Topology Optimization and AI-based Design of Power Electronic and Electrical Devices: Principles and Methods provides an essential foundation in the emergent design methodology as it moves towards commercial development in such 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, the work 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, particle swarm optimization and simulated annealing, alongside representative algorithms. The work 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. The work concludes with a lengthy exposition of AI-based design methods, including surrogate models for optimization, deep neural networks, and automatic 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
1. Topology Optimization and AI-based Design of Power Electronic and Electrical Devices - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Preface - p. Page v 8. Nomenclature - p. Page x 9. List of Illustrations - p. Page xi 10. List of Tables - p. Page xii 11. 1 : Equations of electromagnetic field - p. Page xii 12. 2 : Modeling of electromagnetic systems - p. Page 22 13. 3 : Finite element method for electromagnetic field - p. Page 59 14. 4 : Numerical methods for electromagnetic field analysis - p. Page 92 15. 5 : Optimization methods - p. Page 126 16. 6 : Topology optimization - p. Page 179 17. 7 : Basics of machine learning - p. Page 235 18. 8 : Optimal design based on machine learning - p. Page 283 19. 9 : Optimal design based on deep learning - p. Page 303 20. A : Maxwell stress tensor in orthogonal curvilinear coordinates - p. Page 331 21. B : Newton–Raphson method - p. Page 334 22. C : Differential forms - p. Page 335 23. D : Mathematical properties of FE matrices - p. Page 337 24. Bibliography - p. Page 347 25. Index - p. Page 347
Topology Optimization and AI-based Design of Power Electronic and Electrical Devices: Principles and Methods provides an essential foundation in the emergent design methodology as it moves towards commercial development in such 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, the work 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, particle swarm optimization and simulated annealing, alongside representative algorithms. The work 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. The work concludes with a lengthy exposition of AI-based design methods, including surrogate models for optimization, deep neural networks, and automatic 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
1. Topology Optimization and AI-based Design of Power Electronic and Electrical Devices - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Preface - p. Page v 8. Nomenclature - p. Page x 9. List of Illustrations - p. Page xi 10. List of Tables - p. Page xii 11. 1 : Equations of electromagnetic field - p. Page xii 12. 2 : Modeling of electromagnetic systems - p. Page 22 13. 3 : Finite element method for electromagnetic field - p. Page 59 14. 4 : Numerical methods for electromagnetic field analysis - p. Page 92 15. 5 : Optimization methods - p. Page 126 16. 6 : Topology optimization - p. Page 179 17. 7 : Basics of machine learning - p. Page 235 18. 8 : Optimal design based on machine learning - p. Page 283 19. 9 : Optimal design based on deep learning - p. Page 303 20. A : Maxwell stress tensor in orthogonal curvilinear coordinates - p. Page 331 21. B : Newton–Raphson method - p. Page 334 22. C : Differential forms - p. Page 335 23. D : Mathematical properties of FE matrices - p. Page 337 24. Bibliography - p. Page 347 25. Index - p. Page 347
