Topology Optimization and AI-based Design of Power Electronic and Electrical Devices (eBook)

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.

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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