基于数值模拟的设计理论与方法（英文版）

　　This book systematically introduces readers to advanced design theory and methods， including precise modeling based on inverse techniques， rapid structure computation， optimization design， and uncertainty analysis. It describes mechanical design theory， focusing on the key common technologies of simulation-based mechanical design.

暂缺《基于数值模拟的设计理论与方法（英文版）》作者简介

Contents
1 Introduction 1
1.1 Background and Significance 1
1.2 Key Scientific Issues and Technical Challenges 4
1.3 State-of-the-Art 7
1.3.1 Theory and Methods for High-Fidelity Numerical Modeling 7
1.3.2 Theory and Methods for Rapid Structural Analysis for Complex Equipment 9
1.3.3 Theory and Methods for Efficient Structural Optimization Design 10
1.3.4 Theory and Methods for Uncertainty Analysis and Reliability Design 11
1.4 Contents of This Book 12
References 14
2 Introduction to High-Fidelity Numerical Simulation Modeling Methods 17
2.1 Engineering Background and Significance 17
2.2 Modeling Based on Computational Inverse Techniques 20
References 26
3 Computational Inverse Techniques 29
3.1 Introduction 29
3.2 Sensitivity Analysis Methods 31
3.2.1 Local and Global Sensitivity Analysis 31
3.2.2 Direct Integral-Based GSA Method 32
3.2.3 Numerical Examples 37
3.2.4 Engineering Application: Global Sensitivity Analysis of Vehicle Roof Structure 38
3.3 Regularization Methods for Dl-Posed Problem 41
3.3.1 III-Posedness Analysis 41
3.3.2 Regularization Methods 42
3.3.3 Selection of Regularization Parameter 47
3.3.4 Application of Regularization Method to Model Parameter Identification 50
3.4 Computational Inverse Algorithms 53
3.4.1 Gradicnt Itcration-Bascd Computational Inverse Algorithm 55
3.4.2 Intelligent Evolutionary-Based Computational Inverse Algorithm 59
3.4.3 Hybrid Inverse Algorithm 61
3.5 Conclusions 63
Rcfcrenccs 64
4 Computational Inverse for Modleling Parameters 67
4.1 Introduction 67
4.2 Identification of Model Characteristic Parameters 68
4.2.1 Material Parameter ldentification for Stamping Plate 68
4.2.2 Dynamic Constitutive Parameter Identification for Concretc Matcrial 72
4.3 Identification of Model Environment Parameters 79
4.3.1 Dynamic Load Identification for Cylinder Structure 79
4.3.2 vehicle Crash Condition Identification 82
4.4 Conclusions 85
References 86
5 Introduction to Rapid Structural Analysis 89
5.1 Engineering Background and Significance 89
5.2 surrogate Model Methods 90
5.3 Model Order Reduction Methods 93
References 94
6 Rapid Structural Analysis Based on Surrogate Models 97
6.1 Introduction 97
6.2 Polynomial Response Surface Based on Structural selection Technique 98
6.2.1 Polynomial Structure Selection Based on Error Reduction Ratio 98
6.2.2 Numerical Example 100
6.2.3 Engineering Application: Nonlincar Output Force Modeling for Hydro-Pneumatic Suspension 101
6.3 Surrogate Model Based on Adaptive Radial Basis Function 105
6.3.1 Selection of Sample and Testing Points 106
6.3.2 Optimization of the Shape Parameters 108
6.3.3 RBF Model Updating Procedure 108
6.3.4 Numerical Examples 110
6.3.5 Engineering Application: Surrogate Model Construction for Crash Worthiness of Thin-Walled Beam Structure 112
6.4 High Dimensional Model Representation 115
6.4.1 Improved HDMR 116
6.4.2 Analysis of Calculation Efficiency 119
6.4.3 Numerical Example 120
6.5 Conclusions 122
References 123
7 Rapid Structural Analysis Based on Reduced Basis Method 125
7.1 Introduction 125
7.2 The RBM for Rapid Analysis of Structural Static Responses 126
7.2.1 The Flow of Rapid Calculation Based on RBM 126
7.2.2 Construction of the Reduced Basis Space 129
7.2.3 Engineering Application: Rapid Analysis of Cab Structure 130
7.3 The RBM for Rapid Analysis of Structural Dynamic Responses 132
7.3.1 Parameterized Description of Structural Dynamics 132
7.3.2 Construction of the Reduced Basis Space Based on Time Domain Integration 133
7.3.3 Projection Reduction Based on Least Squares 135
7.3.4 Numerical Example 136
7.4 Conclusions 138
References 140
8 Introduction to Multi-objective Optimization Design 141
8.1 Characteristics of Multi-objective Optimization 141
8.2 Optimal Solution Set in Multi-objective Optimization 143
8.3 Multi-objective Optimization Methods 144
8.3.1 Preference-Based Methods 144
8.3.2 Generating Methods Based on Evolutionary Algorithms 146
References 150
9 Micro Multi-objective Genetic Algorithm 153
9.1 Introduction 153
9.2 Procedure of uMOGA 154
9.3 Implementation Techniques of uMOGA 156
9.3.1 Non-dominated Sorting 156
9.3.2 Population Diversity Preservation Strategies 158
9.3.3 Elite Individual Preserving Mechanism 159
9.4 Algorithm Performance Evaluation 160
9.4.1 Numerical Examples 160
9.4.2 Engineering Testing Example 167
9.5 Engineering Applications 169
9.5.1 Optimization Design of Guide Mechanism of Vehicle Suspension 169
9.5.2 Optimization Design of Variable Blank Holder Force in Sheet Metal Forming 174
9.6 Conclusions 177
References 177
10 Multi-objective Optimization Design Based on Surrogate Models 179
10.1 Introduction 179
10.2 Multi-objective Optimization Algorithm Based on Intellige