify that it should weigh 1 [kg]. All other values are given in Table 3.5. Important: Gravity has a great influence on many mechanical models, but also on other physical phenomena. Thus, the applications may be different in space or on other planets.

3.4 How a Row on a Pulley Works

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Table 3.5 Parameters for the simple rope hoist simulation model Item Parameter CONS00 RSHE002A MECROPE0

RL00A

MAS001 GRAV0

Constant value = −50 [N] Diameter of the sheave = 500 [mm] Roping angle = 90 [degree] Stiffness of unit length of rope = 1e+06 [N/m] Viscous friction of unit length of rope = 1000 [N/m/s] Initial length = 10 [m] Moment of inertia = 1 [kgm**2] Coefficient of viscous friction = 1 [Nm/(rev/min)] (All other values are set to 0) Mass = 1 [kg] Constant gravity value = 9,80665 [m/s/s]

Table 3.6 Simulation time for the simulation model simple wire rope hoist and pulley

Simulation settings Start time = 0 [s] Final time = 4 [s] Print interval = 0.01 [s]

The symbol with the apple [GROV0] indicates the magnitude of gravity. On the earth the gravitational acceleration of 9,80665 [m/s2 ] acts almost everywhere. If we were on another planet like Mars or Venus, e.g. we would have to use a different value for gravity. If we were on our neighboring planet Mars, we would not be able to use this digital twin because the gravity there is 3.711 [m/s2 ]. On the moon, the gravity is 1.62 [m/s2 ]. We can’t change the gravity acceleration arbitrarily in the individual items, and the apple icon doesn’t affect all models the same. Unfortunately, however, you can always paste the icon on the desktop, and that’s what we did, because the apple icon looks very nice. But inside the submodels, unfortunately, the gravitational constant of the earth is still used in some places. This should be adapted once in following versions of the program Amesim. Then digital twins can also be created for other planets. Here we want to do the calculation for 4.0 [s]. Please make sure that the simulation time in Table 3.6 is not too long, otherwise there is a risk that you will pull the whole rope through the pulley. Our model is not designed for this and no useful results would come out.

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Fig. 3.11 Height by which the weight is lifted at the bottom of the rope

3.4.3 Results and Analysis What result can we evaluate now? It definitely makes sense to first look at how many meters the weight at the bottom of the rope is lifted when pulling with a force of 50 [N]. Again, please pay attention to the sign. It is negative because the rope is pulled away from the pulley. Take care, when specifying boundary conditions such as forces, velocities or mass flows, it is always important in which direction the force or the flows act. According to the direction you have to select the sign. If you do not know which sign to use. Just test if it has to be a plus or a minus. If we look at the displacement port variable, we see in Fig. 3.11 that the weight can be pulled up by about one meter in 4 [s]. This fact is still relatively easy to understand and we could have derived it with simple physical considerations. Just take a closer look at Fig. 3.12. This is because our digital twin can predict even more. Since the rope is pulled up very quickly over the pulley, there is a brief lengthening of the rope at the beginning of the pulling process. It is first tensioned, so to speak, and thus stretched. You can see this in the following figure from the brief change in length. As soon as the mass is pulled up evenly, the change in length of the rope decreases again. This process is much more complex and cannot be determined so easily with paper and pencil without a simulation model of the technical system. However, the additional lengthening of the rope can be quite important in engineering or in daily life. Just think of bungee jumping.

3.5 Damper of a Driving Car

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Fig. 3.12 Change in length of the rope due to acceleration at the start

Problems Now let’s look at what else we can use our digital twin of the rope running over a pulley for. They can investigate the following work suggestions on their own using their simulation model: 3.8 What would happen if they double the force with which they pull on the rope. How fast will the mass be lifted then? 3.9 Check how the height to which the weight is pulled changes when the mass hanging at the bottom of the rope changes. Does the length of the rope also change during the process? 3.10 Try to create a simulation model where you run the cable over two different pulleys. What does this do for you? 3.11 Just remove the apple symbol and see if the model still runs.

3.5 Damper of a Driving Car A damper or shock absorber holds the wheel of a car and is a mechanical, hydraulic or pneumatic device designed to absorb and damp shock impulses from the road. It does this by converting the kinetic energy of the shock into another form of energy which is then dissipated [1]. We want to show you in this example how engineers work on a problem and try to solve it step by step.

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Fig. 3.13 Model for one wheel of a car with a mechanical damper and road profile

3.5.1 Simulation Model Pneumatic and hydraulic damper or shock absorbers are used in conjunction with cushions and springs. An modern automobile shock absorber contains spring-loaded check valves and orifices to control the flow of oil through an internal piston [1]. In this example we concentrate on the simpler mechanical damper, because pneumatic and hydraulic library will be introduced later. The simulation model in Fig. 3.13 for the damper consists only of one wheel for the the whole car, this is done to make the model simpler. After sketching the model of the damper, one can define the parameters.

3.5.2 Submodels and Parameters We have mentioned that the model of the car is simplified, we modeled only one wheel of the car and the total mass. We have modeled the car with only one mass element and estimate the weight of the vehicle to be 1500 [kg]. The second mass model is for the wheel. In Table 3.7 we give the full list of parameters for the model.

3.5 Damper of a Driving Car

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Table 3.7 Parameters for the shock absorber of a driving car Item Parameter MAS002

SPR000A DAM0000 MAS002

SD0000A XVLC01 UD00

(of the car) Mass = 1500 [kg] Inclination = 0 [degree] Spring rate = 100000 [N/mm] Spring force with both displacements zero = 0 [N] Damping rate = 1000 [N/(m/s)] (of the wheel) Mass = 35 [kg] Inclination = 0 [degree] Spring rate = 100000 [N/m] Damper rating = 1000 [N/(m/s)] Time constant for derivative of Displacement = 0.0001 [s] Constant gravity value = 9,80665 [m/s/s] Number of stages = 2 Cyclic = no Time at which duty cycle starts = 0 [s] Output at start of stage 1 = 0 null Output at end of stage 1 = 0 null Duration of stage 1 = 2 [s] Output at start of stage 2 = 0.08 null Output at end of stage 2 = 0.08 null Duration of stage 2 = 8 [s]

Table 3.8 Simulation time for shock absorber of a driving car

Simulation settings Start time = 0 [s] Final time = 10 [s] Print interval = 0.01 [s]

We took a simulation time of 10.0 [s], which is of course rather short, so we only want to model the point of time where the wheel hits the edge of the sidewalk. The simulation time and time step is defined in Table 3.8.

3.5.3 Results and Analysis Before we have a deeper look at the results of the car movement and damper behavior, we want to check if the simulation is running and the model works. In Fig. 3.14

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Fig. 3.14 Height of the road profile, e.g. step of a sidewalk

the signal of the submodel [UD00] is shown and gives us an impression, that this function was defined in the right way. Of course, you can make the road profile more sophisticated. Now let us take a closer look at the more interesting results. The damping of the wheel and bouncing of the damper after step is shown in Fig. 3.15. You can see, that the pneumatic tire which is modeled by a spring [SD0000A] reacts with an oscillation of the weight on the step of the sidewalk. The movement and oscillation of the whole cabin of the car is shown in Fig. 3.16 and gives us an impression how the second damping acts on the car. It can be seen that here the amplitude of the oscillation is even higher. This is probably not very convenient and should be avoided in an normal car. At the end of this section you can work on some problems that we have defined for you. This is a normal and good approach and shows you how engineers work. First we created a simple model that works and after this introduction to the problem we will try to improve our car model more and more until we end up with a good car behavior.

3.5 Damper of a Driving Car

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Fig. 3.15 Damping of the wheel and bouncing of the damper after step

Fig. 3.16 Oscillation of the car cabin in the moving car after step of the sidewalk

Problems We have already discussed some problems. Now you can investigate the following problems for your damper with your simulation model. 3.12 Try to make the damping of the spring system softer to reduce the oscillation in the cars cabin.

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3.13 Change the size of the step and examine, how the damping changes. 3.14 We have modeled only one wheel as an example, try to build a digital twin of a car with four wheels. Consider how to model a correct road profile for four wheels. 3.15 Work through the chapters on hydraulics and pneumatics and also try to build a hydraulic or pneumatic shock absorber. 3.16 Try to generate a more complicated road profile and chose longer simulation times.

3.6 How to Use 3D Geometries To get a more vivid idea of the mechanical twin and to be able to have a discourse during the innovation process, the results can also be visualized three dimensional in Simcenter Amesim. An example, how to visualise such an model in 3D is shown in Fig. 3.17. Here we use a model of a spring to visualise the working behavior. The visualization can be done with Animation dialog, which can be opened by clicking on the Animation button in the menu bar of the main graphical user interface (GUI). Based on every digital twin, 3D geometries can be generated for better understanding of dynamic simulation results and test their behavior and interaction. We refer to this also as computer aided engineering (CAE) or CAx, which includes also computer aided design (CAD). The designer is given the opportunity to perform real-time tests on the digital twin, thus applying theory directly to the design and questions how well it works. Theoretical knowledge is deepened through such virtual dry runs and various application scenarios can be run through independently. In the following chapter examine the digital twin of a chassis with a hydraulic damping system when driving over an obstacle. A complete vehicle in a landscape can also be visualized, as in Fig. 3.18. The 3D geometry of the car can be created by the designer, e.g. this could also be done with the tool Blender [2] or any other alternative CAD tool. How the vehicle behaves, how the damper system works or which forces act on the chassis depends on the spring model of the damper an is then again calculated by the Amesim main program making it possible to test the virtual geometry of the chassis under real conditions and to investigate in advance how it works. With this model, e.g. driving cycles of the whole car can be simulated, as shown in Fig. 3.18. The behavior of different bumpers can be examined. The results are much more understandable, if the whole car maneuver can be visualised. So, simple 3D geometries can be added within the Animations dialog. If an 1D model is already running, e.g. the spring of the cars damper system, all the other object of the scene can be added with the Animations dialog. Standard objects like boxes, spheres, splines or springs can be added by pressing the Add menu in the menu bar. In addition, 3D models created with Blender can also be imported. To connect the 1D model from the main GUI, the result values of the simulation run can

3.6 How to Use 3D Geometries

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Fig. 3.17 Animation screen for 3D visualization and animation of the model with the animation dialog

Fig. 3.18 Results of a driving cycle with 3D geometry of a car and realistic driving behavior

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Fig. 3.19 Connecting a 1D spring model with a 3D spring model in the animations dialog

be dragged to geometric values of the 3D model. This can be seen in Fig. 3.19 and is done simply by dragging the result value on the animation window. After this step the simulations result data can be used to move and animate the 3D model by pressing the play button in the menu bar of the animation dialog. As shown in Fig. 3.20 this will lead to an animation scene of the deformed damper spring. Furthermore, in conjunction with artificial intelligence (AI) as shown in a later chapter of this book, the program can learn to find its way over the obstacle as efficiently as possible. In the remainder of this book we will refrain from using 3D geometry as this is beyond the scope of this book, but we have further books on this topic planned. Today there is a really good new smartphone app called poly-cam [3]. It is a 3D capture application for high-quality 3D models from photos with mobile device, and rapidly generate scans of spaces with the LiDAR sensor. LiDAR is an acronym of light detection and ranging or laser imaging, detection, and ranging [4]. This process is sometimes referred to as 3D laser scanning, a special combination of 3D scanning and laser scanning. Graphical models can be scanned from real objects very easily and you can use them to create animated presentations of results. After scanning the 3D models can be edited with the free of charge tool Blender [2] and it is possible to export them in different file formats, e.g. GLTF or STL. GLTF (GL Transmission Format) is an open-source and royalty-free 3D file that supports

References

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Fig. 3.20 3D animation of the spring at different simulation time steps

static models, animation, and moving scenes. GLTF is used in games, native web applications, and 3D ads. STL is a file format native to the stereo-lithography CAD software created by 3D Systems. STL has several acronyms such as Standard Triangle Language or Standard Tessellation Language. This file format is supported by many other software packages. It is commonly used for rapid prototyping, 3D printing, and computer-aided manufacturing. STL files describe only the surface geometry of a three-dimensional object without any representation of color, texture, or other common CAD model attributes [5].

References 1. Bauer, H. (ed).: Automotive Handbook, 4th edn, Robert Bosch GmbH, ISBN 0-8376-0333-1, p 584 (1996) 2. https://www.blender.org 3. https://poly.cam/ 4. Travis, S.: Introduction to Laser Science and Engineering. CRC Press, Taylor (2019) 5. https://en.wikipedia.org/wiki/STL_(file_format)

Chapter 4

The Thermal Twin

4.1 Examination of Heat Transfer After the mechanical library in the previous chapter, we now want to turn to a completely different problem. For example, we don’t know how much a seesaw heats up when exposed to sunlight, or what amount of heat is generated by the friction between the rope and the pulley. Therefore, we have chosen the so-called thermal library to create a thermal simulation model. The color of the thermal library is dark brown. There are three different heat transfer processes. First, we speak of heat conduction inside a body or between two neighboring bodies. This process is also called conduction. The second mechanism is called convection. In this process, heat is transported by a gas or liquid. The third main heat transfer process is radiation. There are further heat transfer processes, but we will not go too deep into this topic. Important: There are different transport mechanisms for heat. In most cases, heat transport occurs from a higher temperature to a lower temperature. Not in the opposite direction. Let us consider practical examples of heat conduction in an aluminium heat sink and a solar collector. The temperature of a body results due to heat transfer with its environment or due to transformation processes and chemical reactions inside it. In heat transport, one essentially considers heat conduction, convection as well as heat transport by radiation. It is important to what mass a body has. This allows us to calculate how much heat can be stored and what temperature it assumes. All individual processes can be described by different digital twins. At the end, further working suggestions are given. Also crucial to understanding the philosophy of Simcenter Amesim is that the thermal library can be connected to the mechanical library or any other library in one

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_4

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model. For example, one can calculate how much heat is generated by friction from a brake disc during the braking process of a vehicle to a couple of different complex twins. For example, you can link certain items of the mechanical library with the thermal library. However, it is only possible to connect items if this makes physical sense. If two items cannot be connected, then this almost always has a reason. Now let’s look at our thermal model for the heat sink of a generator. The design and creation of heat sinks are very important for many technical applications. For example, almost all electronic components such as electric motors, generators, batteries or accumulators need to be cooled during operation.

4.2 Heat Transfer in Electric Generators When dealing with the temperature of bodies, we should first realize that the temperature of a body (in [◦ C]) is usually the effect of a previous heat or energy transfer in joules [J] per unit time. In the case of an electrical generator, eddy currents cause a heat input. An attempt is usually made to use heat sinks to keep the temperature of the material low and to dissipate heat to the environment. The material from which the generator and heat sink are made plays a role here. A heat transfer process is given the unit watt [W].

4.2.1 Simulation Model For the thermal simulation model, we need to specify for the metal bodies what material they are made of. Here we want to model the heat conduction between the two metals iron and aluminium. The necessary material parameters are already stored in Simcenter Amesim. In addition, we want to consider the so-called convection, i.e. the cooling of the metals by surrounding air, as well as the heat radiation. Important: If we cool a heat sink with a fluid like air or water it is always important how fast the fluid moves past the body and if turbulence occurs. This is also characterized by the so-called Nusselt Number [1]. In our twin, we have to use a special element [THCD00] for the heat conduction between the metal bodies. We composed our digital twin from a total of four different metal bodies [THC00]. To tell the model which material each item in Fig. 4.1 is made of, we have to use a so-called solid type index. A heat source of 50.0 [W] in [THHS0] is attached to the right side of the iron block and to the aluminium block, respectively. At the top is the aluminium block where heat is dissipated by convection and radiation. Here, too, it is possible to change the air speed in order to influence the heat transport.

4.2 Heat Transfer in Electric Generators

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Fig. 4.1 Simulation model for heat conduction, the heat is transferred between a body made of iron and a body made of aluminium

4.2.2 Submodels and Parameters For the model parameters in Table 4.1, note that in each case the two aluminium bodies have an equal mass, and the same weight was also used for the two iron bodies. Here, the solid type index = 1 is set for aluminium and the solid type index = 2 for iron. Other materials would also be possible. Be careful to use the right solid type index for the components. It has to be noted, that for the estimation of the contact surface for [THCD00] the volumes of the [THC000] blocks have to be taken into account. The simulation time in Table 4.2 is chosen to be very long for this example, because such heat conduction processes can take quite a long time power source is low and weight is relatively high. Therefore, we adjust the time step size accordingly to higher time step sizes.

4.2.3 Results and Analysis If we want to look at the results and estimate how heat from the source is conducted first through the iron and then through the aluminium elements, we look at the time period of 1 h (= 3600 [s]) in Fig. 4.2. We have applied 50.0 [W] of heating power on the right side which is due to eddy currents, e.g. from an electric generator. The mass elements are numbered consecutively and after 1 hour the temperature has risen up

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Table 4.1 Parameters for the simulation model of an aluminium heat sink Item Parameter THHS0 THSD00 THSD00 THC00 THCD00 THGCV0

Heat flow rate at port 1 = 50 [W] Solid type index = 1 [-] Material definition = pure aluminium (Al) [-] Solid type index = 2 [-] Material definition = pure iron (Fe) [-] Solid type index = 1 [-] Mass of material = 5 [kg] Contact surface = 1000 [mm**2] Thermal contact conductance = 1000 [W/mm**2/degC] Inclination angle = 90 [degree] Width = 100 [mm] Length = 150 [mm] Velocity of the fluid = 4 [m/s]

Table 4.2 Simulation time for the model of an aluminium heatsink

Simulation settings Start time = 0 [s] Final time = 3600 [s] Print interval = 0.1 [s]

to approx. 80.0 [◦ C] for the elements that are directly applied to the 50.0 [W] heat source. The temperatures inside the metal blocks in Fig. 4.3 result from the corresponding heat fluxes between the heads and the surroundings. Towards the left side, the temperatures of both the iron and aluminium blocks only increased to about 50.0 [◦ C] each. The explanation for this is that on the left side, cooling of the two bodies takes place by convection. During convection, heat is transferred from the metal to the ambient air. The level of air velocity plays a decisive role in this process. Heat conduction between bodies is given in units of watts [W]. We see that about 30.0 [W] of heat flows between both aluminium and the iron. The convection that takes place on the left side is about 15 [W] after 1 hour. Note that this is only the case when cooling air is flowing past the heat sink at a velocity of 4 [m/s]. Fig. 4.4 is also interesting. It shows how the heat is conducted from the iron core to the aluminium heat sink. We assume here that the heat conduction is quite good. Let us have a look at the heat flows in a diagram. Near the 50.0 [W] source on the right side, more heat is conducted than on the left side, where cooling by the air takes place. This can be explained by the fact that we are already at a lower temperature level there and there are no longer such large amounts of heat stored inside the material. Now let us have a look at a few more work proposals or problems that we can investigate with this thermal twin.

4.2 Heat Transfer in Electric Generators

Fig. 4.2 Average temperatures inside the iron and the aluminium blocks

Fig. 4.3 Heat conduction within the bodies and convection at the boundaries

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Fig. 4.4 Heat conduction from the iron block to the aluminium heat sink

Problems Using your digital twin for the heat sink, investigate the following work suggestions once: 4.1 Change the materials and use copper instead of aluminium, for example. How does this change the temperatures of the bodies? 4.2 Investigate what happens if you increase the masses of the iron bodies from 5 to 10 and 15 [kg]. 4.3 Set the heat source on the right side from 50 to 100 and 150 [W]. Observe what happens to the temperatures of the metal bodies. 4.4 Try to cool the iron body further by increasing the air velocity on the right side to 20 [m/s]. 4.5 Have a look at the result output for convection. There you will find different comparison numbers, like the Nusselt number or the Reynolds number. What do these numbers stand for?

4.3 Site Selection for a Solar Collector We will now look at heat transfer by radiation. In a solar or solar collector, radiation from the sun strikes a collector body and heats it. Through subsequent conduction

4.3 Site Selection for a Solar Collector

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and convection processes, this energy can be further distributed. However, it is also crucial how much solar energy falls on the collector on a given day and where it is located.

4.3.1 Simulation Model To design the solar collector in Fig. 4.5, it is important to know where it is located. The location plays a crucial role in its function. A collector located near Johannesburg in South Africa has much more solar energy available than a collector located in Paris or Helsinki. Furthermore, the time of day at which the solar radiation falls on the collector is of decisive importance. In most places, the radiation is strongest around noon. In Simcenter Amesim, it is possible to set these environmental conditions using a custom model. This makes it possible to design the collector exactly for its particular position. We could also create the collector from single items. But this is not absolutely necessary. There is already a ready-made model that we can use. The solar collector should still be connected with elements made of aluminium, which should symbolize the design and structure of the collector support and its heat exchangers. Through the contact, it can give its energy to these elements and thus will heat them up.

Fig. 4.5 Simulation model for a solar collector and the heat conduction in the adjacent material

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4.3.2 Submodels and Parameters In the simulation with the parameters from Table 4.3, all components are to be made of aluminium. We also want to choose a very long period of 30.000 [s] simulation time for this simulation. During this time, the collector heats up slowly. We select Paris as the location for our twin number two. Decisive for the absorption of the amount of heat is the area of the collector and at what angle the collector is inclined to the horizon. We can specify many different cities all over the world to indicate the position. However, it is also possible to enter the GPS coordinates of a location. GPS stands for global positioning system. A system by which signals are sent from satellites used to show the position on the globe. This makes it possible to calculate yields that a solar collector field can produce. The simulation times are given in Table 4.4.

Table 4.3 Parameters for the simulation model of a solar collector in Paris Item Parameter THSD00 THAMBCOND0

THGCV0

THC000 THR03

THRSOL002

Material definition = pure aluminium (Al) City name = Paris Year = 2007 Month = December Day = 16 Hour = 8 Minute = 00 Inclination angle = 90 [degree] Width = 100 [mm] Length = 150 [mm] Velocity of the fluid = 5 [m/s] Solid type index = 1 [-] Mass of material = 50 [kg] and 20 [kg] Equivalent emission factor wall/gas = 1 [] Exchange area = 100 [m**2] Temperature of the gas = 20 [degC] Solar radiation setting mode = using ambient conditions Exchange area = 1 [m**2] Adsorption factor = 0.7 [] Equivalent emission factor gas/surface = 0.9 [] Surface inlination = 45 []

4.3 Site Selection for a Solar Collector Table 4.4 Simulation time for the simulation model of a solar collector

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Simulation settings Start time = 0 [s] Final time = 30000 [s] Print interval = 1 [s]

Fig. 4.6 Angle with which the sun is above the solar collector

4.3.3 Results and Analysis We have already described that when simulating the solar collector, it is of crucial importance where exactly it is located and on which day the investigation is carried out. We can use the twin to determine the exact solar path as shown in Fig. 4.6 for any given day over the collector. In our example, the morning of Dec. 16, 2007, was considered. These are, together with 06/24/2011, the best days of the century or millennium. On the morning of this day, the solar radiation heated up the collector and, as shown in Fig. 4.7, we can increase the temperature in the components with the amount of energy absorbed as a result. We can also see in Fig. 4.7 that air cooling of the left component will not have a high impact on the temperature profile at the low air velocities. The radiant power absorbed by the collector can now be accurately determined in Fig. 4.8. Unfortunately, this lacks information on the extent to which the energy has been suppressed by moisture in the atmosphere like fog, clouds, or rain. Dense clouds and rain in the atmosphere have of course a very large influence on the solar radiation.

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Fig. 4.7 Temperature profile of the aluminium components of the solar collector

Fig. 4.8 Radiant power impinging on the surface of the solar collector

Reference

57

Problems With the solar collector model is completed, you can now perform various tests and modifications: 4.6 Once, change the weight of the left aluminium bracket to half its value. What happens? How does the temperature change when they increase the wind speed from 5.0 to 8.0 [m/s]? 4.7 How does the energy input to the collector change, if we move the collector to Johannesburg or Helsinki? Select some alternative cities for simulation. 4.8 Consider a model setup where they can use the collector to heat a medium such as air or water.

Reference 1. Incropera, F.P., DeWitt, D.P.: Fundamentals of Heat and Mass Transfer, 4th edn. Wiley (1996)

Chapter 5

The Hydraulic Twin

5.1 What Is Hydraulics? The word hydraulics comes from the ancient Greek word hydro for water and aulos for pipe. We refer to a technology in which fluids are transported or used to transmit power or energy. However, it is not just about water or oil. Many other fluids in such a system can also be considered. When transporting liquids, the so-called pressure loss plays a special role [1]. Any diversion, bend, or fitting will cause a pressure loss within the pipeline. In the case of liquids, this usually has to be overcome by a pump. Otherwise no transport takes place and the liquid cannot flow. Systems for the use of heat or kinetic energy can also be considered as hydraulic systems, e.g. heating systems or hydroelectric power plants. We want to use the hydraulic library to create them. The hydraulic library (see: Fig. 5.1) of Amesim is located on the right side of the workbench in the Library Tree.

