Topology Optimization and AI-based Design of Power Electronic and Electrical Devices
Principles and Methods
- 1st Edition - January 15, 2024
- Author: Hajime Igarashi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 1 6 6 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 6 7 5 - 4
Topology optimization and AI-based design of power electronic and electrical devices provides an essential foundation in the emergent design methodology as it moves toward co… Read more
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Request a sales quoteTopology optimization and AI-based design of power electronic and electrical devices provides an essential foundation in the emergent design methodology as it moves toward commercial development, including electrical devices as traction motors for electric motors, transformers, inductors, reactors, and power electronics circuits.
Opening with an introduction to electromagnetism and computational electromagnetics for optimal design, this book outlines principles and foundations in finite element methods and illustrates numerical techniques useful for finite element analysis. It summarizes the foundations of deterministic and stochastic optimization methods, including genetic algorithm, CMA-ES, and simulated annealing for quantum and quantum-inspired optimization, alongside representative algorithms. The book goes on to discuss parameter optimization and topology optimization of electrical devices alongside current implementations including magnetic shields, 2D and 3D models of electric motors, and wireless power transfer devices. Finally, it concludes with a lengthy exposition of AI-based design methods, including surrogate models for optimization, Bayesian optimization, direct inverse modeling, deep neural networks, explainable AI, variational autoencoder, and integrated design methods using Monte Carlo tree searches for electrical devices and circuits.
Assists researchers and design engineers in applying emergent topology design optimization to power electronics and electrical device design, supported by step-by-step methods, heuristic derivation, and pseudocodes
Proposes unique formulations of AI-based design for electrical devices using Monte Carlo tree search and other machine learning methods
Is richly accompanied by detailed numerical examples and repletes with computational support materials in algorithms and explanatory formulae
Includes access to pedagogical videos on topics including the evolutionary process of topology optimization, the distribution of genetic algorithms, and CMA-ES
Early career researchers and students studying computer-aided engineering for power electronics and electrical device applications, electromagnetic field analysis, optimal design of electric devices, and design of electronic and electrical applications. Engineers who design electrical machines such as motors, transformers, inductors, wireless power transfer systems as well as electric circuits for power electronics and other systems in automotive and electric industries. Design engineers in automotive industries seeking effective motor design methods
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Nomenclature
- 1: Equations of electromagnetic field
- Abstract
- 1.1. Maxwell equations
- 1.2. Conservation laws
- 1.3. Static fields
- 1.4. Quasistatic fields
- 1.5. Electromagnetic waves
- 1.6. Boundary conditions
- 1.7. Summary
- Bibliography
- 2: Modeling of electromagnetic systems
- Abstract
- 2.1. Permanent magnet (PM)
- 2.2. Energy and force
- 2.3. Inductance
- 2.4. Skin and proximity effects
- 2.5. Loss analysis
- 2.6. Modeling of electric motors
- 2.7. Summary
- Bibliography
- 3: Finite element method for electromagnetic field
- Abstract
- 3.1. Two-dimensional analysis
- 3.2. Three-dimensional analysis
- 3.3. Finite elements
- 3.4. Computation of electromagnetic force
- 3.5. Summary
- Bibliography
- 4: Numerical methods for electromagnetic field analysis
- Abstract
- 4.1. Homogenization method
- 4.2. Model-order reduction
- 4.3. Summary
- Bibliography
- 5: Optimization methods
- Abstract
- 5.1. Introduction
- 5.2. Basics of deterministic methods
- 5.3. Method of Lagrange multiplier
- 5.4. Method of moving asymptotes
- 5.5. Genetic algorithm
- 5.6. Covariance Matrix Adaptation Evolution Strategy: CMA-ES
- 5.7. Genetic algorithm for multi-objective optimization
- 5.8. Simulated annealing
- 5.9. Summary
- Bibliography
- 6: Topology optimization
- Abstract
- 6.1. Introduction
- 6.2. Topology optimization (TO) methods
- 6.3. TO based on Gaussian basis functions
- 6.4. Advanced TO using Gaussian basis functions
- 6.5. Discussions
- 6.6. Summary
- Bibliography
- 7: Basics of machine learning
- Abstract
- 7.1. Introduction
- 7.2. What is a surrogate model
- 7.3. When surrogate models are effective
- 7.4. Offline and online surrogate models
- 7.5. Least squares method
- 7.6. Minimum norm solution and generalized inverse matrix
- 7.7. Method of maximum likelihood
- 7.8. Response surface methods
- 7.9. Neural networks
- 7.10. Regression tree
- 7.11. Numerical examples 1: optimal design using neural network
- 7.12. Numerical examples 2: comparison of surrogate models
- 7.13. Bayesian optimization
- 7.14. Summary
- Bibliography
- 8: Optimal design based on machine learning
- Abstract
- 8.1. Direct inverse modeling
- 8.2. Adaptive surrogate model
- 8.3. Integrated design using Monte Carlo tree search
- 8.4. Summary
- Bibliography
- 9: Optimal design based on deep learning
- Abstract
- 9.1. Introduction
- 9.2. Convolutional neural network (CNN)
- 9.3. Fast optimization using deep learning
- 9.4. Regression based on material and magnetic field distributions
- 9.5. Regression of torque and flux functions
- 9.6. Explainable AI
- 9.7. Variational autoencoder
- 9.8. Summary
- Bibliography
- A: Maxwell stress tensor in orthogonal curvilinear coordinates
- B: Newton–Raphson method
- C: Differential forms
- Bibliography
- D: Mathematical properties of FE matrices
- D.1. Gradient and rotation matrices
- D.2. Condition required for interpolation functions
- D.3. A method and A-φ method
- Bibliography
- Bibliography
- Bibliography
- Index
- No. of pages: 382
- Language: English
- Edition: 1
- Published: January 15, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780323991667
- eBook ISBN: 9780323996754
HI
Hajime Igarashi
Hajime Igarashi has been a Professor at the Graduate School of Information Science and Technology, Hokkaido University (Japan), since 2004. He was a guest researcher at the Technical University of Berlin from 1995 to 1997. His research areas are computational electromagnetism, design optimization, and energy harvesting. Igarashi is a vice president of the International COMPUMAG Society, a fellow of Institute of Electrical Engineers in Japan (IEEJ), a member of the Institute of Electronics, Information and Communication Engineers (IEICE), and a member of IEEE. He was the recipient of the Ministry of Education, Culture, Sports, Science and Technology Award and the Outstanding Technical Paper Award by IEEJ in 2016. He has authored or coauthored more than 300 international journal papers.
Affiliations and expertise
Professor, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, JapanRead Topology Optimization and AI-based Design of Power Electronic and Electrical Devices on ScienceDirect