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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| Formato: | livro eletrônico |
| Idioma: | inglês |
| Publicado em: |
London :
Academic Press,
2024.
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| Acesso em linha: | ScienceDirect |
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| 100 | 1 | |a Igarashi, Hajime. | |
| 245 | 1 | 0 | |a Topology optimization and AI-based design of power electronic and electrical devices : |b principles and methods / |c Hajime Igarashi. |
| 260 | |a London : |b Academic Press, |c 2024. | ||
| 300 | |a 1 online resource | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Electronics |x Design. | |
| 650 | 0 | |a Artificial intelligence |x Industrial applications. | |
| 650 | 6 | |a Intelligence artificielle |0 (CaQQLa)201-0008626 |x Applications industrielles. |0 (CaQQLa)201-0374039 | |
| 650 | 7 | |a Artificial intelligence |x Industrial applications |2 fast |0 (OCoLC)fst00817262 | |
| 776 | 0 | 8 | |i Print version: |z 0323991661 |z 9780323991667 |w (OCoLC)1389879156 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780323991667 |