Multiobjective Shape Design in Electricity and Magnetism (eBook)
XVII, 313 Seiten
Springer Netherlands (Verlag)
978-90-481-3080-1 (ISBN)
Multiobjective Shape Design in Electricity and Magnetism is entirely focused on electric and magnetic field synthesis, with special emphasis on the optimal shape design of devices when conflicting objectives are to be fulfilled. Direct problems are solved by means of finite-element analysis, while evolutionary computing is used to solve multiobjective inverse problems. This approach, which is original, is coherently developed throughout the whole manuscript. The use of game theory, dynamic optimisation, and Bayesian imaging strengthens the originality of the book. Covering the development of multiobjective optimisation in the past ten years, Multiobjective Shape Design in Electricity and Magnetism is a concise, comprehensive and up-to-date introduction to this research field, which is growing in the community of electricity and magnetism. Theoretical issues are illustrated by practical examples. In particular, a test problem is solved by different methods so that, by comparison of results, advantages and limitations of the various methods are made clear.
Paolo DI BARBA graduated in Electronic Engineering (MSc) in the year 1987-1988 at the University of Pavia, Italy. He obtained the PhD degree in Electrical Engineering from the Technical University of Lodz, Poland, in the year 2001-2002. At the time being, he is a full professor of electrical engineering (tenure position) at the University of Pavia, Faculty of Engineering. He is a member of the steering committees of some international symposia in the area of computational electromagnetism, in particular: Intl Symposium on Electromagnetic Fields in Electrical Engineering (ISEF), Workshop on Optimization and Inverse Problems in Electromagnetism (OIPE).
The scientific interests of the author include the computer-aided design of electric, magnetic and electromechanical devices with special emphasis on the methodologies for multi-objective optimisation in electromagnetism. He is author of more than 100 papers, either presented to international conferences or published in international journals; relevant applications concern electrical power engineering as well as biomedical engineering.Multiobjective Shape Design in Electricity and Magnetism is entirely focused on electric and magnetic field synthesis, with special emphasis on the optimal shape design of devices when conflicting objectives are to be fulfilled. Direct problems are solved by means of finite-element analysis, while evolutionary computing is used to solve multiobjective inverse problems. This approach, which is original, is coherently developed throughout the whole manuscript. The use of game theory, dynamic optimisation, and Bayesian imaging strengthens the originality of the book. Covering the development of multiobjective optimisation in the past ten years, Multiobjective Shape Design in Electricity and Magnetism is a concise, comprehensive and up-to-date introduction to this research field, which is growing in the community of electricity and magnetism. Theoretical issues are illustrated by practical examples. In particular, a test problem is solved by different methods so that, by comparison of results, advantages and limitations of the various methods are made clear.
Paolo DI BARBA graduated in Electronic Engineering (MSc) in the year 1987-1988 at the University of Pavia, Italy. He obtained the PhD degree in Electrical Engineering from the Technical University of Lodz, Poland, in the year 2001-2002. At the time being, he is a full professor of electrical engineering (tenure position) at the University of Pavia, Faculty of Engineering. He is a member of the steering committees of some international symposia in the area of computational electromagnetism, in particular: Intl Symposium on Electromagnetic Fields in Electrical Engineering (ISEF), Workshop on Optimization and Inverse Problems in Electromagnetism (OIPE).The scientific interests of the author include the computer-aided design of electric, magnetic and electromechanical devices with special emphasis on the methodologies for multi-objective optimisation in electromagnetism. He is author of more than 100 papers, either presented to international conferences or published in international journals; relevant applications concern electrical power engineering as well as biomedical engineering.
