Numerical Simulation-based Design: Theory and Methods

✍ Scribed by Xu Han, Jie Liu

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 262
• Category: Library

✦ Synopsis

This book focuses on numerical simulation-based design theory and methods in
mechanical engineering. The simulation-based design of mechanical equipmentinvolves considerable scientific challenges including extremely complex systems,extreme working conditions, multi-source uncertainties, multi-physics coupling, andlarge-scale computation. In order to overcome these technical difficulties, this booksystematically elaborates upon the advanced design methods, covering high-fidelitysimulation modeling, rapid structural analysis, multi-objective design optimization,uncertainty analysis and optimization, which can effectively improve the designaccuracy, efficiency, multi-functionality and reliability of complicated mechanicalstructures.
This book is primarily intended for researchers, engineers and postgraduate studentsin mechanical engineering, especially in mechanical design, numerical simulation andengineering optimization.

✦ Table of Contents

Preface
Contents
1 Introduction
1.1 Background and Significance
1.2 Key Scientific Issues and Technical Challenges
1.3 State-of-the-Art
1.3.1 Theory and Methods for High-Fidelity Numerical Modeling
1.3.2 Theory and Methods for Rapid Structural Analysis for Complex Equipment
1.3.3 Theory and Methods for Efficient Structural Optimization Design
1.3.4 Theory and Methods for Uncertainty Analysis and Reliability Design
1.4 Contents of This Book
References
2 Introduction to High-Fidelity Numerical Simulation Modeling Methods
2.1 Engineering Background and Significance
2.2 Modeling Based on Computational Inverse Techniques
References
3 Computational Inverse Techniques
3.1 Introduction
3.2 Sensitivity Analysis Methods
3.2.1 Local and Global Sensitivity Analysis
3.2.2 Direct Integral-Based GAS Method
3.2.3 Numerical Examples
3.2.4 Engineering Application: Global Sensitivity Analysis of Vehicle Roof Structure
3.3 Regularization Methods for Ill-Posed Problem
3.3.1 Ill-Posedness Analysis
3.3.2 Regularization Methods
3.3.3 Selection of Regularization Parameter
3.3.4 Application of Regularization Method to Model Parameter Identification
3.4 Computational Inverse Algorithms
3.4.1 Gradient Iteration-Based Computational Inverse Algorithm
3.4.2 Intelligent Evolutionary-Based Computational Inverse Algorithm
3.4.3 Hybrid Inverse Algorithm
3.5 Conclusions
References
4 Computational Inverse for Modeling Parameters
4.1 Introduction
4.2 Identification of Model Characteristic Parameters
4.2.1 Material Parameter Identification for Stamping Plate
4.2.2 Dynamic Constitutive Parameter Identification for Concrete Material
4.3 Identification of Model Environment Parameters
4.3.1 Dynamic Load Identification for Cylinder Structure
4.3.2 Vehicle Crash Condition Identification
4.4 Conclusions
References
5 Introduction to Rapid Structural Analysis
5.1 Engineering Background and Significance
5.2 Surrogate Model Methods
5.3 Model Order Reduction Methods
References
6 Rapid Structural Analysis Based on Surrogate Models
6.1 Introduction
6.2 Polynomial Response Surface Based on Structural Selection Technique
6.2.1 Polynomial Structure Selection Based on Error Reduction Ratio
6.2.2 Numerical Example
6.2.3 Engineering Application: Nonlinear Output Force Modeling for Hydro-Pneumatic Suspension
6.3 Surrogate Model Based on Adaptive Radial Basis Function
6.3.1 Selection of Sample and Testing Points
6.3.2 Optimization of the Shape Parameters
6.3.3 RBF Model Updating Procedure
6.3.4 Numerical Examples
6.3.5 Engineering Application: Surrogate Model Construction for Crash Worthiness of Thin-Walled Beam Structure
6.4 High Dimensional Model Representation
