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.
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目录
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 Intelligent Sampling 182 10.2.1 Intelligent Sampling Technology 182 10.2.2 Convergence Criteria 184 10.2.3 Procedure of IS-uMOGA 185 10.2.4 Performance Tests 187 10.2.5 Engineering Application: Multi-objective Optimization Design of Commercial Vehicle Cab Structure 192 10.3 Multi-objective Optimization Algorithm Based on Sequential Surrogate Model 197 10.3.1 Multi-objective Trust Region Model Management 198 10.3.2 Sample Inheriting Strategy 200 10.3.3 Computational Procedure 201 10.3.4 Performance Test 204 10.3.5 Engineering Application: Multi-objective Optimization Design of the Door Structure of a Minibus 207 10.4 Conclusions 212 References 213 11 Introduction to Uncertain Optimization Design 215 11.1 Stochastic Programming and Fuzzy Programming 215 11.2 Interval Optimization 217 References 219 12 Uncertain Optimization Design Based on Interval Structure Analysis 221 12.1 Introduction 221 12.2 The General Form of Nonlinear Interval Optimization 221 12.3 Interval Optimization Model 223 12.3.1 Interval Order Rclation and Transformation of Uncertain Objective Function 223 12.3.2 Interval Possibility Degree and Transformation of Uncertain Constraints 225 12.3.3 Deterministic Optimization 229 12.4 Interval Structure Analysis Method 23012.5 Nonlinear Interval Optimization Algorithm Based on Interval StructureAnalysis 233 12.6 Engineering Applications 235 12.6.1 Uncertain Optimization Design of Vehicle Frame Structure 235 12.6.2 Uncertain Optimization Design of Occupant Restraint System 238 12.7 Conclusions 241 References 241 13 Interval Optimization Design Based on Surrogate Models 243 13.1 Introduction 24313.2 Interval Optimization Algorithm Based on Surrogate Model Management Strategy 243 13.2.1 Approximate Modeling for Uncertain Optimization 244 13.2.2 Design Space Updating 245 13.2.3 Calculation of the Actual Penalty Function 246 13.2.4 Algorithm Flow 248 13.2.5 Engineering Application: Uncertain Optimization for Grinder Spindlc 249 13.3 Interval Optimization Algorithm with Local-Densifying Surrogatc Model 251 13.3.1 Approximate Uncertain Optimization Modeling 252 13.3.2 Algorithm Flow 253 13.3.3 Engineering Application: Crashworthiness Design on a Thin-Walled Beam of a Vehicle Body 254 13.4 Conclusions 258 References 258
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