161736_1_En_BookFrontmatter_OnlinePDF 1
161736_1_En_1_Chapter_OnlinePDF 15
Chapter Chapter 1: Introduction 15
Prologue 15
161736_1_En_2_Chapter_OnlinePDF 19
Chapter Chapter 2: Inverse Problems and Error Minimisation 19
A Copernican Revolution: Direct and Inverse Problems 19
Insidiousness of Inverse Problems 20
Classification of Inverse Problems 21
Green Formula and Fredholm Equation 22
Case Studies 23
Solving Inverse Problems by Minimising a Functional 24
Constrained Minimisation 26
Classical Optimality Conditions 26
Managing Constraints 27
Local vs Global Search 28
A Deterministic Algorithm of Lowest Order: Simplex Method 30
Evolutionary Computing 31
An Evolution Strategy of Lowest Order 32
No Free-Lunch 34
Solving Inverse Problems by Means of Rectangular Systems of Algebraic Equations 35
Least Squares 36
Singular-Value Decomposition 37
Regularization 37
References 39
161736_1_En_3_Chapter_OnlinePDF 40
Chapter Chapter 3: A Paretian Approach to MOSD Theory 40
Need of a Multiobjective Formulation 40
Multiobjective Formulation of a Design Problem 42
Paretian Optimality 43
References 53
161736_1_En_4_Chapter_OnlinePDF 54
Chapter Chapter 4: Field Models and Shape Design 54
Maxwell Equations in Differential Form 54
Wave, Diffusion and Steady-State Equations in Terms of Vectors 55
Wave, Diffusion and Steady-State Equations in Terms of Potentials 58
Boundary and Transmission Conditions 63
Insidiousness of Direct Problems 67
Field-Based Inverse Problems 69
More Insidious Difficulties 71
A Unifying View of Analysis and Synthesis 72
References 75
161736_1_En_5_Chapter_OnlinePDF 76
Chapter Chapter 5: Solving Multiobjective Optimisation Problems 76
Classical Methods of Multiobjective Optimisation 76
Objective Weighting 76
Epsilon-Constraint Formulation 78
Weighted Min-Max Formulation 79
Min-Max Formulation with Variable Goals 81
Goal-Attainment Method 82
Classical vs Paretian Formulation 83
Evolutionary Methods of Multiobjective Optimisation 84
Strength Pareto Evolutionary Algorithm (SPEA) 85
Non-Dominated Sorting Genetic Algorithm (NSGA) 85
Enhancing Diversity in the Objective Space with NSGA 88
Multi-Objective Evolution Strategy (MOESTRA) 89
MOESTRA Validation: Metrics and Convergence 92
MOESTRA Validation: Sample-and-Sort Technique 95
The Gradient-Balance (GB) Method for 2D Problems 97
Analytical Benchmarks 100
References 105
161736_1_En_6_Chapter_OnlinePDF 106
Chapter Chapter 6: A Field-Based Benchmark 106
A Twofold Meaning of Benchmarking 106
Test Problem: Shape Design of a Magnetic Pole 108
Synthesis 108
Analysis 110
The Test Problem Simplified 112
Criticism to Pareto Optimality in the Static Case 113
161736_1_En_7_Chapter_OnlinePDF 115
Chapter Chapter 7: Static MOSD 115
A Bibliographic Insight 115
Surrogate Modelling 117
Kriging-Assisted Design Optimisation 118
Topology Optimisation and Sensitivity Analysis 119
Multi-Level Design Optimisation 120
FEM-Assisted Optimal Design 120
Test Problem: A Priori Analysis of the Objective Space 121
Re-Aggregating Feasible Points in the Objective Space 125
Optimisation Strategies and Results 127
Processing Clusters 132
The Test Problem Solved by Means of the GB Method 134
Comparing Results 137
An Industrial Case Study: Permanent-Magnet Alternator 138
Design Problem 140
Analysis Problem 141
Elitist NSGA and MOESTRA in Action 143
161736_1_En_8_Chapter_OnlinePDF 148
Chapter Chapter 8: Moving Along the Pareto Front 148
John Optimality 148
Reconsidering the Industrial Case Study 151
Exploring the Pareto Front 151
Optimising Along the Front 157
References 157
161736_1_En_9_Chapter_OnlinePDF 158
Chapter Chapter 9: Sensitivity Analysis and MOSD 158
Discrete Sets and Perturbation Domains 158
Case Study: Superconducting Magnetic-Bearing Design 160
Design Optimisation of the PM-HTSC Interaction 161
An Inexpensive Evaluation of Sensitivity 162
Results 165
161736_1_En_10_Chapter_OnlinePDF 167
Chapter Chapter 10: Non-Conflicting Multiple Objectives 167
Case Study: A System for Magnetic Induction Tomography 167
Design Problem 168
Analysis Problem 170
Optimal Shape Design of the MIT Antenna 171