5.2 Two Fuel Oil Tanks and a Pump In the application in Fig. 5.1 we have two fuel oil tanks. The models for these tanks [TK10] can be found in the hydraulic library. We connect them by a line with a pump [PU001] in between. With this pump we can increase the pressure and transport the oil into the second tank. You can also connect the hydraulic library with other libraries of Simcenter Amesim. For example, you can connect the pump shaft to the mechanical library to account for friction. Or you can couple the pump casing with the heat library to calculate the heat dissipation at the casing [2].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_5

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Fig. 5.1 Simulation model for two fuel oil tanks with one pump

5.2.1 Simulation Model There is one major difficulty in creating networks of pipes. If one tank is on a higher position compared to the other, you have to define the slope within the line connecting the tanks and you will get a pressure difference between these two tanks. However, since this is too complex for the first example, we only want to connect the oil tanks with normal pipes without any slope. The resulting pressures in the pipelines will then only result from the filling level inside the tanks. We will show how to create inclined pipelines with a slope later. When all the items are connected, the digital twin for our hydraulic system is ready. Now we need to look a little closer and define the submodels and set the parameters according to Table 5.1 for our model. In this model, we have used the simplest model for a motor [PM001] without any friction. Of course, we can use much more complicated models from mechanical library here.

5.2.2 Submodels and Parameters Both tanks [TK10] should be the same size and contain the same amount of oil at the beginning of the simulation. We define the contained amount by specifying the fill level of the tank. It matters in the model whether we place the connections at the top or at the bottom of the tank. We also need to define what material properties [FP04] of the fuel oil because the fluid properties have a major impact on the flow behavior. Please make sure that each tank contains the same fluid by choosing the same index of hydraulic fluid for every component.

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Table 5.1 Parameters for the simulation model for two connected heating oil tanks Item Parameter TK10

PU001

PM000 FP04

Table 5.2 Simulation time for the simulation model for two connected heating oil tanks

Index of hydraulic fluid = 0 [] Initial height of liquid tank = 0.25 [m] Tank area = 0.5 [m**2] Minimum height alarm level = 0.1 [m] Maximum height alarm level = 1.0 [m] Index of hydraulic fluid = 0 [] Pump displacement = 100 [cc/rev] Typical speed of pump = 1000 [rev/min] Shaft speed = 1500 [rev/min] Index of hydraulic fluid = 0 [] Temperature = 40 [degC] Density = 850 [kg/m**3] Bulk modulus = 17000 [bar] Absolute viscosity = 51 [cP]

Simulation settings Start time = 0 [s] Final time = 10 [s] Print interval = 0.01 [s]

You start the simulation again at 0 [s] according to Table 5.2 and let it run until a calculation time of 10 [s]. When the progress bar has risen to 100 %, the simulation has been completed successfully. If a simulation does not run successfully or it takes a long time to generate a result, there is a possibility to make the simulation time smaller. This allows to better check which possible errors are present in the simulation model. Mostly it is due to non-physical parameters. Important: Always pay attention to what liquid you expect, e.g. whether it is oil, water, or another liquid. Change the liquid properties, density and viscosity, with the droplet symbol [FP04]. The index for the fluid to be used must also be set there. This index can be redefined for each hydraulic component. Several different fluids are possible within one larger system. If you look at the fluid properties of the liquid in this experiment, you will notice that it is not water but an oil, e.g. hydraulic oil, heating oil or diesel fuel, due to its low density. Here the index of the hydraulic fluid = 0 was selected.

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Fig. 5.2 Filling level of the different oil tanks as a result of the analysis

5.2.3 Results and Analysis During the simulation run all important quantities are saved and you can evaluate the interesting and decisive quantities when it is finished [3]. To check if the results make sense you can first have a look at a quantity that is easy to understand. The level inside the two tanks shown in Fig. 5.2 is the first thing we should look at and analyze. The level of the tanks is the simplest quantity and we can estimate whether changing this value is realistic or not. We want to show the filling level from the upper tank and the filling level from the lower tank in the same diagram and can now compare the results very well. Since both tanks are exactly the same size, the level in the upper tank has increased by exactly the amount of oil that is missing in the lower tank. The next thing is, we want to evaluate the effect of the pressure increase behind the pump. As the level in the upper tank in Fig. 5.2 continues to increase, the pressure in the line between the tanks also increases. We have to apply an increasingly higher pressure with the pump to move the fuel oil to the second tank. However, we have specified in our model in Fig. 5.3 that the speed of the pump should be kept constant. Therefore, an ever-increasing torque must occur at the pump shaft during the pumping process. This expected result is also shown in the graph in Fig. 5.3.

5.2 Two Fuel Oil Tanks and a Pump

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Fig. 5.3 Torque of the pump shaft and pressure behind the hydraulic oil pump

Problems Let’s look at some problems we can explore with the digital twin of the two connected tanks. 5.1 What happens to the simulation when the bottom tank is empty? Investigate how long you can run the simulation. Discuss the result. 5.2 How do the results change when you increase the footprint of the lower tank from 0.5 [m] to twice the value of 1.0 [m]? 5.3 Operate the experimental setup with water instead of fuel oil. What do you have to change? Compare the results. 5.4 Investigate what happens when the speed of the pump is doubled. 5.5 Try to build a network of tanks with a slope of the pipes between them.

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5.3 How Does a Hydraulic Jack Work? Just like in the previous example with the two tanks, we will have to deal with liquids. Let’s take a closer look at the example of the hydraulic jack [HJ001]. It basically consists of a hydraulic piston. A hydraulic jack can be used to lift up cars or other vehicles to change the wheels. Most liquids such as water or oil, unlike gases such as air, have the property that they are incompressible. This means that they cannot be compressed. Density changes are therefore usually not very large. This property can be exploited very well in lifting platforms, jacks, or an excavator bucket of a digger. Volume and mass flows are almost always constant and can be easily balanced.

5.3.1 Simulation Model Our model for the jack in Fig. 5.4 consists of an item for the force [FORC]. This force is coupled with the step signal from the red library [STEP0]. It then acts on the model of a so-called hydraulic jack [HJ000]. The model is already quite complete and complex. The equations behind the model of the hydraulic jack can be found in the help function of Amesim. You can open this help dialog by right-clicking on the item with the mouse. All equations are very well documented and sketches for the technical models are offered for better understanding.

Fig. 5.4 Simulation model of a hydraulic jack connected with a pressure compensation tank

5.3 How Does a Hydraulic Jack Work?

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Important: For liquid lines, the slope is very important because the liquid can only flow downwards if there is no pressure difference. If the liquid has to flow upwards, we need additional pressure, e.g. from a pump. Therefore, when creating the line, you must pay attention where the starting point (1) and the end point (2) are located. Specify the angle of the line between these starting and end points. Negative angles are also possible or the pipes can also be connected in reverse. A pressure accumulator is connected to the bottom of the piston via a throttle [HYDORF0]. In this case, the pressure accumulator [HA001] is relatively small at 0.5 [L]. The pressure inside the accumulator is 50 [bar]. An overflow for leakage is also connected to the hydraulic piston. This is not just a simple connection, but a real line. This model [HL0002] is created automatically by Amesim you will not find it in the library tree. With this type of line, it is important whether the fluid can flow from top to bottom. You must therefore specify the slope of the line.

5.3.2 Submodels and Parameters Carefully enter the parameters according to Table 5.3. The values must be re-entered in Parameter mode in the corresponding field. Also make sure that the index of the hydraulic fluid is set correctly. In order to improve the jack’s function, an additional restriction should be placed between the pressure vessel and the hydraulic piston to limit the flow and reduce pressure pulsations. Set the simulation time back to 10 [s] according to Table 5.4.

5.3.3 Results and Analysis The simulation runs for exactly 10 [s] according to the specification given in Table 5.4. After the first second, the jack is pushed down from the top with a force of 100 [N]. As can be seen in Fig. 5.5, this happens suddenly. The signal occurs within a very short time window. Due to its viscosity, the hydraulic oil cannot react as quickly as possible. This is noticeable in an oscillation of the oil pressure. We see in Fig. 5.6 that the piston does move downwards in accordance with the force signal. However, it responds with an oscillation at the end of the force application due to the damping of the oil volume inside the piston. The oscillation continues through the pipeline. The components “phone” each other. In the accumulator tank, we can see an increase in pressure from 50 to over 52 [bar]. When the force decreases, the piston can then release the pressure from the accumulator tank. However, in Fig. 5.7, we also see a clear pressure oscillation in the accumulator despite the throttle. We can now perform some experiments with the

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Table 5.3 Parameters for the jack simulation model Item Parameter STEP0 FP04

HJ000

HYDROF0

HL0002

HA001

TK000

Value after step = 100 [zero] Step time = 1 [s] Index of hydraulic fluid = 0 Temperature = 40 [degC] Density = 850 [kg/m**3] Bulk modulus = 17000 [bar] Absolute viscosity = 51 [cP] Pressure at port 1 = 40 [bar] Pressure at port 2 = 0 [bar] Rod velocity = 0 [m/s] Rod displacement = 0.3 [m] Index of hydraulic fluid = 0 Piston diameter = 25 [mm] Rod diameter = 12 [mm] Length of stroke = 0.3 [m] Dead volume at port 1 end = 50 [cm**3] Dead volume at port 2 end = 50 [cm**3] Total mass being moved = 250 [kg] Angle rod makes with horizontal = 90 [degree] Coulomb friction force = 0 [N] Stiction force = 0 [N] Viscous friction coefficient = 0 [N/(m/s)] Leakage coefficient = 0 [L/min/bar] Index of hydraulic fluid = 0 Number of parallel orifices = 1 [] Orifice geometry = circular [] Diameter = 3 [mm] Index of hydraulic fluid = 0 Section type = circular Diameter = 25 [mm] Length = 1 [m] Relative roughness = 1e-05 [] Pressure at port 1 = 40 [bar] Index of hydraulic fluid = 0 [] Adiabatic initialization = 1 [] Gas precharge pressure = 40 [bar] Accumulator volume = 0.5 [L] Polytropic index = 1.4 [zero] Tank pressure = 0 [bar]

5.3 How Does a Hydraulic Jack Work? Table 5.4 Simulation time for the simulation model of a hydraulic jack

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Simulation settings Start time = 0 [s] Final time = 10 [s] Print interval = 0.01 [s]

Fig. 5.5 Force on the jack—after one second the force is increased from 0 to 100 [N]

Fig. 5.6 Downwards movement of the hydraulic piston in the jack

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Fig. 5.7 Pressure oscillation—pressure in pressure accumulator increases from 50 to 52 [bar]

digital twin of the jack in Fig. 5.4. We can investigate the following work suggestions for this hydraulic model on our own.

Problems What can we investigate with our model if we want to lift up a car? Have a look at the following problems. 5.6 How do the results change when the moving mass increases or decreases? Use masses with 150, 250, and 500 [kg]. 5.7 What happens if we increase the accumulator? Change it once to 50 [L]. How does this affect the pressure oscillation? 5.8 How does the pressure oscillation change if you increase or decrease the throttle cross section? 5.9 Change the material values of the hydraulic oil. What would happen if you ran the jack with water? Does that make sense technically?

