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Topology optimization and AI-based design of power electronic and electrical devices : principles and methods /

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,...

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Bibliographic Details
Main Author: Igarashi, Hajime
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: London : Academic Press, 2024.
Subjects:
Electronics > Design.
Artificial intelligence > Industrial applications.
Intelligence artificielle > Applications industrielles.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Topology Optimization and AI-based Design of Power Electronic and Electrical Devices
  • Copyright
  • Contents
  • Preface
  • Nomenclature
  • 1 Equations of electromagnetic field
  • 1.1 Maxwell equations
  • 1.2 Conservation laws
  • 1.2.1 Conservation of electric charge
  • 1.2.2 Conservation of energy
  • 1.2.3 Conservation of momentum
  • 1.3 Static fields
  • 1.3.1 Electrostatic field
  • 1.3.2 Magnetostatic field
  • 1.4 Quasistatic fields
  • 1.4.1 Magneto-quasistatic field
  • 1.4.2 Electro-quasistatic field
  • 1.4.3 Magneto- and electro-quasistatic approximations
  • 1.5 Electromagnetic waves
  • 1.5.1 Wave equation
  • 1.5.2 Displacement current in a power device
  • 1.6 Boundary conditions
  • 1.6.1 Boundary conditions on a material surface
  • 1.6.2 Electric and magnetic walls
  • 1.6.3 Periodic boundary condition
  • 1.6.4 Boundary conditions for wave propagation
  • 1.6.5 Impedance boundary conditions
  • 1.7 Summary
  • 2 Modeling of electromagnetic systems
  • 2.1 Permanent magnet (PM)
  • 2.2 Energy and force
  • 2.2.1 Magnetic field
  • 2.2.2 Electric field
  • 2.3 Inductance
  • 2.3.1 Definition of inductanace
  • 2.3.2 Differential inductance
  • 2.3.3 Effect of an air gap
  • 2.4 Skin and proximity effects
  • 2.5 Loss analysis
  • 2.5.1 Classical eddy current loss
  • 2.5.2 Hysteresis loss
  • 2.5.3 Steinmetz equation
  • 2.5.4 Steinmetz equation in time domain
  • 2.5.5 Eddy current loss considering skin effect
  • 2.5.6 Mathematical models of magnetic hysteresis
  • 2.6 Modeling of electric motors
  • 2.6.1 Circuit equation of moving object and electromagnetic forces
  • 2.6.2 Equations for PM motors
  • 2.6.3 d-q transformation
  • 2.6.4 Motor control
  • 2.6.5 Behavior model of an electric motor
  • 2.6.6 Torque component separation
  • 2.7 Summary
  • 3 Finite element method for electromagnetic field
  • 3.1 Two-dimensional analysis.
  • 3.1.1 Two-dimensional magnetostatic field
  • 3.1.2 Consideration of magnetic saturation
  • 3.1.3 Coupling with an electric circuit
  • 3.1.4 Treatment of a permanent magnet
  • 3.2 Three-dimensional analysis
  • 3.2.1 Thee-dimensional electrostatic field
  • 3.2.2 Thee-dimensional magnetostatic field
  • 3.2.3 Edge elements
  • 3.2.4 Finite element analysis with an edge element
  • 3.2.5 Compatibility
  • 3.2.6 Thee-dimensional magneto-quasistatic field
  • 3.2.6.1 Time-domain analysis
  • 3.2.6.2 Numerical stability in time-domain analysis
  • 3.2.7 Analysis of a three dimensional electro-quasistatic field
  • 3.2.8 Analysis of the three-dimensional wave equation
  • 3.3 Finite elements
  • 3.3.1 Simplex elements
  • 3.3.2 Hexahedral element
  • 3.3.3 Other finite elements
  • 3.4 Computation of electromagnetic force
  • 3.5 Summary
  • 4 Numerical methods for electromagnetic field analysis
  • 4.1 Homogenization method
  • 4.1.1 Homogenization of a laminated steel plate
  • 4.1.2 Ollendorff formula
  • 4.1.2.1 Macroscopic permeability
  • 4.1.2.2 Other homogenization methods
  • 4.1.3 Homogenization of a winding coil
  • 4.1.4 Unit cell approach
  • 4.1.4.1 Linear system
  • 4.1.4.2 Consideration of magnetic saturation
  • 4.1.5 Soft magnetic composite (SMC): Homogenization of a heterogenous material
  • 4.1.5.1 Modeling of SMC
  • 4.1.5.2 Modeling with discrete element method
  • 4.1.6 Expression using an equivalent circuit
  • 4.1.6.1 Laminated steel sheets
  • 4.1.6.2 Winding coil
  • 4.1.6.3 Equivalent circuit for electromagnetic devices
  • 4.1.6.4 Physical interpretation of a Cauer circuit
  • 4.2 Model-order reduction
  • 4.2.1 Principal component analysis
  • 4.2.2 Proper orthogonal decomposition (POD)
  • 4.2.3 Equivalent circuit obtained via PVL method
  • 4.2.3.1 Formulation of PVL method
  • 4.2.3.2 Synthesis of equivalent circuits
  • 4.2.4 Direct synthesis of a Cauer circuit.