6.4.1 Improved HDMR
6.4.2 Analysis of Calculation Efficiency
6.4.3 Numerical Example
6.5 Conclusions
References
7 Rapid Structural Analysis Based on Reduced Basis Method
7.1 Introduction
7.2 The RBM for Rapid Analysis of Structural Static Responses
7.2.1 The Flow of Rapid Calculation Based on RBM
7.2.2 Construction of the Reduced Basis Space
7.2.3 Engineering Application: Rapid Analysis of Cab Structure
7.3 The RBM for Rapid Analysis of Structural Dynamic Responses
7.3.1 Parameterized Description of Structural Dynamics
7.3.2 Construction of the Reduced Basis Space Based on Time Domain Integration
7.3.3 Projection Reduction Based on Least Squares
7.3.4 Numerical Example
7.4 Conclusions
References
8 Introduction to Multi-objective Optimization Design
8.1 Characteristics of Multi-objective Optimization
8.2 Optimal Solution Set in Multi-objective Optimization
8.3 Multi-objective Optimization Methods
8.3.1 Preference-Based Methods
8.3.2 Generating Methods Based on Evolutionary Algorithms
References
9 Micro Multi-objective Genetic Algorithm
9.1 Introduction
9.2 Procedure of μMOGA
9.3 Implementation Techniques of μMOGA
9.3.1 Non-dominated Sorting
9.3.2 Population Diversity Preservation Strategies
9.3.3 Elite Individual Preserving Mechanism
9.4 Algorithm Performance Evaluation
9.4.1 Numerical Examples
9.4.2 Engineering Testing Example
9.5 Engineering Applications
9.5.1 Optimization Design of Guide Mechanism of Vehicle Suspension
9.5.2 Optimization Design of Variable Blank Holder Force in Sheet Metal Forming
9.6 Conclusions
References
10 Multi-objective Optimization Design Based on Surrogate Models
10.1 Introduction
10.2 Multi-objective Optimization Algorithm Based on Intelligent Sampling
10.2.1 Intelligent Sampling Technology
10.2.2 Convergence Criteria
10.2.3 Procedure of IS-μMOGA
10.2.4 Performance Tests
10.2.5 Engineering Application: Multi-objective Optimization Design of Commercial Vehicle Cab Structure
10.3 Multi-objective Optimization Algorithm Based on Sequential Surrogate Model
10.3.1 Multi-objective Trust Region Model Management
10.3.2 Sample Inheriting Strategy
10.3.3 Computational Procedure
10.3.4 Performance Test
10.3.5 Engineering Application: Multi-objective Optimization Design of the Door Structure of a Minibus
10.4 Conclusions
References
11 Introduction to Uncertain Optimization Design
11.1 Stochastic Programming and Fuzzy Programming
11.2 Interval Optimization
References
12 Uncertain Optimization Design Based on Interval Structure Analysis
12.1 Introduction
12.2 The General Form of Nonlinear Interval Optimization
12.3 Interval Optimization Model
12.3.1 Interval Order Relation and Transformation of Uncertain Objective Function
12.3.2 Interval Possibility Degree and Transformation of Uncertain Constraints
12.3.3 Deterministic Optimization
12.4 Interval Structure Analysis Method
12.5 Nonlinear Interval Optimization Algorithm Based on Interval Structure Analysis
12.6 Engineering Applications
12.6.1 Uncertain Optimization Design of Vehicle Frame Structure
12.6.2 Uncertain Optimization Design of Occupant Restraint System
12.7 Conclusions
References
13 Interval Optimization Design Based on Surrogate Models
13.1 Introduction
13.2 Interval Optimization Algorithm Based on Surrogate Model Management Strategy
13.2.1 Approximate Modeling for Uncertain Optimization
13.2.2 Design Space Updating
13.2.3 Calculation of the Actual Penalty Function
13.2.4 Algorithm Flow
13.2.5 Engineering Application: Uncertain Optimization for Grinder Spindle
13.3 Interval Optimization Algorithm with Local-Densifying Surrogate Model
13.3.1 Approximate Uncertain Optimization Modeling
13.3.2 Algorithm Flow
13.3.3 Engineering Application: Crashworthiness Design on a Thin-Walled Beam of a Vehicle Body
13.4 Conclusions
References