References

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References 1. Idel’cik, I.E.: Handbook of Hydraulic Resistance, 3rd edn. Begell House Inc., (1996) 2. Miller, D.S.: Internal Flow Systems, 2nd edn. Amazon Technology (1989) 3. Bode, B.: Verfahren zur Extrapolation wichtiger Stoffeigenschaften von Flüssigkeiten unter hohem Druck. Tribol. Schmierungstech. 37(4), 197–202 (1990)

Chapter 6

The Pneumatic Twin

6.1 Pneumatic, Fluid Flow, and Turbulence Gases such as air have the property of being compressible under common ambient conditions. This means that they can be compressed by a cylinder. Simplified, we can say that gases obey the ideal gas law. Density changes can be very large in gases. Also, the mathematical models and solution methods are usually more elaborate for compressible media than for incompressible fluids. Mass flows are also constant here, but volume flows need not always be constant. Therefore, the same technical applications are often not possible for gases as for liquid fluids. And solution instabilities will be different. Important: The numerical stability of a library is also influenced by its physics. All fluid media must be divided into compressible or incompressible media and it must be clear before selecting a library whether the problem is hydraulic or pneumatic. Use the index of the fluid to define the properties. The description of turbulence is weak in Amesim. Use a computational fluid dynamics (CFD) software tool for detailed description of turbulence. The word pneumatic comes from the ancient Greek pneuma and means breath or wind. In creating the digital twin, we examine how gases can be transported inside pipe systems. When using the pneumatic library, you should keep in mind that gases are usually compressible. This can lead to higher computational effort and a worse convergence of the mathematical equation system than in incompressible problems. It is also difficult to calculate complicated phenomena such as vortexes or turbulence. To model turbulence, one should use another simulation tool such as ANSYS CFX or ANSYS Fluent [1].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_6

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6.2 Safety Valve for a Biogas Tank Biogas consists largely of a mixture of methane and propane gas with small amounts of air. We want to consider this biogas here simplified only as methane gas. Via a pipeline of 10 [m] the gas is to be led into a tank with a volume of 10 [L]. Normally this is possible without any problems. To prevent over pressure inside the tank and in the pipeline an additional safety valve has to be installed. This safety valve is to prevent the wall of the tank or the pipeline from bursting if the pressure inside rises too high. Often spring is used to seal the valve from the environment. If the gas pressure in the pipeline rises above a certain level, the valve opens and releases a certain amount of the gas into the environment.

6.2.1 Simulation Model Let us build a simulation model like the one shown in Fig. 6.1 in which methane [PNGD00] is filled into the biogas tank through a pipe. The pressure in the tank is supposed to rise above 3 [bar]. A safety valve [PNCV01] is installed in front of the tank [PNCH022] to protect it. The biogas tank should have a volume of 10 [L]. As soon as the pressure rises above a value of 1.8 [bar], the valve opens. All other parameters are given in Table 6.1.

Fig. 6.1 Pneumatic simulation model for the safety valve of a biogas tank

6.2 Safety Valve for a Biogas Tank

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Table 6.1 Parameters for the simulation model biogas tank and safety valve Item Parameter PNGD00 PNCS001 PNGD00 PNCS001 PNCV001

PNCH022

PNL000R

Temperature at port 1 = 293.15 [K] Fluid definition = air Index of hydraulic fluid = 0 [] Pressure at port 1 = 1 [barA] Gas type index = 2 [] Fluid definition = methane (CH4) Temperature at port 1 = 293.15 [K] Pressure at port 1 = 3 [barA] Gas type index = 1 [] Orifice area = 5 [mm**2] Check valve cracking pressure = 1.8 [bar] Hysteresis for opening/closing = 0 [bar] Temperature at port 1 = 293.15 [K] Pressure at port 1 = 1.013 [barA] Gas type index = 2 [] Volume = 10 [L] Thermal exchange coefficient = 500 [J/m**2/K/s] Thermal exchange area = 0.1 [m**2] Exchange temperature = 293.15 [K] Gas type index = 2 [] Diameter of pipe = 10 [mm] Pipe length = 10 [m] Relative roughness = 1e-05 [zero]

Table 6.2 Simulation time for the simulation model biogas tank and safety valve

Simulation settings Start time = 0 [s] Final time = 10 [s] Print interval = 0.1 [s]

6.2.2 Submodels and Parameters The simulation times for the biogas tank with the safety valve are given in Table 6.2. We set the simulation time to a high value to see the full reaction of the pressure inside.

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Fig. 6.2 Pressure in the biogas tank and mass flow of the gas through the safety valve

6.2.3 Results and Analysis In the simulation results in Fig. 6.2, two curves are now shown in the diagram. On the one hand, the pressure in front of the tank and, on the other hand, the mass flow of the methane gas can escape through the safety valve. After a successful simulation, click on the result variable to draw and map the simulation results in a diagram in Fig. 6.2. It can be seen that the pressure increases up to the limit of the gas valve. Then the valve opens and the methane is released into the environment for safety reasons. Thereupon, the curve for the pressure flattens out.

Problems Have a look at a few work proposals that we can explore with our models: To do this, you should find the appropriate model parameters in the simulation model and modify them. 6.1 Lower the pressure of the methane gas at the inlet. Observe what happens. 6.2 Increase and decrease the volume of the tank and observe how this affects the pressure curve in the tank.

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6.3 Change the opening behavior of the safety valve by decreasing the pressure at which the valve opens. What happens to the pressure in the tank? 6.4 How do the results change if you use propane instead of methane as biogas? What changes if you simulate with air?

6.3 Ventilation System of a Building The design of the ventilation system for a building is not as trivial as it first seems. However, compared to a system of water pipes, we encounter them relatively often. In large department stores or furniture stores, you can see them on the ceiling. Even though air flows have only small pressure differences compared to liquid flows, differences of even a few pascals [Pa] can lead to high velocity gradients. Anyone who has observed ventilation pipes with an expert’s eye may have noticed that the pipes still have a larger diameter at the beginning. At the end, where the air exits the vents, the pipes become thinner and thinner. This is because at the beginning you still have very high volume flows, but these decrease with each additional branching of the pipe. In addition, another difference compared to liquids should be pointed out. Gas flows are usually compressible flows. Gases can be compressed during movement or at rest. They thereby reduce their volume but increase their density. This relationship must be taken into account during the calculation.

6.3.1 Simulation Model We want to build a simulation model for a building where we introduce air from a main duct into a ventilation system and then split it to other ducts. The pipe branches and different lengths result in different pressure losses. In turn, the volume flow in the other parts of the pipe network depends on the pressure drop in a pipe. The whole model is shown in Fig. 6.3. The parameters and submodel are listed in Table 6.3 (see Table 6.4). The simulation time is given in Table 6.5. To make this example a bit more interesting and challenging, the reader should try to find the ramp for the step signal [UD00] by himself. In Fig. 6.4, the mass flow of the compressor can be seen and the duration of the steps of the signal [UD00] can be calculated from this diagram.

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Fig. 6.3 Pneumatic simulation model of a ventilation system for buildings Table 6.3 Parameters for the simulation model of a ventilation system (part 1) Item Parameter PNGD00 PNCS001 PNCP00

PNPC1

PNBP001

Gas type index = 1 [] Fluid definition = air Temperature at port 1 = 293.15 [K] Pressure at port 1 = 1.013 [barA] Gas type index = 1 [] Compressor displacement = 1000 [cc/rev] Polytropic constant = 1.35 [null] Gas type index = 1 [] Diameter of pipe = 8 [cm] or 15 [cm] Pipe length = 5 [m] Relative roughness = 1e-05 [null] Gas type index = 1 [] Hydraulic diameter = 8 [cm] Curvature radius = 10 [cm] Center angle = 60 [degree] Relative roughness = 1e-05 [zero]

6.3 Ventilation System of a Building

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6.3.2 Submodels and Parameters

Table 6.4 Parameters for the simulation model of a ventilation system (part 2) Item Parameter TPTE001

PN3P000

PNL0000

Gas type index = 1 [] Diameter at port 1 = 15 [cm] Diameter at ports 2 and 3 = 20 [cm] Friction factor in the main branch = 0.1 [null] Friction factor side branch = 1.2 [null] Gas type index = 1 [] Pressure drop coefficients = Idelchik Side branch diameter (port 1) = 15 [cm] Straight passage diameter (port 2 and 3) = 15 [cm] Angle between side branch and straight passage = 90 [degree] Critical Reynolds number = 5000 [zero] Time constant = 1e-06 [s] Transition accuracy = 0.9 [null] Gas type index = 1 [] Model = polytropic Diameter of pipe = 10 [cm], 15 [cm] and 20 [cm] Pipe length = 1 [m], 2 [m], 3 [m] and 20 [m] Polytropic constant = 1.35 [zero]

Table 6.5 Simulation time for the simulation model of a ventilation system

Simulation settings Start time = 0 [s] Final time = 10 [s] Print interval = 0.1 [s]

6.3.3 Results and Analysis To generate the airflow, we use a compressor. Actually, a blower should be used for this purpose. However, modeling a blower in Simcenter Amesim is quite complex and only feasible with additional efficiency tables. Therefore, we want to do without it in the model in Fig. 6.4. The compressor is driven by a motor. Different speeds are to be investigated.

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Fig. 6.4 Mass flow rate of the air at the compressor at different speeds

Fig. 6.5 Mass flow of the air at all three outlets of the ventilation system

In Fig. 6.4, we look at the mass flows of air at the end of the three branches of the ventilation system. There is a different mass flow in each pipe. Depending on the length and diameter of the pipes, but also due to diversions, there are different pressure drops. This further complicates the behavior of the pipe system.

Reference

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You can see the air distribution at the three outlets of the duct system in Fig. 6.5. Interpret what this means for the ventilation system. Is it desirable to have a strong uneven distribution of air flows? How does this affect the indoor climate inside the building? Also work on the following suggestions.

Problems We want to look at following problems and solve them with the model of the ventilation system: 6.5 Try changing the speed distribution on the compressor. How does this affect the airflow? 6.6 Does the air distribution stay the same if they keep increasing the speed of the compressor? 6.7 Investigate how the air distribution changes when they make individual pipes thinner. 6.8 Each of the ventilation pipes should have a slider. You can open and close it. Sliders result in changed pressure drops at the outlets, on which in turn the volume flow depends. How can they create a uniform air distribution? 6.9 Create additional flow channels and try to adjust them so that the air is distributed as evenly as possible at the outlets.

Reference 1. https://www.ansys.com/

Chapter 7

The Electric Twin

7.1 Permanent Electric Motor with Load The electrical motor model shown in Fig. 7.1 is a permanent magnet motor. It can be found in larger fluid energy machines, such as for driving ship propellers or compressors. The model can work either as a motor or a generator. The model of the machine or generator is [EMDPMDC01] which can be seen as reversible. Important: The electric current in the rotor is needed to produce torque. It is obtained by electromagnetic induction from the magnetic field of the stator winding [1]. This produces waste heat inside the armature. An induction motor can be made without electrical connections to the rotor. An induction rotor can be either wound or squirrel-cage type [1].

7.1.1 Simulation Model Let us build the simulation model as shown in Fig. 7.1, in which the motor is introduced and connected to the electric current. The current is given by a function from the signal library. The signal step model [UD00] is a duty cycle submodel with a dimensionless output. The user may specify three stages giving a start value, an end value, and the duration for each stage. Linear interpolation is used to determine the output. Thus constant sections, ramps, and steps may be constructed with the signal library. For the signal library, a designer dialog is given to define the specific application to create the piecewise linear function. It is available by double clicking on the icon in parameter mode and in simulation mode. The item is used to generate piecewise linear signals like ramps, steps, squares, saw tooth, or trapezoidal signals. Using the electrical motor convention, the rotor relative speed can be measured by sensors for torque T and rotary speed ω as an output speed at the mechanical © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_7

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Fig. 7.1 Permanent electric DC motor with voltage source and mechanical load

Fig. 7.2 Stepwise voltage increase as an input to the permanent electric motor

port and the electromagnetic torque can be measured with T as an output torque at the mechanical port. When ω and T are of same sign, the machine is working as a motor and the output mechanical power P is positive. When ω and T are of opposite sign, the machine is working as a generator and the output mechanical power P is negative. This formula can be used for further examinations. The diagram in Fig. 7.2 shows a stepwise voltage increase and the resulting rotor speed of this example. You can choose whether the machine is acting as a motor or generator by looking at the signs. If the signs of the speed (n) and the torque (M) are different, the result of the mechanical power is negative (P) and thus the machine

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Table 7.1 Parameters for the simulation model of the permanent motor with load Item Parameter EMDPMDC01

UD00

THTS1 RL02

Reference temperature = 25 [degC] Armature winding resistance = 0.6 [Ohm] Corrective coefficient armature winding = 0 [1/K] Armature winding inductance = 0.012 [H] Electromotive force and torque constant = 1.8 [V*s/rad] Corrective coefficient electromotive force = 0.0 [1/K] Number of stages = 3 [] Cyclic = no Time at which duty cycle starts = 0 [s] Output at start of stage 1 = 48 [] Output at end of stage 1 = 48 [] Duration of stage 1 = 3 [s] Output at start of stage 2 = 100 [] Output at end of stage 2 = 100 [] Duration of stage 2 = 3 [s] Output at start of stage 3 = 200 [] Output at end of stage 3 = 200 [] Duration of stage 3 = 3 [s] Temperature at port 1 = 20 [degC] Moment of inertia = 0.005 [kg m**2]

is operated as a generator. As can be seen, the signs are different in this example and the machine is regarded as a generator. The following formula gives the ratio of speed and torque: P = 2π · M · n (7.1) With the variables P for power in [W], M torque [Nm], and n speed [1/s] . The electromotive force and torque coefficient and the armature winding resistance are corrected with the temperature. The variables to solve this equation could be taken from the sensors in the model (see Table 7.1).