  • 4.2.4.1 CVL method
  • 4.2.4.2 Simple example
  • 4.3 Summary
  • 5 Optimization methods
  • 5.1 Introduction
  • 5.2 Basics of deterministic methods
  • 5.2.1 Mathematical properties
  • 5.2.2 Steepest descent method
  • 5.2.3 Adjoint variable method
  • 5.3 Method of Lagrange multiplier
  • 5.3.1 Equality-constrained minimization problem
  • 5.3.2 Inequality-constrained minimization problem
  • 5.3.3 Augmented Lagrangian method
  • 5.3.4 Numerical example
  • 5.4 Method of moving asymptotes
  • 5.4.1 Principle and method
  • 5.4.2 Simple example
  • 5.5 Genetic algorithm
  • 5.5.1 Principle and method
  • 5.5.1.1 Design of chromosome
  • 5.5.1.2 Algorithm
  • 5.5.1.3 Building block hypothesis
  • 5.5.2 Real-coded genetic algorithm
  • 5.5.3 Real-coded ensemble crossover
  • 5.5.4 Micro-genetic algorithm
  • 5.5.5 Robust genetic algorithm
  • 5.5.6 Consideration of constraints
  • 5.5.7 Numerical examples
  • 5.6 Covariance Matrix Adaptation Evolution Strategy: CMA-ES
  • 5.6.1 Normal distribution
  • 5.6.2 Geometry of Gaussian function
  • 5.6.3 Principle and method of CMA-ES
  • 5.6.4 Treatment of constraints in CMA-ES
  • 5.6.5 Numerical example 1: Optimization of magnetization distribution
  • 5.6.6 Numerical example 2: Comparison of GA and CMA-ES
  • 5.7 Genetic algorithm for multi-objective optimization
  • 5.7.1 Principle and method
  • 5.7.2 Non-dominated sorting genetic algorithm: NS-GAII
  • 5.7.3 Treatment of constraints
  • 5.7.4 Numerical example
  • 5.8 Simulated annealing
  • 5.8.1 Principle and method
  • 5.8.2 Quantum and emulated quantum annealing
  • 5.9 Summary
  • 6 Topology optimization
  • 6.1 Introduction
  • 6.1.1 Features of parameter and topology optimization
  • 6.1.2 Comparison of PO with TO
  • 6.2 Topology optimization (TO) methods
  • 6.2.1 Overview
  • 6.2.2 Density method
  • 6.2.3 Level-set method
  • 6.2.4 Naive ON-OFF method.
  • 6.2.5 Numerical example of naive ON-OFF method
  • 6.2.6 Hybridization of ON-OFF and level-set methods
  • 6.3 TO based on Gaussian basis functions
  • 6.3.1 Principle and methods
  • 6.3.2 Numerical example: PM motor
  • 6.3.2.1 Single-objective optimization
  • 6.3.2.2 Multi-objective optimization
  • 6.3.3 Numerical example: experimental validation for PM motor model
  • 6.3.4 Numerical example: wireless power transfer
  • 6.3.5 Numerical example: wireless power transfer considering eddy currents
  • 6.3.6 Numerical example: microstrip lines
  • 6.4 Advanced TO using Gaussian basis functions
  • 6.4.1 Consideration of a motor-control system
  • 6.4.2 Consideration of mechanical strength
  • 6.4.3 Hybridization of TO and PO
  • 6.4.4 Multi-material optimization
  • 6.4.5 2.5D topology optimization
  • 6.5 Discussions
  • 6.5.1 Comparison of topology optimization methods
  • 6.5.2 Challenging problems in topology optimization
  • 6.6 Summary
  • 7 Basics of machine learning
  • 7.1 Introduction
  • 7.2 What is a surrogate model
  • 7.3 When surrogate models are effective
  • 7.4 Offline and online surrogate models
  • 7.4.1 Curse of dimensionality
  • 7.4.2 Determination of hyper-parameters in a surrogate model
  • 7.4.3 Sampling
  • 7.5 Least squares method
  • 7.6 Minimum norm solution and generalized inverse matrix
  • 7.7 Method of maximum likelihood
  • 7.7.1 Application to least squares method
  • 7.7.2 Application to classification
  • 7.8 Response surface methods
  • 7.9 Neural networks
  • 7.9.1 Learning based on steepest descent method
  • 7.9.2 Back propagation
  • 7.9.3 Error functions for NN
  • 7.9.4 Classification using NN
  • 7.10 Regression tree
  • 7.10.1 Principle and method
  • 7.10.2 Tree-based methods
  • 7.11 Numerical examples 1: optimal design using neural network
  • 7.12 Numerical examples 2: comparison of surrogate models
  • 7.13 Bayesian optimization.