Buch, Englisch, 400 Seiten, Format (B × H): 173 mm x 246 mm, Gewicht: 794 g
ISBN: 978-1-118-49212-3
Verlag: Wiley
Multidisciplinary Design Optimization supported by Knowledge Based Engineering supports engineers confronting this daunting and new design paradigm. It describes methodology for conducting a system design in a systematic and rigorous manner that supports human creativity to optimize the design objective(s) subject to constraints and uncertainties. The material presented builds on decades of experience in Multidisciplinary Design Optimization (MDO) methods, progress in concurrent computing, and Knowledge Based Engineering (KBE) tools.
Key features:
- Comprehensively covers MDO and is the only book to directly link this with KBE methods
- Provides a pathway through basic optimization methods to MDO methods
- Directly links design optimization methods to the massively concurrent computing technology
- Emphasizes real world engineering design practice in the application of optimization methods
Multidisciplinary Design Optimization supported by Knowledge Based Engineering is a one-stop-shop guide to the state-of-the-art tools in the MDO and KBE disciplines for systems design engineers and managers. Graduate or post-graduate students can use it to support their design courses, and researchers or developers of computer-aided design methods will find it useful as a wide-ranging reference.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
- Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Konstruktionslehre, Bauelemente, CAD
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
Weitere Infos & Material
Preface xiii
Acknowledgment xv
Styles for Equations xvi
1 Introduction 1
1.1 Background 1
1.2 Aim of the Book 3
1.3 The Engineer in the Loop 3
1.4 Chapter Contents 4
1.4.1 Chapter 2: Modern Design and Optimization 4
1.4.2 Chapter 3: Searching the Constrained Design Space 4
1.4.3 Chapter 4: Direct Search Methods for Locating the Optimum of a Design Problem with a Single-Objective Function 5
1.4.4 Chapter 5: Guided Random Search and Network Techniques 5
1.4.5 Chapter 6: Optimizing Multiple-Objective Function Problems 6
1.4.6 Chapter 7: Sensitivity Analysis 6
1.4.7 Chapter 8: Multidisciplinary Design and Optimization Methods 7
1.4.8 Chapter 9: KBE 7
1.4.9 Chapter 10: Uncertainty-Based Multidisciplinary Design and Optimization 8
1.4.10 Chapter 11: Ways and Means for Control and Reduction of the Optimization Computational Cost and Elapsed Time 8
1.4.11 Appendix A: Implementation of KBE in Your MDO Case 9
1.4.12 Appendix B: Guide to Implementing an MDO System 9
2 Modern Design and Optimization 10
2.1 Background to Chapter 10
2.2 Nature and Realities of Modern Design 11
2.3 Modern Design and Optimization 12
2.3.1 Overview of the Design Process 13
2.3.2 Abstracting Design into a Mathematical Model 15
2.3.3 Mono-optimization 17
2.4 Migrating Optimization to Modern Design: The Role of MDO 20
2.4.1 Example of an Engineering System Optimization Problem 21
2.4.2 General Conclusions from the Wing Example 24
2.5 MDO’s Relation to Software Tool Requirements 25
2.5.1 Knowledge-Based Engineering 26
References 26
3 Constrained Design Space Search 27
3.1 Introduction 27
3.2 Defining the Optimization Problem 29
3.3 Characterization of the Optimizing Point 32
3.3.1 Curvature Constrained Problem 32
3.3.2 Vertex Constrained Problem 34
3.3.3 A Curvature and Vertex Constrained Problem 36
3.3.4 The Kuhn–Tucker Conditions 37
3.4 The Lagrangian and Duality 39
3.4.1 The Lagrangian 40
3.4.2 The Dual Problem 41
Appendix 3.A 44
References 46
4 Direct Search Methods for Locating the Optimum of a Design Problem with a Single-Objective Function 47
4.1 Introduction 47
4.2 The Fundamental Algorithm 48
4.3 Preliminary Considerations 49
4.3.1 Line Searches 50
4.3.2 Polynomial Searches 50
4.3.3 Discrete Point Line Search 51
4.3.4 Active Set Strategy and Constraint Satisfaction 53
4.4 Unconstrained Search Algorithms 54
4.4.1 Unconstrained First-Order Algorithm or Steepest Descent 55
4.4.2 Unconstrained Quadratic Search Method Employing Newton Steps 56
4.4.3 Variable Metric Search Methods 58
4.5 Sequential Unconstrained Minimization Techniques 59
4.5.1 Penalty Methods 60
4.5.2 Augmented Lagrangian Method 64
4.5.3 Simple Comparison and Comment on SUMT 64
4.5.4 Illustrative Examples 66
4.6 Constrained Algorithms 68
4.6.1 Constrained Steepest Descent Method 70
4.6.2 Linear Objective Function with Nonlinear Constraints 74
4.6.3 Sequential Quadratic Updating Using a Newton Step 78
4.7 Final Thoughts 79
References 79
5 Guided Random Search and Network Techniques 80
5.1 Guided Random Search Techniques (GRST) 80
5.1.1 Genetic Algorithms (GA) 81
5.1.2 Design Point Data Structure 81
5.1.3 Fitness Function 82
5.1.4 Constraints 87
5.1.5 Hybrid Algorithms 87
5.1.6 Considerations When Using a GA 87
5.1.7 Alternative to Genetic-Inspired Creation of Children 88
5.1.8 Alternatives to GA 88
5.1.9 Closing Remarks for GA 89
5.2 Artificial Neural Networks (ANN) 89
5.2.1 Neurons and Weights 91
5.2.2 Training via Gradient Calculation and Back-Propagation 93
5.2.3 Considerations