7.1.2 Submodels and Parameters To improve the function of the electric model, set the simulation time to 9.0 [s] according to Table 7.2. The armature current can then be computed inverting the generalized Ohm’s law equation for the rotor phase. For details concerning the calculation of power, energy, and activity in this submodel, please refer to the definitions specific to this library in the help menu.

84 Table 7.2 Simulation time for the simulation model for permanent motor with load

7 The Electric Twin Simulation settings Start time = 0 [s] Final time = 9 [s] Print interval = 0.001 [s]

The simulation times for the twin of the permanent electrical motor with load are given in Table 7.2.

7.1.3 Results and Analysis When looking at the simulation results of speed and torque in Fig. 7.3, two curves are now shown in the diagram. This is always helpful if you want to explain how the values influence each other. After successful simulation, you can click on the result variable and drag it into an existing diagram like in Fig. 7.3. On one hand, the relative motor speed due to the different currents is shown. After each step, an oscillation of the rotor speed can be seen, even if the electromagnetic torque remains more or less constant. It reacts to the oscillations in the generator’s magnetic field.

Fig. 7.3 The torque [rev/min] and speed [Nm] of the electric permanent DC motor

7.2 How an Asynchronous Motor Works?

85

Problems For our model of the electric motor we can make several working suggestions. Take a look at a few work suggestions that we can explore with our twin: To do this, they should find and modify the appropriate model parameters in the simulation model. 7.1 Change the armature winding resistance to 1.0 [Ohm] and to 2.4 [Ohm]. Explain what happens to the results. 7.2 Change the temperature of the [THTS1] from 20 [degC] to 40 [degC]. What happens? 7.3 Couple the motor’s armature with an additional convective cooling element from the thermal library. Test different materials, such as iron, cast iron, aluminium, etc. 7.4 Change the load’s moment of inertia. Explain what is happening.

7.2 How an Asynchronous Motor Works? A three-phase, asynchronous motor consists of two basic components, stator and rotor. The stator is the fixed part of the motor. It has a stator core and field windings often made of metal wires. Inside the stators of three-phase asynchronous motors, there is a separate winding for each phase. It is operating with three-phase alternating current. An asynchronous motor model setup in Fig. 7.4 shows a three-phase motor operated with all three phases of alternating current. These asynchronous motors are

Fig. 7.4 Asynchronous squirrel-cage induction machine with three phase and mechanical load

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7 The Electric Twin

often used in industry for pumps, tools, or even electric vehicles. Friction at mechanical load lowers the efficiency of the motor. Heat conduction plays an important role for its efficiency.

7.2.1 Simulation Model Let us build a simulation model of the electric motor where we can change the three phases of the electric current stepwise by a sinus signal function. The sinus functions can be modeled by [SIN0]. Figure 7.4 shows the input voltage of the asynchronous motor. The phases are each shifted by 120 [degree]. The motor is a squirrel-cage induction machine in a rotor frame. The simulation model [EMDSCIM01] is used to model linear Squirrel-Cage Induction Machine (SCIM) and it is used to rotate a load. In the [EMDSCIM01], the reference angle used for transformation is the rotor position. In the rotor frame, the model quantities vary with the slip frequency at steady-state operating points. The reference angle used for transformation is the rotor flux linkage position. In the rotor flux linkage frame, the model quantities are constant at steady-state operating points.

7.2.2 Submodels and Parameters The important parameters for the submodels of the three-phase motor setup are compiled in Table 7.3. Two submodels [EMDSCIM01] and [EMDSCIM02] return the same results. The model [EMDSCIM02] produces usually faster simulations but it may raise numerical problem when the currents tend to zero. To improve the function, an additional constriction should be placed, set the simulation time to 0.04 [s] according to Table 7.4. Use [EMDSCIM01] when the best robustness is required. Note also that [EMDSCIM01] internal variables in the frame are not consistent with the internal variables of the control elements.

7.2.3 Results and Analysis In Fig. 7.5, you can see the phase shift of the three different currents. The machine windings are balanced, and two different connections are possible. The enumeration winding connection enables to choose between star and delta connection for the equivalent windings A, B, and C. The three-phase electrical ports are associated on the icon with the notations I, II, and III. The star and delta connections for the windings A, B, and C as well as the winding voltages and currents are defined within this convention.

7.2 How an Asynchronous Motor Works?

87

Table 7.3 Parameters for the simulation for the electric three-phase motor Item Parameter EMDSCIM01

RL02 THTS1 W000 SIN0-1

SIN0-2

SIN0-3

Winding connection = star [connectType] Number of pole pairs = 1 [] Reference temperature = 25 [degC] Stator winding resistance = 1 [Ohm] Corrective coefficient on stator resistance = 0 [1/K] Stator cyclic inductance = 0.25 [H] Rotor time constant = 0.153 [s] Corrective coefficient on rotor time = 0 [1/K] Dispersion coefficient = 0.066 [] Moment of inertia = 0.005 [kgm**2] Temperature at port 1 = 20 [degC] Fixed angle = 0 [degree] Sine wave frequency = 50 [Hz] Mean level = 0 [] Sine wave amplitude = 230 [s] Phase shift = 0 [degree] Sine wave frequency = 50 [Hz] Mean level = 0 [] Sine wave amplitude = 230 [s] Phase shift = −120 [degree] Sine wave frequency = 50 [Hz] Mean level = 0 [] Sine wave amplitude = 230 [s] Phase shift = +120 [degree]

Table 7.4 Simulation time for the simulation model of the electric three-phase motor

Simulation settings Start time = 0 [s] Final time = 0.04 [s] Print interval = 0.0001 [s]

Problems Let’s look at some problems that we can now investigate with our electric motor twin. To do this, you should modify the corresponding model parameters in the simulation model. 7.5 Change frequency of the three sinusoidal signal functions up to 100 [Hz]. 7.6 Try a different stator winding resistance like 2 or 3 [Ohm].

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Fig. 7.5 Phase-shifted voltage curves of the asynchronous motor with three phase and mechanical load

7.7 Change the rotor time constant to 0.165 [s]. What happens? 7.8 Change the load of the model and change the limit temperature of the armature of the motor. Give an interpretation of your results.

7.3 Electric Generator with Resistor and Heat Transfer The permanent magnet motor or generator can be defined as a motor which includes a permanent magnet pole, and therefore it is called Permanent Magnet DC Motor (PMDC). The motor includes an armature core, commutator, and armature winding. There are usually two different types of windings in a conventional DC motor. A solenoid can be used to make the flux working within the air gap in its place of the field winding. This type of machine is always reversible, so it can work either as a motor or as a generator. The rotor structure is similar to the straight DC motor.

7.3.1 Simulation Model Let us build a motor simulation model in combination with the thermal library that was already described before and combine it with a heat transfer problem. Figure 7.6 shows the input voltage permanent magnet DC motor.

7.3 Electric Generator with Resistor and Heat Transfer

89

Fig. 7.6 Electric permanent magnet DC motor (PMDC) in combination with the thermal library

The model setup in Fig. 7.4 shows a generator operated by an mechanical drive [OMEGC0]. The main function of field winding is to produce the functioning magnetic flux within the air gap as well as wound on the stator of the motor while armature winding can be wound on the rotor. Inactive carbon brushes are pushed on the commutator like in conventional DC motor. Using the motor convention, the rotor relative speed can be measured with W as an output speed at the mechanical port and the electromagnetic torque can be measured with T as an output torque at the mechanical port. When W and T are of same sign, the machine is working as a motor and the output mechanical power P is positive. When W and T are of opposite sign, the machine is working as a generator and the output mechanical power P is negative.

7.3.2 Submodels and Parameters The important parameters for the submodels of the generator setup are compiled in Table 7.5. There are two submodels, namely, [EMDPMDC01] and [THC000]. The model [EMDPMDC01] produces a heat source to heat up the mass model [THC000]. To improve the function, an additional constriction should be placed, set the simulation time to 0.04 [s] according to Table 7.6. Use [EMDSCIM01] when the best robustness is required. Note also that [EMDSCIM01] internal variables in the frame are not consistent with the internal variables of the control elements.

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Table 7.5 Parameters for the simulation for the electric three-phase motor Item EMDPMDC01

Parameter Reference temperature = 25 [degC] Armature winding resistance at reference temperature = 0.6 [Ohm] Corrective coefficient on armature winding resistance = 0 [1/K] Armature winding inductance = 0.012 [H] Electromotive force and torque constant at reference temperature = 1.8 [V*s/rad] Corrective coefficient on electromotive force and torque constant = 0 [1/K]

EBR02

Type of reference resistor value = resistor value directly entered by user Resistance = 1 [Ohm]

THSD00

Solid type index = 1 Material definition = pure aluminium (Al)

THC000

Solid type index = 1 Mass of material = 0.5 [kg]

UD00

Number of stages = 3 [] Cyclic = no Time at which duty cycle starts = 0 [s] Output at start of stage 1 = 48 [] Output at end of stage 1 = 48 [] Duration of stage 1 = 3 [s] Output at start of stage 2 = 100 [] Output at end of stage 2 = 100 [] Duration of stage 2 = 3 [s] Output at start of stage 3 = 200 [] Output at end of stage 3 = 200 [] Duration of stage 3 = 3 [s]

Table 7.6 Simulation time for the simulation model of the electric three-phase motor

Simulation settings Start time = 0 [s] Final time = 10.0 [s] Print interval = 0.01 [s]

7.3.3 Results and Analysis To examine the simulation model of the generator, we check the torque at the shaft of the generator firstly to see if mechanical load is provided. In Fig. 7.7, we can see that all boundary conditions are set correctly.

7.3 Electric Generator with Resistor and Heat Transfer

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Fig. 7.7 Checking the boundary conditions of the generator’s torque based on mechanical drive

In Fig. 7.8, the electric power is presented which can be calculated by the P formula. The mechanical load will be converted into electric capacity in the generator and the direct current machine will not only generate electric energy, but also heat. P =U·I

(7.2)

The thermal losses are identified with Joule’s losses in the machine which can be estimated by the changing temperature in the generator’s housing, as can be seen in Fig. 7.9. Joule heating, also known as resistive, resistance, or Ohmic heating, is the process by which the passage of an electric current through a conductor produces heat. The temperature change in the housing is influenced by the mass of the housing [THC000] and the material. Here in Fig. 7.9 we used aluminium, but alternative materials like iron or cats iron can also be used. We can see how the temperature changes inside the generator housing. Cooling of housing for electric generators as well as the construction and design of fins for heat conductors is an important topic. It is widely used for different applications like wind turbines, power plant, and other fluid energy machines. Here we see a good example of the connection between the thermal library and the electric library.

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Fig. 7.8 Changes of the current and voltage steps based of the production of the generator

Fig. 7.9 Joule’s losses produce a heat source in the housing of the generator

Problems We want to have a look at some problems that we now can explore our generator. To do this, you should modify the simulation model. 7.9 Change the thermal mass that is connected to the housing to 5 [kg] and to 10 [kg] and compare the results. Use cast iron as alternative housing material. Discuss the results.

Reference

93

7.10 Change the armature winding resistance at reference temperature to 2 and 6 [Ohm]. What happens? 7.11 How long does it take to heat up the housing up to 350 [K]. Do you see any influence on the current? 7.12 Try to build a convective cooling unit with the thermal library to cool down the electric housing.

Reference 1. IEC 60050 Rotation Machinery – General, IEV ref. 411-31-10: Induction Machine – an asynchronous machine of which only one winding is energized. Publication date: 1990-10. Section 411-31

Chapter 8

Analysis of Complex Technical Systems

8.1 How Does the Liquid Piston Compressor Work? The storage of energy, e.g. from wind or water turbines, is one of the main objectives in the future development of stable power grids [1, 2]. In this chapter, a model for a liquid piston compressor (LPC) is presented. This LPC can be used to store mechanical energy produced by a wind or water turbine [3]. Based on a short explanation of the structure and function of an LPC, it is shown how such a system can be modeled from existing hydraulic, pneumatic, and mechanical components. In sections that build on one another, a basic model is first developed and parameterized. Subsequently, the model is successively extended, e.g. second piston, control system, etc. and simulation results are presented and discussed in diagrams after each section. Finally, a parameter study on the overall model is presented. Important: In Amesim all elements of the different libraries can be connected and combined to create more complex models. They can be combined with each other or with the signal library. In the student version only some basic libraries are included.