References 174
161736_1_En_11_Chapter_OnlinePDF 175
Chapter Chapter 11: Higher-Order Dimensionality 175
Case Study: An Electrostatic Micromotor 175
Field Analysis: Doubly-Connected Domain 176
Field Synthesis and Rotor Shape Design 179
Results 179
A Criterion for Decision Making 182
161736_1_En_12_Chapter_OnlinePDF 184
Chapter Chapter 12: Multi-Scale Evolution Strategy 184
Industrial Electromagnetic Design 184
A Multi-Scale Evolutionary Search 186
Permanent-Magnet Alternator Design 187
Results 188
References 193
161736_1_En_13_Chapter_OnlinePDF 194
Chapter Chapter 13: Game Theory and MOSD 194
From Pareto Front to Nash Equilibrium 194
Theoretical Background 194
Analytical Validation 196
Numerical Implementation 198
Case Study: Permanent-Magnet Motor Design 199
Direct and Inverse Model 199
Results 202
References 211
161736_1_En_14_Chapter_OnlinePDF 212
Chapter Chapter 14: Dynamic MOSD 212
From Static to Dynamic Conditions 212
Theoretical Background 212
An Analytical Benchmark 213
Criticism to Dynamic Pareto Optimality 214
Numerical Benchmark 216
Direct Problem 216
Design Problem 217
Auxiliary Inverse Problems 218
Identifying Maximum Field 218
Identifying Time Threshold 219
Main Inverse Problem: Synthesising Device Geometry 219
Computational Aspects 221
Towards an Algorithm for Dynamic Optimisation 222
Results I 222
The Design Problem Revisited: Recovering Steady State from Time Evolution 226
Results II 228
References 231
161736_1_En_15_Chapter_OnlinePDF 232
Chapter Chapter 15: An Introduction to Bayesian Probability Theory 232
Bayesian Conception of Probability 232
Basic Bayesian Rules 233
Bayes Theorem 237
Prior Distributions 239
Uniform Prior 239
Scale-Independent Prior 240
Bayesian Inference vs Maximum Likelihood 242
Bayesian Non-Parametric Problems 244
Model Choice 249
161736_1_En_16_Chapter_OnlinePDF 253
Chapter Chapter 16: A Bayesian Approach to Multiobjective Optimisation 253
Reasons for a New Approach 253
Weak Regularity 255
Local Bayesian Formulation 257
The Stopping Term 258
The Back-Mapping Term 259
The Front-Mapping Term 259
Integral Bayesian Formulation 260
Computation of the Bayesian Terms 264
Paretian Distance 265
The Integral Back-Mapping Term 267
The Integral Front-Mapping Term 270
Bayesian Imaging 272
Clustering 272
Cluster Probabilistic Analysis 273
Cluster Space Analysis 276
Cluster Selection 277
References 1
161736_1_En_17_Chapter_OnlinePDF 278
Chapter Chapter 17: Bayesian Imaging and Shape Design 278
Algorithmic Aspects 278
Implementation 279
Sampling 280
Details of Clustering 280
Improving Cluster Selection 281
Extracting New Samples 282
An Analytical Test Case 283
Algorithmic Cost 284
Case Study: Shape Design of a Linear Actuator 285
Magnetic Analysis of the Device 285
MOSD Problem 288
Optimisation Results 289
On the Meaning of Convergence 291
References 293
161736_1_En_18_Chapter_OnlinePDF 294
Chapter Chapter 18: Conclusion 294
161736_1_En_BookBackmatter_OnlinePDF 298
Chapter : References 298
Electronic Sources 308
: Index 309
| Erscheint lt. Verlag | 3.12.2009 |
|---|---|
| Reihe/Serie | Lecture Notes in Electrical Engineering | Lecture Notes in Electrical Engineering |
| Zusatzinfo | XVII, 313 p. |
| Verlagsort | Dordrecht |
| Sprache | englisch |
| Themenwelt | Naturwissenschaften ► Physik / Astronomie ► Elektrodynamik |
| Technik ► Elektrotechnik / Energietechnik | |
| Schlagworte | Electric And Magnetic Field Synthesis • Evolutionary Computing • Finite-element modelling of electric and magnetic devices • HTS • Magnetic field • Maxwell equation • Multiobjective Optimisation In Electricity And Magnetism • Optimal Shape Design |
| ISBN-10 | 90-481-3080-8 / 9048130808 |
| ISBN-13 | 978-90-481-3080-1 / 9789048130801 |
| Haben Sie eine Frage zum Produkt? |
PDF (Wasserzeichen)Größe: 8,2 MB
DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasserzeichen und ist damit für Sie personalisiert. Bei einer missbräuchlichen Weitergabe des eBooks an Dritte ist eine Rückverfolgung an die Quelle möglich.
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich
