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Multidisciplinary Design Optimization Supported by Knowledge Based Engineering

Book Description

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.

Table of Contents

  1. Cover
  2. Title page
  3. Preface
  4. Acknowledgment
  5. Styles for Equations
  6. 1 Introduction
    1. 1.1 Background
    2. 1.2 Aim of the Book
    3. 1.3 The Engineer in the Loop
    4. 1.4 Chapter Contents
  7. 2 Modern Design and Optimization
    1. 2.1 Background to Chapter
    2. 2.2 Nature and Realities of Modern Design
    3. 2.3 Modern Design and Optimization
    4. 2.4 Migrating Optimization to Modern Design: The Role of MDO
    5. 2.5 MDO’s Relation to Software Tool Requirements
    6. References
  8. 3 Constrained Design Space Search
    1. 3.1 Introduction
    2. 3.2 Defining the Optimization Problem
    3. 3.3 Characterization of the Optimizing Point
    4. 3.4 The Lagrangian and Duality
    5. Appendix 3.A
    6. References
  9. 4 Direct Search Methods for Locating the Optimum of a Design Problem with a Single-Objective Function
    1. 4.1 Introduction
    2. 4.2 The Fundamental Algorithm
    3. 4.3 Preliminary Considerations
    4. 4.4 Unconstrained Search Algorithms
    5. 4.5 Sequential Unconstrained Minimization Techniques
    6. 4.6 Constrained Algorithms
    7. 4.7 Final Thoughts
    8. References
  10. 5 Guided Random Search and Network Techniques
    1. 5.1 Guide Random Search Techniques (GRST)
    2. 5.2 Artificial Neural Networks (ANN)
    3. References
  11. 6 Optimizing Multiobjective Function Problems
    1. 6.1 Introduction
    2. 6.2 Salient Features of Multiobjective Optimization
    3. 6.3 Selected Algorithms for Multiobjective Optimization
    4. 6.4 Weighted Sum Procedure
    5. 6.5 ε-Constraint and Lexicographic Methods
    6. 6.6 Goal Programming
    7. 6.7 Min–Max Solution
    8. 6.8 Compromise Solution Equidistant to the Utopia Point
    9. 6.9 Genetic Algorithms and Artificial Neural Networks Solution Methods
    10. 6.10 Final Comment
    11. References
  12. 7 Sensitivity Analysis
    1. 7.1 Analytical Method
    2. 7.2 Linear Governing Equations
    3. 7.3 Eigenvectors and Eigenvalues Sensitivities
    4. 7.4 Higher Order and Directional Derivatives
    5. 7.5 Adjoint Equation Algorithm
    6. 7.6 Derivatives of Real-Valued Functions Obtained via Complex Numbers
    7. 7.7 System Sensitivity Analysis
    8. 7.8 Example
    9. 7.9 System Sensitivity Analysis in Adjoint Formulation
    10. 7.10 Optimum Sensitivity Analysis
    11. 7.11 Automatic Differentiation
    12. 7.12 Presenting Sensitivity as Logarithmic Derivatives
    13. References
  13. 8 Multidisciplinary Design Optimization Architectures
    1. 8.1 Introduction
    2. 8.2 Consolidated Statement of a Multidisciplinary Optimization Problem
    3. 8.3 The MDO Terminology and Notation
    4. 8.4 Decomposition of the Optimization Task into Subtasks
    5. 8.5 Structuring the Underlying Information
    6. 8.6 System Analysis (SA)
    7. 8.7 Evolving Engineering Design Process
    8. 8.8 Single-Level Design Optimizations (S-LDO)
    9. 8.9 The Feasible Sequential Approach (FSA)
    10. 8.10 Multidisciplinary Design Optimization (MDO) Methods
    11. 8.11 Closure
    12. References
  14. 9 Knowledge Based Engineering
    1. 9.1 Introduction
    2. 9.2 KBE to Support MDO
    3. 9.3 What is KBE
    4. 9.4 When Can KBE Be Used
    5. 9.5 Role of KBE in the Development of Advanced MDO Systems
    6. 9.6 Principles and Characteristics of KBE Systems and KBE Languages
    7. 9.7 KBE Operators to Define Class and Object Hierarchies
    8. 9.8 The Rules of KBE
    9. 9.9 KBE Methods to Develop MMG Applications
    10. 9.10 Flexibility and Control: Dynamic Typing, Dynamic Class Instantiation, and Object Quantification
    11. 9.11 Declarative and Functional Coding Style
    12. 9.12 KBE Specific Features: Runtime Caching and Dependency Tracking
    13. 9.13 KBE Specific Features: Demand-Driven Evaluation
    14. 9.14 KBE Specific Features: Geometry Kernel Integration
    15. 9.15 CAD or KBE?
    16. 9.16 Evolution and Trends of KBE Technology
    17. Acknowledgments
    18. References
  15. 10 Uncertainty-Based Multidisciplinary Design Optimization
    1. 10.1 Introduction
    2. 10.2 Uncertainty-Based Multidisciplinary Design Optimization (UMDO) Preliminaries
    3. 10.3 Uncertainty Analysis
    4. 10.4 Optimization under Uncertainty
    5. 10.5 Example
    6. 10.6 Conclusion
    7. References
  16. 11 Ways and Means for Control and Reduction of the Optimization Computational Cost and Elapsed Time
    1. 11.1 Introduction
    2. 11.2 Computational Effort
    3. 11.3 Reducing the Function Nonlinearity by Introducing Intervening Variables
    4. 11.4 Reducing the Number of the Design Variables
    5. 11.5 Reducing the Number of Constraints Directly Visible to the Optimizer
    6. 11.6 Surrogate Models (SMs)
    7. 11.7 Coordinated Use of High- and Low-Fidelity Mathematical Models in the Analysis
    8. 11.8 Design Space in n Dimensions May Be a Very Large Place
    9. References
  17. Appendix A Implementation of KBE in an MDO System
    1. A.1 Introduction
    2. A.2 Phase 1: Knowledge Acquisition
    3. A.3 Phase 2: Formalization of the Knowledge
    4. A.4 Phase 3: Coding of the KBE Application
    5. A.5 Advanced Concepts
    6. A.6 Concluding Remarks and Future Developments
    7. References
  18. Appendix B Guide to Implementing an MDO System
    1. B.1 Introduction
    2. B.2 Phase 1: Defining a DEE
    3. B.3 Phase 2: The Product Data Model
    4. B.4 Phase 3: Data Configuration
    5. B.5 Phase 4: Modules and Module Interaction
    6. B.6 Phase 5: Supporting the Use of Large Number of Computers
    7. B.7 Phase 6: Job Control and User Interface
    8. B.8 Summing Up
    9. References
  19. Index
  20. End User License Agreement