8.2 Design and Function of a Liquid Piston Compressor In one of the simplest designs of a compressor with a liquid piston, the liquid piston acts as a direct hydraulic-pneumatic transformer. This possibility of compressing air in the application of an energy storage system was presented, among others, in [4]. Figure 8.1 schematically shows the circuit diagram of such a system with a hydraulic-pneumatic transformer. In this process, a cylinder filled with gas is filled with a liquid, e.g. hydraulic oil or water, by a hydraulic pump (the hydraulic valve was controlled for this purpose— switching position 2). The liquid flowing into the cylinder compresses the gas in the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_8

95

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Fig. 8.1 System with technical components of a liquid piston compressor (LPC) [3]

cylinder and the pressure in the system increases. When the pressure prevailing in the accumulator is reached, the check valve “2” opens and releases the gas flow to the accumulator. When the cylinder is almost completely filled with the liquid, the hydraulic valve switches again (switching position 1) and the liquid in the cylinder can run back into the liquid tank. The vacuum thus created ensures that the cylinder is filled with gas from the environment via check valve “1”. The process can then run again. We want to present three variants of the LPC in detail.

8.3 Liquid Piston Compressor with One Cylinder for One Stroke 8.3.1 Simulation Model For modeling purposes, the existing components from the areas of hydraulics, pneumatic, and mechanics are to be used first. For this purpose, the real system is replaced by the combination of a spring-loaded single-acting hydraulic cylinder in combination with a spring-loaded single-acting pneumatic cylinder. To couple the two cylinders, a mass is interposed, since coupling of both cylinders is not possible without an inertial mass (natural frequency of the system). The model for a liquid piston Compressor with One Cylinder for One Stroke can be seen in Fig. 8.2. The role of the relief valve [RV010] is to limit the upstream pressure within a hydraulic circuit and thus protect hydraulic components from over pressure. This component is also known as pressure limiting valve, maximum-pressure valve, or safety valve [5].

8.3 Liquid Piston Compressor with One Cylinder for One Stroke

97

Fig. 8.2 Digital twin for liquid piston compressor with one cylinder for multiple strokes

8.3.2 Submodels and Parameters To improve the function of the model, set the simulation time to 10 [s] according to Tables 8.1 and 8.2 in this submodel, refer to the definitions specific to this library in the help menu. The fluid [FP04] inside the hydraulic cylinder is an hydraulic oil. The existing model must now be parameterized accordingly. In the following only those parameters are shown, which deviate from the basic settings. The simulation times for the simulation model are given in Table 8.3.

8.3.3 Results and Analysis To check, if the twin of the first liquid piston compressor is working, we plot the movement of the mass element between the pneumatic and the hydraulic chamber. The result is presented in Fig. 8.3, and we can see that the hydraulic pressure-relief valve [RV010] is working and switches and the mass reaches its maximum position at about 4.5 [s]. From the results, the pressure curves inside the hydraulic cylinder, the pneumatic cylinder, and the pressure accumulator can be evaluated, among other things. The

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Table 8.1 Parameters for simulation model of the liquid piston compressor with one cylinder for one stroke (part 1) Item Parameter PNCH022

PNCV010

PNGD00

PNAS001 PNJ002

Temperature at port 1 = 293.15 [K] Pressure at port 1 = 1.013 [barA] Gas type index = 1 Volume = 0.1 [L] Thermal exchange coefficient = 0 [J/m**2/K/s] Thermal exchange area = 0.1 [m**2] External temperature = 293.15 [K] Gas type index = 1 Check valve cracking pressure = 0.2 [bar] Check valve mass flow rate pressure gradient = 10 [g/s/bar] Valve hysteresis = 0 [bar] Gas type index = 1 Fluid definition = air Properties definition = semi perfect Temperature at port 1 = 293.15 [K] Temperature at port 1 = 293.15 [K] Pressure at port 1 = 1.013 [barA] Gas type index = 1 Model = with thermal exchange Use initial displacement = yes [] Displacement of piston = 0.5 [m] Piston diameter = 25 [mm] Rod diameter = 12 [mm] Length of stroke = 0.5 [m] Dead volume at port 1 end = 50 [cm**3] Thermal exchange coefficient = 0 [J/m**2/K/s] External temperature = 293.15 [K] Viscous friction coefficient = 0 [N/(m/s)] Leakage coefficient = 0 [g/s/barA] Spring preload = 0 [N] Spring rate = 1 [N/m] Pressure in rod chamber = 1.013 [barA] Spring rate at endstops = 100000 [N/mm] Damping coefficient at endstops = 100000 [N/(m/s)] Deformation on endstops at which damping rate = 0.001 [mm]

8.3 Liquid Piston Compressor with One Cylinder for One Stroke

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Table 8.2 Parameters for simulation model of the liquid piston compressor with one cylinder for one stroke (part 2) Item Parameter RV010

HJ0023

PU001

PM000 FP04

MECMAS21

TK000

Index of hydraulic fluid = 0 Relief valve cracking pressure = 5 [bar] Relief valve flow rate pressure gradient = 500 [L/min/bar] Valve hysteresis = 0 [bar] Pressure at port 1 = 1.013 [bar] Index of hydraulic fluid = 0 Use initial displacement = yes Displacement of piston = 0 [m] Piston diameter = 25 [mm] Rod diameter = 12 [mm] Length of stroke = 0.5 [m] Dead volume at port 1 end = 50 [cm**3] Viscous friction coefficient = 0 [N/(m/s)] Leakage coefficient = 0 [L/min/bar] Spring preload = 0 [N] Spring rate = 1 [N/m] Pressure in rod chamber = 1 [bar] Spring rate at endstops = 100000 [N/m] Damping coefficient on endstops = 10 [N/(m/s)] Deformation on endstops at which damping rate is fully effective = 0.001 [mm] Index of hydraulic fluid = 0 Pump displacement = 2 [cc/rev] Typical pump speed = 1500 [rev/min] Shaft speed = 1500 [rev/min] Type of fluid properties = elementary Index of hydraulic fluid = 0 Temperature = 40 [degC] Name of fluid = unnamed fluid Density = 850 [kg/m**3] Bulk modulus = 17000 [bar] Absolute viscosity = 51 [cP] Velocity at port 1 = 0 [m/s] Displacement at port 1 = 0 [m] Use friction = no Endstop type = none Mass = 0.1 [kg] Inclination (+90 port 1 lowest, −90 port 1 highest) = 0 [degree] Tank pressure = 0 [bar]

100 Table 8.3 Simulation time for the simulation model of the liquid piston compressor

8 Analysis of Complex Technical Systems Simulation settings Start time = 0 [s] Final time = 10 [s] Print interval = 0.01 [s]

Fig. 8.3 Displacement of the mass element between hydraulic and pneumatic pistons

pressure curves in the hydraulic cylinder and the pneumatic cylinder are shown in Fig. 8.4. It can be clearly seen that the compression process takes place exactly once. When the mass reaches its maximum position, the cylinders are not able to move further on. So the hydraulic pump increases the pressure in the hydraulic system very fast. At a pressure of 5 [bar] the pressure-relief valve opens and the hydraulic oil flows through this valve directly back to the tank. The pressure curves of the pressure accumulator are shown in Fig. 8.5. First, the pressure in the pneumatic cylinder is increased by the extension of the hydraulic cylinder, which corresponds to a retraction of the pneumatic cylinder, until the accumulator pressure is reached. A slight pressure fluctuation in the fluid [FP04] of the hydraulic cylinder can be seen. The valve [RV010] is normally closed. When the pressure drop across the valve exceeds the relief valve cracking pressure (typically a spring force), the valve opens and let the fluid flow across so that the pressure drop gets regulated to the cracking pressure [5]. Subsequently, the pressure in Fig. 8.5 remains almost constant and the air present is forced into the pressure accumulator. In the process, the pressure increases both in the pressure accumulator and in the pneumatic and hydraulic cylinders. When the hydraulic cylinder is completely extended (end position), the hydraulic pressure

8.3 Liquid Piston Compressor with One Cylinder for One Stroke

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Fig. 8.4 Diagram of pressure curve in the hydraulic, pneumatic cylinder

Fig. 8.5 Diagram of pressure curve in the pressure accumulator chamber

increases like mentioned before until the opening pressure of the pressure-relief valve is reached and the process is finished.

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8.4 Liquid Piston Compressor with One Cylinder and Multiple Strokes 8.4.1 Simulation Model In order to be able to run through the compression process several times, a control system for the movement of the piston must be implemented. For this purpose, a 2/3-way hydraulic valve [HSV23_02], a displacement sensor [MECDS0B], and a “THRESHOLD” are to be added to the model, as can be seen in Fig. 8.6. The sensor gives a trigger signal for the lift and controls the valve. In Fig. 8.6, we can see the digital twin for liquid piston compressor with one cylinder for multiple strokes with components from the Amesim model. We improved the model from the further section and added a two-position three-port hydraulic servo-valve [HSV23_02] to the model. The existing model must now be parameterized accordingly. In the following only the parameters of the added components are shown, which differ from the basic settings to avoid repetition. Only additional parameters will be given in Table 8.4.

Fig. 8.6 Digital twin for liquid piston compressor with one cylinder for one stroke

8.4 Liquid Piston Compressor with One Cylinder and Multiple Strokes

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Table 8.4 Additional parameters for liquid piston compressor with one cylinder for multiple strokes Item

Parameter

MECDSS0B

Sign convention: positive from = 0 Offset to be subtracted from displacement = 0 [m] Gain for signal output = 1 [1/m] Initial output value = low High input threshold value = 0.499 null Low input threshold value = 0.001 null High output value = 0 null Low output value = 1 null Index of hydraulic fluid = 0 Valve rated current = 1 [mA] Valve natural frequency = 80 [Hz] Valve damping ratio = 0.8 null Ports P to A characteristic flow rate at maximum opening = 100 [L/min] Ports P to A corresponding pressure drop = 0.01 [bar] Ports P to A critical flow number (laminar/turbulent) = 100 null Ports A to T flow rate at maximum opening = 100 [L/min] Ports A to T corresponding pressure drop = 0.01 [bar] Ports A to T critical flow number (laminar/turbulent) = 1000 null

TRIG0

HSV23_02

Table 8.5 Simulation time for the liquid piston compressor with multiple strokes

Simulation settings Start time = 0 [s] Final time = 10 [s] Print interval = 0.01 [s]

8.4.2 Submodels and Parameters Most parameters for the digital twin of the liquid piston compressor with one cylinder and multiple strokes stay the same as in the previous model in Fig. 8.6 for the system with one stroke. Therefore, we will make Table 8.4 for the parameters a bit shorter, and add only the parameters for the additional submodels and deviating parameters. Time steps and simulation time for the liquid piston compressor with multiple stroke are given in Table 8.5.

8.4.3 Results and Analysis The travels of the hydraulic and pneumatic cylinders show that they both work in opposite directions. The signal from the displacement sensor is used to switch over

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Fig. 8.7 Displacement of the mass element between hydraulic and pneumatic pistons with multiple strokes

the hydraulic valve via the “THRESHOLD” when the hydraulic cylinder is almost completely extended. In Fig. 8.7, we can see that movement of the mass occurs two times according to the trigger of the signal [TRIG0] and the displacement of the mass element between hydraulic and pneumatic pistons with multiple strokes. In Fig. 8.9, the pressure in the pneumatic and hydraulic cylinder is shown. The hydraulic cylinder after the first stroke retracts again due to the residual pressure in the pneumatic cylinder and the spring force. The pneumatic cylinder draws in new air. When the hydraulic cylinder is almost completely retracted, the process is repeated. With each conveying process, the pressure in the pressure accumulator increases step by step. It can be seen that pressure fluctuations inside the hydraulic fluid are rather high. In technical applications, this behavior should normally be avoided (see Fig. 8.10).

