Jaroslaw Sobieszczanski-Sobieski, Alan Morris, Michel van Tooren

Multidisciplinary Design Optimization Supported by Knowledge Based Engineering (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 392
• Erscheinungstermin: 8. Mai 2017
• Englisch
• ISBN-13: 9781118897096
• Artikelnr.: 52579412

Autorenporträt

Jaroslaw Sobieszczanski-Sobieski NASA Langley Research Center, USA Alan Morris Cranfield University, UK Michel van Tooren University of South Carolina, USA

Inhaltsangabe

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 on the Use of ANN
97 References 97 6 Optimizing Multiobjective Function Problems 98 6.1
Introduction 98 6.2 Salient Features of Multiobjective Optimization 99 6.3
Selected Algorithms for Multiobjective Optimization 102 6.4 Weighted Sum
Procedure 104 6.5 epsilon-Constraint and Lexicographic Methods 108 6.6 Goal
Programming 111 6.7 Min-Max Solution 111 6.8 Compromise Solution
Equidistant to the Utopia Point 113 6.9 Genetic Algorithms and Artificial
Neural Networks Solution Methods 113 6.9.1 GAs 114 6.9.2 ANN 114 6.10 Final
Comment 115 References 115 7 Sensitivity Analysis 116 7.1 Analytical Method
116 7.1.1 Example 7.1 118 7.1.2 Example 7.2 121 7.2 Linear Governing
Equations 122 7.3 Eigenvectors and Eigenvalues Sensitivities 124 7.3.1
Buckling as an Eigen-problem 125 7.3.2 Derivatives of Eigenvalues and
Eigenvectors 125 7.3.3 Example 7.3 127 7.4 Higher Order and Directional
Derivatives 129 7.5 Adjoint Equation Algorithm 131 7.6 Derivatives of
Real-Valued Functions Obtained via Complex Numbers 133 7.7 System
Sensitivity Analysis 135 7.7.1 Example 7.4 139 7.8 Example 144 7.9 System
Sensitivity Analysis in Adjoint Formulation 145 7.10 Optimum Sensitivity
Analysis 146 7.10.1 Lagrange Multiplier lambda as a Shadow Price 149 7.11
Automatic Differentiation 150 7.12 Presenting Sensitivity as Logarithmic
Derivatives 153 References 154 8 Multidisciplinary Design Optimization
Architectures 155 8.1 Introduction 155 8.2 Consolidated Statement of a
Multidisciplinary Optimization Problem 156 8.3 The MDO Terminology and
Notation 158 8.3.1 Operands 159 8.3.2 Coupling Constraints 159 8.3.3
Operators 160 8.4 Decomposition of the Optimization Task into Subtasks 161
8.5 Structuring the Underlying Information 162 8.6 System Analysis (SA) 167
8.7 Evolving Engineering Design Process 170 8.8 Single-Level Design
Optimizations (S-LDO) 173 8.8.1 Assessment 175 8.9 The Feasible Sequential
Approach (FSA) 176 8.9.1 Implementation Options 177 8.10 Multidisciplinary
Design Optimization (MDO) Methods 178 8.10.1 Collaborative Optimization
(CO) 179 8.10.2 Bi-Level Integrated System Synthesis (BLISS) 189 8.10.3
BLISS Augmented with SM 192 8.11 Closure 199 8.11.1 Decomposition 199
8.11.2 Approximations and SM 200 8.11.3 Anatomy of a System 200 8.11.4
Interactions of the System and Its BBs 201 8.11.5 Intrinsic Limitations of
Optimization in General 202 8.11.6 Optimization across a Choice of
Different Design Concepts 202 8.11.7 Off-the-Shelf Commercial Software
Frameworks 203 References 205 9 Knowledge Based Engineering 208 9.1
Introduction 208 9.2 KBE to Support MDO 209 9.3 What is KBE 210 9.4 When
Can KBE Be Used 213 9.5 Role of KBE in the Development of Advanced MDO
Systems 214 9.6 Principles and Characteristics of KBE Systems and KBE
Languages 220 9.7 KBE Operators to Define Class and Object Hierarchies 222
9.7.1 An Example of a Product Model Definition in Four KBE Languages 226
9.8 The Rules of KBE 230 9.8.1 Logic Rules (or Conditional Expressions) 230
9.8.2 Math Rules 231 9.8.3 Geometry Manipulation Rules 232 9.8.4
Configuration Selection Rules (or Topology Rules) 234 9.8.5 Communication
Rules 235 9.8.6 Beyond Classical KBS and CAD 236 9.9 KBE Methods to Develop
MMG Applications 236 9.9.1 High-Level Primitives (HLPs) to Support
Parametric Product Modeling 237 9.9.2 Capability Modules (CMs) to Support
Analysis Preparation 238 9.10 Flexibility and Control: Dynamic Typing,
Dynamic Class Instantiation, and Object Quantification 241 9.11 Declarative
and Functional Coding Style 241 9.12 KBE Specific Features: Runtime Caching
and Dependency Tracking 243 9.13 KBE Specific Features: Demand-Driven
Evaluation 246 9.14 KBE Specific Features: Geometry Kernel Integration 247
9.14.1 How a KBE Language Interacts with a CAD Engine 248 9.15 CAD or KBE?
252 9.16 Evolution and Trends of KBE Technology 253 Acknowledgments 256
References 256 10 Uncertainty-Based Multidisciplinary Design Optimization
258 10.1 Introduction 258 10.2 Uncertainty-Based Multidisciplinary Design
Optimization (UMDO) Preliminaries 259 10.2.1 Basic Concepts 259 10.2.2
General UMDO Process 263 10.3 Uncertainty Analysis 264 10.3.1 Monte Carlo
Methods (MCS) 265 10.3.2 Taylor Series Approximation 266 10.3.3 Reliability
Analysis 268 10.3.4 Decomposition-Based Uncertainty Analysis 271 10.4
Optimization under Uncertainty 272 10.4.1 Reliability Index Approach (RIA)
and Performance Measure Approach (PMA) Methods 273 10.4.2 Single Level
Algorithms (SLA) 275 10.4.3 Approximate Reliability Constraint Conversion
Techniques 278 10.4.4 Decomposition-Based Method 280 10.5 Example 282 10.6
Conclusion 285 References 285 11 Ways and Means for Control and Reduction
of the Optimization Computational Cost and Elapsed Time 287 11.1
Introduction 287 11.2 Computational Effort 288 11.3 Reducing the Function
Nonlinearity by Introducing Intervening Variables 289 11.4 Reducing the
Number of the Design Variables 289 11.4.1 Linking by Groups 290 11.5
Reducing the Number of Constraints Directly Visible to the Optimizer 292
11.5.1 Separation of Well-Satisfied Constraints from the Ones Violated or
Nearly Violated 292 11.5.2 Representing a Set of Constraints by a Single
Constraint 293 11.5.3 Replacing Constraints by Their Envelope in the
Kreisselmeier-Steinhauser Formulation 293 11.6 Surrogate Methods (SMs) 298
11.7 Coordinated Use of High- and Low-Fidelity Mathematical Models in the
Analysis 301 11.7.1 Improving LF Analysis by Infrequent Use of HF Analysis
301 11.7.2 Reducing the Number of Quantities Being Approximated 303 11.7.3
Placement of the Trial Points xT in the Design Space x 304 11.8 Design
Space in n Dimensions May Be a Very Large Place 308 References 309 Appendix
A Implementation of KBE in an MDO System 310 Appendix B Guide to
Implementing an MDO System 349 Index 360