图书简介
The digital twin of a physical system is an adaptive computer analog which exists in the cloud and adapts to changes in the physical system dynamically. This book introduces the computing, mathematical and engineering background to understand and develop the concept of the digital twin. It provides background in modeling/simulation, computing technology, sensor/actuators, and so forth, needed to develop the next generation of digital twins. Concepts on cloud computing, big data, IoT, wireless communications, high performance computing, and blockchain are also discussed. Features:Provides background material needed to understand digital twin technology.Presents computational facet of digital twin.Includes physics based and surrogate model representations.Addresses the problem of uncertainty in measurements and modeling.Discusses practical case studies of implementation of digital twins addressing additive manufacturing, server farms, predictive maintenance, and smart cities.This book is aimed at Graduate students and Researchers in Electrical, Mechanical, Computer, and Production Engineering.
Chapter 1 Introduction and Background1.1 Introduction1.2 Modeling and Simulation1.3 Sensors and Actuators1.4 Signal Processing1.5 Estimation Algorithms1.6 Industry 4.01.7 Applications
Chapter 2 Computing and Digital Twin2.1 Digital Twin Use cases and the Internet of things (IOT)2.2 Edge Computing2.3 Telecom and 5G2.4 Cloud2.5 Big Data2.6 Google Tensorow2.7 Blockchain and digital twin
Chapter 3 Dynamic Systems3.1 Single-degree-of-freedom undamped systems3.2 Single-degree-of-freedom viscously damped systems3.3 Multiple-degree-of-freedom undamped systems3.4 Proportionally damped systems3.5 Non-proportionally damped systems3.6 Summary
Chapter 4 Stochastic Analysis4.1 Probability theory4.2 Reliability4.3 Simulation methods in UQ and reliability4.4 Robustness
Chapter 5 Digital Twin of Dynamic Systems5.1 Dynamic model of the digital twin5.2 Digital twin via sti ness evolution5.3 Digital twin via mass evolution5.4 Digital twin via mass and sti ness evolution5.5 Discussions5.6 Summary
Chapter 6 Machine learning and Surrogate Models6.1 Analysis of Variance Decomposition6.2 Polynomial Chaos Expansion6.3 Support Vector Machines6.4 Neural Networks6.5 Gaussian Process6.6 Hybrid polynomial correlated function expansion
Chapter 7 Surrogate based digital twin of dynamic system7.1 The dynamic model of the digital twin7.2 Overview of Gaussian process emulators7.3 Gaussian process based digital twin7.4 Discussion7.5 Summary
Chapter 8 Digital Twin at Multiple Time Scales8.1 The problem statement8.2 Digital twin for multi-timescale dynamical systems8.3 Illustration of the proposed framework8.4 Summary
Chapter 9 Digital twin of nonlinear MDOF systems9.1 Physics based nominal model9.2 Bayesian ltering algorithm9.3 Supervised machine le
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