8.5 Liquid Piston Compressor with Two Cylinder and Multiple Strokes

105

Fig. 8.8 Digital twin for liquid piston compressor with two cylinder and multiple strokes

8.5 Liquid Piston Compressor with Two Cylinder and Multiple Strokes 8.5.1 Simulation Model In order to prevent the system from not compressing air during the emptying of the hydraulic cylinder, i.e. not working, so to speak, the system can be extended by another fluid piston. This configuration is shown in Fig. 8.8. The two fluid pistons then work alternately, each using the hydraulic oil of the cylinder that is just emptying to compress the air. For this purpose, the model must be appropriately planed with two cylinders. Now, we added a two-position four-port hydraulic valve [HSV24_02] to the model to switch between the two different hydraulic cylinders. This valve is controlled also by the trigger signal [TRIG0].

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Fig. 8.9 Pressure curves in hydraulic, pneumatic cylinder with multiple stroke and high oscillations

Fig. 8.10 Pressure curves in the pressure accumulator chamber with multiple strokes

8.5 Liquid Piston Compressor with Two Cylinder and Multiple Strokes

107

Table 8.6 Additional parameters for liquid piston compressor with one cylinder for multiple strokes Item

Parameter

HSV24_02

Index of hydraulic fluid = 0 Valve rated current = 1 [mA] Valve natural frequency = 80 [Hz] Valve damping ratio = 0.8 null Ports P to A characteristic flow rate at maximum opening = 100 [L/min] Ports P to A corresponding pressure drop = 0.1 [bar] Ports P to A critical flow number (laminar/turbulent) = 1000 null Ports B to T characteristic flow rate at maximum opening = 100 [L/min] Ports B to T corresponding pressure drop = 0.1 [bar] Ports B to T critical flow number (laminar/turbulent) = 1000 null Ports P to B characteristic flow rate at maximum opening = 100 [L/min] Ports P to B corresponding pressure drop = 0.1 [bar] Ports P to B critical flow number (laminar/turbulent) = 1000 null Ports A to T characteristic flow rate at max. opening = 100 [L/min] Ports A to T corresponding pressure drop = 0.1 [bar] Ports A to T critical flow number (laminar/turbulent) = 1000 null

8.5.2 Submodels and Parameters Additional parameters and data for the new two-position four-port hydraulic servovalve [HSV24_02] were sampled in Table 8.6. All other parameters are similar to the model in the previous section.

8.5.3 Results and Analysis Finally, this model is to be extended according to the basic scheme in Fig. 8.11. For this purpose, it was recorded how the cylinders move in both pistons. It can be seen clearly in the diagram that the movement of the right piston is inverse to the movement of the left piston. When the right piston moves up, the left piston moves down. This is completely plausible. Also, the motion is in the same range because both pistons are of the same size. It could be investigated how the movement of the pistons changes if we choose different volumes. Important: Comparing different variants of the same technical application is an effective method for selecting the best solution. Even though the absolute results may be somewhat different when compared with measurements. The decision for a favored variant might be possible.

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Fig. 8.11 Three displacements of the mass elements of the two pistons of the LPC

Figure 8.12 shows the pressure curves of the pneumatic actuator on the left and the pressure curve of the pneumatic actuator on the right. It should be mentioned

Fig. 8.12 Pressure curve in hydraulic, pneumatic cylinder with multiple strokes

Problems

109

Fig. 8.13 Pressure curve in the pressure accumulator chamber with multiple strokes

that we have not visualized the pressure variations in the hydraulic chamber as in the previous section, but they are comparable. The pressure in the chamber of the pressure accumulator is shown in Fig. 8.13. It can be seen that there are many more stages forming the stepwise pressure rise in the pressure accumulator chamber. We want to analyze our complex technical system and have a look at some problems to improve the model. You can modify the appropriate parameters in the model and try out what’s happening.

Problems 8.1 Change the size of the pressure accumulator from 0.1 to 10 [L]. What happens? 8.2 Change the trigger signal of the sensor and try to reduce the fluctuations inside the hydraulic chamber. 8.3 Add an additional third hydraulic and pneumatic actuator and repeat the simulations. 8.4 Examine the temperature of the gas inside the pneumatic accumulator. Is it adiabatic? How can you change the model to reduce the temperature inside the accumulator? 8.5 Think about a new component to store the heat energy of the gas in the accumulator.

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8.6 Add a hydraulic chamber in the back flow area between the 2/4-way hydraulic valve and the pump.

References 1. Staudacher, T., von Roon, S., Vogler, G.: Energy storage - status, perspectives and economic viability; study summary report; client boarfeleven; performed by Forschungsstelle für Energiewirtschaft e.V., 03.2009 2. German Federal Ministry for Economic Affairs and Energy (The energy of the future, 2021): 8th Monitoring Report on the Energy Transition – Reporting years 2018 and 2019, Access: 2021-05-09. https://www.bmwi.de/Redaktion/DE/Publikationen/Energie/ achter-monitoring-bericht-energie-der-zukunft.html 3. Grün, S., Hübner, D.H., Ortwig, H.H., Rückert, F.U.: Investigation and optimization of a three stage inter-cooled piston compressor for an energy storage system with pressurized air. In: Proceedings of the Interdisciplinary Conference on Mechanics, Computers and Electrics (ICMECE 2022), 6–7 October 2022, Barcelona, Spain 4. Grün, S., Hübner, D.H., D., Molter, J.: Investigation and simulation based optimization of an energy storage system with pressurized air. Arch. Thermodyn. 42(4), 183–200 (2021). https:// doi.org/10.24425/ather.2021.139658. https://journals.pan.pl/Content/122213/PDF/art11_corr. pdf 5. Siemens Digital Industries Software: Simcenter Amesim Student Edition (2020)

Chapter 9

Digital Twins and Artificial Intelligence

9.1 Neural Networks in Nature We want to give an example how one can use an artificial neural network for the physical libraries described before. Neural networks can be used widely in different fields of business administration, economics, science, and technology. They were built similar to biological neural networks of animals. In the following, we will see that our brain or the brain of any other animal in a certain form performs its services with the help of matrix calculation. For this some basics have been formulated by Spitzer [1]. Important: Artificial intelligence (AI) can be used to reproduce results from digital twins much faster, but the quality of the results could be weaker. We can also use AI to control the twins or make results of the calculations available faster and with less effort. AI can be used in many different areas of science.

9.2 Neural Networks and Digital Twins Sensory cells in the eye, ear, nose, skin, etc. react specifically to physical or chemical stimuli and produce output by discharging a binary electrical signal. The so-called action potential. We can describe this in mathematics with the coded 0 and 1. This signal is transmitted via the axon of the sensory cell and usually reaches a large number of other neurons. The nerve cells, which further process the corresponding input. In our brain, each neuron receives impulses from about 10.000 other neurons in this way. An input cell is connected to another nerve cell and, given sufficient stimulus, sends an electrical impulse along the connecting nerve fiber. The AI can also collaborate with complex networks of digital twins and predict new results. Often the predictions are much faster than with a complex system of digital twins, but the quality can be weaker. To produce data for such neural networks, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_9

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Fig. 9.1 Artificial neural networks are used to collaborate with digital twins and predict results

you also need, for example, the pneumatic or thermal library as shown in Fig. 9.1. However, the current does not flow directly into a nerve cell, but into a synapse, which usually docks with so-called dendrites or with the cell body of N. The synapse then generates more or less neurotransmitter molecules, e.g. glutamate, depending on its own strength. The synapse then generates more or less neurotransmitter molecules like glutamate depending on its own strength [1]. The synapse then generates more or less neurotransmitter molecules, depending on its own strength, which pass over to the nerve cell. The synapse then generates an impulse, which passes over to the nerve cell, depending on its own strength. The synapse then generates an impulse when nerve cell receives an impulse. The nerve cell in turn generates an impulse when a sufficient number of transmitter molecules arrive. An important role is played by the fact that very many nerve fibers end and therefore receive transmitter molecules from many synapses simultaneously. This behavior has to be transferred to a mathematical formulation. It depends on the synapse whether it emits inhibitory or excitation transmitter molecules. The synapse thus multiplies the incoming signal (0 or 1) by its own strength, which can be modeled as a number in the interval [−1, 1]. The neuron sums all the values obtained in this way and fires when the summed value exceeds a critical threshold characteristic of the neuron. Creation of an artificial neural network (ANN) is simple in Simcenter Amesim, as shown in Fig. 9.2. A ready-to-use model is already included. The [DYNNNFF01] submodel allows evaluating a restricted feed-forward neural network model having one hidden layer.

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Fig. 9.2 Creation of an artificial neural network with Simcenter Amesim based on signal library

Replacing some Simcenter Amesim physical components by a feed-forward artificial neural network might be a source of undesired algebraic loops since all the dynamics of the original model have been removed. Indeed, such a neural network establishes a direct algebraic relationship between each output and all the inputs. If necessary, algebraic loops can be broken by placing some first-order lags or some discrete delays on some of the neural network’s outputs. Beware that time constants need to be carefully set, depending on the dynamics of the system from which a surrogate model is being created. The icon of [DYNNNFF01] submodel is a dynamic icon. When placing it on the sketch, the user must indicate the number of inputs and the number of the outputs of the neural network model. In addition, the number of neurons used for the training process under MATLAB or an external program which could be provided on our homepage needs to be provided [4]. A backpropagating neural network is a network of neurons, the simplest unit of this technique. A neuron receives inputs, changes its internal state (activation state), and produces outputs in accordance with inputs and activation states. The connection of these elementary units forms the network, connecting the outputs of certain neurons with the inputs of other ones [3]. To start a first test with the neural network data for learning is needed. In Fig. 9.3, we connect our first neural network to input data and output data. We have to make sure that we have two columns of input data for the network and one column of output data as defined in Fig. 9.2. We will come back to this setup later. Firstly, we want to explain how a neural network works and how learning can train the brain.

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Fig. 9.3 Adding data elements to the artificial neural network based on signal library

9.3 The Artificial Frog Model Ever since Alessandro Volta used a frog to demonstrate the influence of electric current, frogs have been widely used for scientific experiments. How a frog processes what it sees? We will discuss a simple frog example for a neural network. Think about a frog whose eye consists of three photo receptors which is shown in Fig. 9.4. Thus, inputs of the network are propagated to the network’s output through the neurons, following weighted connections. When the direction of propagated signals goes always from inputs to outputs, i.e. signals are never propagated backwards, the network is called a feed-forward neural network. The weights and activation functions are modified during the training process in order to get values that provide the best outputs. To train a neural network, a learning algorithm is used to minimize a related cost function. For example, mean least squares can be used between the desired output and the predicted one [3]. There are three events that are important for the frog. The frog sees a stork, a fly, or just the blue sky. Each time, the photoreceptors are excited in a characteristic way. The photoreceptors are now connected to three neurons that initiate an action when they are sufficiently excited.

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Fig. 9.4 Photoreceptors of a frog are now connected to three neurons that initiate an action when they are sufficiently excited [2]

9.3.1 Submodels and Parameters Let us consider a simple test case for the frog model [2]. We imagine a simple living creature, e.g. like a frog in a pond. How can a frog processes what it sees? If the stork is detected like excitation in pattern A, flight is initiated excitation of nerve cell 1. For the frog the fly is the prey. If the fly is sighted (B), the frog sticks out its tongue to catch the fly. This means excitation of nerve cell 3. If the frog sees the blue sky (C), it activates digestion. Excitation of nerve cell 2. For this event, data can be generated. In Fig. 9.5, we give you an example data set with 10 hidden neurons for this case. We now graphically represent the connection between the three visual cells and the three nerve cells, the connecting nerve fibers and the synapses. In each of the synapses, their strength is plotted as a value between -1 and 1. Each of the three nerve cells fires when the sum of the transmitters arriving via the synapses exceeds the measure 0.8 in an interval from 0 to 1 (see Fig. 9.6). The model [DYNNNFF01] can be compared with the [DYNPSM02]. For now, this process needs to be done using the MathWorks Neural Network toolbox for MATLAB [5]. It is also a submodel that allows creating global approximations, and it is especially suitable in presence of non-smooth or highly non-linear models. The input file listed in Table 9.1 has to be generated by MATLAB or by our in-house programmed optiflow neural network tool. The only drawback of this submodel is that it currently has a dependency to the commercial code MATLAB which is rather slow for creating the artificial neural network, but not for evaluating it. Note that, just as [DYNPSM02], [DYNNNFF01] only uses standard Amesim XY tables, which is particularly interesting to create a surrogate model that can be efficiently exported as a Simulink blackbox, as a Functional

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Fig. 9.5 Definition file frog_example.net for the frog with training and test data Table 9.1 Data files written by optiflow neural network tool for Simcenter Amesim Data file Description B1.data B2.data W1.data W2.data Scaling_in.data Scaling_out.data

Bias file for the connection of the input layer with hidden layer Bias file for the connection of the hidden layer with output layer Weighting file for the connections between input and hidden layers Weighting file for the connections between hidden and output layers File with the input scaling matrix File with the output scaling matrix

Mock-up Unit (FMU), or executed on a real-time target. Examples for the XY-tables needed are given in Table 9.1. Important: Artificial intelligence and artificial neural networks are currently a hot topic and under very fast development. Therefore, the information in this book might be outdated already. To prevent this we developed a standalone tool to calculate the weights of the neural network. The icon of [DYNNNFF01] submodel is a dynamic icon. When placing it on the sketch, the user must indicate the number of inputs and the number of the out-

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Fig. 9.6 Enter the input data for the experience of the frog’s life and hunting behavior Table 9.2 Ports for the neural network model Port Description Port 1 Port 2

Used to supply inputs to the neural network model Provides the responses estimated by the neural network model

puts of the neural network model. In addition, the number of neurons used for the training process under MATLAB needs to be provided or you can use our in-house programmed code [3]. For our frog model, we should define three inputs and outputs, and 10 hidden neurons. The item component comprises two ports that are listed in Table 9.2.

9.3.2 Optiflow Neural Network (ONN) To try out simple neural network on your own and to predict test data results, we offer you the optiflow neural network (ONN) tool. The tool can be downloaded under [4]. It is easy to use and free of charge. The user interface to the tool can be seen in Figs. 9.7 and 9.8. After starting the tool one can open a net-file to define the neural network. Doing this is quite simple. An example file is given in Fig. 9.5. For our frog we have to define the number of input cells (N inputs = 3), the number of hidden neurons (N inputs = 10), and the number of output neurons (N out puts = 3).

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Fig. 9.7 Software tool optiflow neural network (ONN) can be used to create weights

Under the topic Connectcalls you define how to connect the sensor cells and neurons against each other. The line (1 3 4 13) means that all three input cells have one connection to the hidden layer (4 to 13). And all hidden neurons (4 to 13) have a connection to the output cells (14 to 16). After loading the example file frog_example.net you can start the training of your frog brain. Important: Note that some basic knowledge of artificial neural networks is beneficial before using this component [3]. We provide our own in-house programmed tool optiflow neural network (ONN). The tool and an example parameter set with data files for the frog model can be downloaded at https://www.optiflow.htwsaar.de/. As can be seen in Fig. 9.7 you just have to press the button Start Training. The tool will start with the training for a certain number of epochs. The number of epochs can be defined in the GUI together with the random seed of our random number generator. This seed is used to ensure a reproducible simulation run.

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Fig. 9.8 Testing your neural network of the frog’s example

9.3.3 Results and Analysis After finishing the training of your frog brain you can test the neural network. As can be seen in Fig. 9.8, the neural network gives quite good results for prediction of the frog’s behavior. So we can learn that 10 hidden neurons might be pretty well to reproduce the frog’s behavior. The data files that are described in Fig. 9.5 can be written with the menu topic File/Save Weights. Weights from further simulation runs can be loaded and used for prediction.

Problems We want to have a look at some problems that we now can explore with an artificial neural network. To do this, you try the following things with our froggy. 9.1 Create a data set for the learning behavior of the frog. It should contain 100 entries of training data. Verify the results with your own test data. 9.2 Train your neural network with the frog information data set. And try to predict the behavior.

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9.3 Visualize the test cases in a diagram and compare input and output data. Discuss the result. 9.4 Try to create a mesh with 20 hidden layers and compare the results of both meshes.

References 1. Spitzer, M.: Lernen – Gehirnforschung und die Schule des Lebens. Elsevier (2007) 2. Mathematik für Wirtschaftsingenieure; Skript, Hochschule für Wirtschaft und Technik des Saarlandes (htw saar) (2020) 3. LMS Imagine.Lab Amesim Users Guide, Siemens Industry Software S.A.S (2016) 4. https://www.optiflow.htwsaar.de/ 5. https://www.mathworks.com/help/matlab/ref/rand.html

Chapter 10

Conclusions

10.1 Overview of This Textbook At the beginning of this book, we introduce the handling of the program Simcenter Amesim. It was chosen because it is easy to learn and no deep mathematical skills or programming knowledge is necessary. It can be used by students free of charge. We started with the creation of a simple pocket calculator, with which they can perform first mathematical calculations. In the individual chapters, digital twins from different, technical fields are presented and developed. The authors have chosen to divide them into mechanical, thermal, hydraulic, pneumatic, or electric twins. For example, simple models of ventilation and tank systems or a solar collector are modeled. The combination of digital twins with artificial intelligence is also presented. The combination of digital twins with artificial intelligence is also presented. In the last chapter, it is shown how a simple artificial neural network of the behavior of a frog is modeled and how this can be used to control the digital twins. In the last chapter, it is shown how a simple artificial neural network of the behavior of a frog is modeled and how this can be used to control the digital twins. A complex technical twin in which all the individual libraries have been combined into one overall model of a new innovation, the liquid piston compressor (LPC). This invention could be used, after further development, to store mechanical energy generated by wind or water turbines. The book closes with a description how to connect the digital twins with artificial intelligence, e.g. neural networks to control the twins and learn from the data. To carry out the exercises and solve the problems, the appropriate equipment is very important. Each student must have the opportunity to work independently on a computer. A desire was expressed to provide a second screen for each student. This will allow one screen to be used to work with the program and the other to follow the lecture. Switching back and forth between the program and the online meeting on only one screen makes it difficult to follow the lecture, the second screen would make this easier. Also it has to be mentioned that the program has a really good help dialog with many examples from industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. U. Rückert et al., Digital Twin Development, https://doi.org/10.1007/978-3-031-25692-9_10

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So far, exercises are given in the form of tasks from lesson to lesson, without handing out a proper task template. What is desired is a written accompaniment to the respective tasks, which have a real reference for better understanding, as well as a textbook in which some processing steps can be looked up. Another suggestion for improvement is to set aside time at the beginning of each lecture hour to discuss final papers and clarify difficulties.

10.2 What You Can Take Away The first run-through of the lecture with the program Simcenter Amesim proved to be still very expandable, since both lecturers and students were new to using it. Suggestions for improvement are to provide better equipment for students, a textbook, as well as written task descriptions and to discuss the tasks at the beginning of each lecture. Through student evaluation, the critical points can be revised and eliminated for future lectures. Thus there is a continuous learning process to improve the handling of the system. If the program Amesim can be taught in an understandable way, this serves very well as a basis for similar modeling and simulation programs. Furthermore, the students are introduced to the practical experience and overlapping topics with other lectures through the handling. They thus gain a deeper understanding in topics that are otherwise discussed purely theoretically. But what outcome can we take from this book? • You will have noticed that in this essential we have tried to introduce the program at the beginning and only briefly present the examples later on. We don’t want to try to document all examples completely, but rather to encourage you to create your own simulation model. • Our goal is to make the reader want to create digital twins of technical devices and machines himself. The aim is to train the eye for the essentials. You start with simple models and roughly estimated volumes, distances, forces, or weights. In the course of the work one goes then ever further into the detail. • The interested reader, student, engineer, or designer should be able to create and test first detailed, quantitative estimations and calculations for his new product before he starts with the complex work of designing and constructing with common CAD tools. Further, the simulation engineer can use the twin to search for boundary conditions for more complex simulations. • Digital twins may be a new phenomenon. However, drawings, sketches, and mathematical and physical equations have been created for engineering products in advance of a design for a much longer time. • The combination of digital twins together with artificial intelligence is an important step. You can learn how to train an artificial neural network with a data set and how to use this trained network to predict further results and compare them with data for testing.

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10.3 Teaching Methods and Gamification The book was written during winter semester 2020 and summer semester 2022 at the University of Applied Sciences Saarbrücken (Germany). During this time, all lectures had to be held as online classes with Microsoft Teams due to Corona crisis regulations. Students could install the program Amesim at home and use it for their homework. In addition, all exercises were tested in face-to-face summer school together with students from LAB University in Lappeenranta (Finland). We tested our textbook in two semesters with students from LAB University (Finland) in such a way that we first formed teams of two who sketched the examples one by one and solved the given problems. During these exercises they learned how to improve the models and at the end of the seminar they had to create their own complex digital twin together with a 3D visualization. The summer school lasts 1 week, during which time not all examples can be worked on. Students had to create each twin from the illustrations in the book by themselves using their own computers. During lecture, it was helpful to form collaborating teams of two or three to help each other. Initial uncertainties and difficulties hopefully subsided during the review sessions. At the end of each seminar, all team members created their own digital twins to a new, previously unknown problem and had to present them to the audience to pass the exam. Discussing and presenting the twin in front of the other ones always was a helpful and interesting situation and often resulted in new ideas and further improvements of the models. Showing animations of the digital twin in 3D environment gave a good overview and was always fun at the end of each presentation, e.g. they had to build a robot and its 3D model was moving between cones to an exit. Here all the different libraries of Amesim could be tested. To improve the teaching method, students were asked for their feedback. Further improvements of the teaching method with gamification were done in a 1-week workshop at Universidade de Aveiro (Portugal) in November 2022. Within the framework of the teaching project e-CLOSE financed by the European Union (EU), we have developed this form of seminar into a more self-motivating method [1]. We combined the tasks from the problem section of each twin with the innovative teaching method of role-playing. A role-play is a form of experiential learning described by Russell and Shepherd in 2010 [2]. Students take on assigned roles of a fictive character and act out those role through a scripted play. The method should be carried out by two teams with four persons in each team. The two teams have to work against each other. Each member of the group should take on a specific role, like an actor on stage.

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Important: Communication and new teaching methods are important. When working with the models, users and engineers should learn how to create the real applications. But always question how realistic a result is. Rely on experience, never decide based on math alone, or you will end up failing. Then the teams create a technical scenario with the digital twins and changed the parameters and constraints to produce different results for the same model. Based on the results, roles and rules need to be clearly defined in two scripts. As with the hunting scene from the petroglyphs in Bryce Canyon, the moderator with the antlers handed the two alternative scripts with the different results to the two teams and gave them the task of discussing them and trying to convince the other side. Teams could act out a specific discussion in this safe environment. Our idea was that this could be a dynamic learning experience for actors to gain knowledge about physical behavior by immersing them in simulated real-world problems. But also in industry, such an artificial dispute in a safe, moderated environment can help teams learn very quickly and focus on a technical problem. Adrenalin is provided free of charge and no additional coffee is needed.

10.4 Our Outlook for the Future For further work, it would be very interesting to combine the digital twins on one hand with 3D visualization to show the behavior in a much more practical way and to develop more 3D models for the physical twins. The second most important step has to be the combination of the digital twins with artificial intelligence or to depict the physical models to neural networks. However, it must also be said that artificial neural networks are definitely not the end of the line in artificial intelligence. Further work will focus much more on reduced-order models (ROM) that lead to more sophisticated predictions and better represent the digital twins. We also want to improve the gamification teaching method role-play and create scripts with controversy, technical tasks.

10.5 Important: Disclaimer for Our Work The information contained in this book has been obtained solely from personal experience. The information provided may not be correct, complete, or accurate. The authors assume no responsibility for losses or other related liabilities and make no claim as to the accuracy of the information contained in this book resulting from its use in any manner, or as to the infringement of any patent rights which may result. Likewise, the authors and the publisher do not warrant that the procedures and software tools described are free of third-party intellectual property rights. Simcenter Amesim is a registered trademark of Siemens Industry Software NV. The tools,

References

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Blender, ANSYS CFX, ANSYS Chemkin, ANSYS Fluent, openFOAM, and MATLAB, are also registered trademarks. The reproduction of common names, trade names, product designations, etc. in this work does not entitle the user to assume that such names are or would be considered free within the meaning of trademark protection legislation, even without special identification. Further information can be found on the Siemens PLM Software homepage [3].

References 1. https://www.ua.pt/en/news/9/69847 2. Russell, C., Shepherd, J.: Online role-play environments for higher education. Br. J. Educ. Technol., Special Issue: Learning Objects in Progress 41, 6 (2010) 3. https://www.plm.automation.siemens.com/global/en/