Atjaunināt sīkdatņu piekrišanu

E-grāmata: Multidisciplinary Design Optimization Supported by Knowledge Based Engineering

5.00/5 (1 ratings by Goodreads)
, ,
  • Formāts: PDF+DRM
  • Izdošanas datums: 08-May-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118897096
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 108,20 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Bibliotēkām
Multidisciplinary Design Optimization Supported by Knowledge Based EngineeringPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 08-May-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118897096
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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.
Preface xiii
Acknowledgment xv
Styles for Equations xvi
1 Introduction 1(9)
1.1 Background
1(2)
1.2 Aim of the Book
3(1)
1.3 The Engineer in the Loop
3(1)
1.4
Chapter Contents
4(6)
1.4.1
Chapter 2: Modern Design and Optimization
4(1)
1.4.2
Chapter 3: Searching the Constrained Design Space
4(1)
1.4.3
Chapter 4: Direct Search Methods for Locating the Optimum of a Design Problem with a Single-Objective Function
5(1)
1.4.4
Chapter 5: Guided Random Search and Network Techniques
5(1)
1.4.5
Chapter 6: Optimizing Multiple-Objective Function Problems
6(1)
1.4.6
Chapter 7: Sensitivity Analysis
6(1)
1.4.7
Chapter 8: Multidisciplinary Design and Optimization Methods
7(1)
1.4.8
Chapter 9: KBE
7(1)
1.4.9
Chapter 10: Uncertainty-Based Multidisciplinary Design and Optimization
8(1)
1.4.10
Chapter 11: Ways and Means for Control and Reduction of the Optimization Computational Cost and Elapsed Time
8(1)
1.4.11 Appendix A: Implementation of KBE in Your MDO Case
9(1)
1.4.12 Appendix B: Guide to Implementing an MDO System
9(1)
2 Modern Design and Optimization 10(17)
2.1 Background to
Chapter
10(1)
2.2 Nature and Realities of Modern Design
11(1)
2.3 Modern Design and Optimization
12(8)
2.3.1 Overview of the Design Process
13(2)
2.3.2 Abstracting Design into a Mathematical Model
15(2)
2.3.3 Mono-optimization
17(3)
2.4 Migrating Optimization to Modern Design: The Role of MDO
20(5)
2.4.1 Example of an Engineering System Optimization Problem
21(3)
2.4.2 General Conclusions from the Wing Example
24(1)
2.5 MDO's Relation to Software Tool Requirements
25(1)
2.5.1 Knowledge-Based Engineering
26(1)
References
26(1)
3 Constrained Design Space Search 27(20)
3.1 Introduction
27(2)
3.2 Defining the Optimization Problem
29(3)
3.3 Characterization of the Optimizing Point
32(7)
3.3.1 Curvature Constrained Problem
32(2)
3.3.2 Vertex Constrained Problem
34(2)
3.3.3 A Curvature and Vertex Constrained Problem
36(1)
3.3.4 The Kuhn—Tucker Conditions
37(2)
3.4 The Lagrangian and Duality
39(5)
3.4.1 The Lagrangian
40(1)
3.4.2 The Dual Problem
41(3)
Appendix
44(2)
References
46(1)
4 Direct Search Methods for Locating the Optimum of a Design Problem with a Single-Objective Function 47(33)
4.1 Introduction
47(1)
4.2 The Fundamental Algorithm
48(1)
4.3 Preliminary Considerations
49(5)
4.3.1 Line Searches
50(1)
4.3.2 Polynomial Searches
50(1)
4.3.3 Discrete Point Line Search
51(2)
4.3.4 Active Set Strategy and Constraint Satisfaction
53(1)
4.4 Unconstrained Search Algorithms
54(5)
4.4.1 Unconstrained First-Order Algorithm or Steepest Descent
55(1)
4.4.2 Unconstrained Quadratic Search Method Employing Newton Steps
56(2)
4.4.3 Variable Metric Search Methods
58(1)
4.5 Sequential Unconstrained Minimization Techniques
59(9)
4.5.1 Penalty Methods
60(4)
4.5.2 Augmented Lagrangian Method
64(1)
4.5.3 Simple Comparison and Comment on SUMT
64(2)
4.5.4 Illustrative Examples
66(2)
4.6 Constrained Algorithms
68(11)
4.6.1 Constrained Steepest Descent Method
70(4)
4.6.2 Linear Objective Function with Nonlinear Constraints
74(4)
4.6.3 Sequential Quadratic Updating Using a Newton Step
78(1)
4.7 Final Thoughts
79(1)
References
79(1)
5 Guided Random Search and Network Techniques 80(18)
5.1 Guided Random Search Techniques (GRST)
80(9)
5.1.1 Genetic Algorithms (GA)
81(1)
5.1.2 Design Point Data Structure
81(1)
5.1.3 Fitness Function
82(5)
5.1.4 Constraints
87(1)
5.1.5 Hybrid Algorithms
87(1)
5.1.6 Considerations When Using a GA
87(1)
5.1.7 Alternative to Genetic-Inspired Creation of Children
88(1)
5.1.8 Alternatives to GA
88(1)
5.1.9 Closing Remarks for GA
89(1)
5.2 Artificial Neural Networks (ANN)
89(8)
5.2.1 Neurons and Weights
91(2)
5.2.2 Training via Gradient Calculation and Back-Propagation
93(4)
5.2.3 Considerations on the Use of ANN
97(1)
References
97(1)
6 Optimizing Multiobjective Function Problems 98(18)
6.1 Introduction
98(1)
6.2 Salient Features of Multiobjective Optimization
99(3)
6.3 Selected Algorithms for Multiobjective Optimization
102(2)
6.4 Weighted Sum Procedure
104(4)
6.5 c-Constraint and Lexicographic Methods
108(3)
6.6 Goal Programming
111(1)
6.7 Min—Max Solution
111(2)
6.8 Compromise Solution Equidistant to the Utopia Point
113(1)
6.9 Genetic Algorithms and Artificial Neural Networks Solution Methods
113(2)
6.9.1 GAs
114(1)
6.9.2 ANN
114(1)
6.10 Final Comment
115(1)
References
115(1)
7 Sensitivity Analysis 116(39)
7.1 Analytical Method
116(6)
7.1.1 Example 7.1
118(3)
7.1.2 Example 7.2
121(1)
7.2 Linear Governing Equations
122(2)
7.3 Eigenvectors and Eigenvalues Sensitivities
124(5)
7.3.1 Buckling as an Eigen-problem
125(1)
7.3.2 Derivatives of Eigenvalues and Eigenvectors
125(2)
7.3.3 Example 7.3
127(2)
7.4 Higher Order and Directional Derivatives
129(2)
7.5 Adjoint Equation Algorithm
131(2)
7.6 Derivatives of Real-Valued Functions Obtained via Complex Numbers
133(2)
7.7 System Sensitivity Analysis
135(9)
7.7.1 Example 7.4
139(5)
7.8 Example
144(1)
7.9 System Sensitivity Analysis in Adjoint Formulation
145(1)
7.10 Optimum Sensitivity Analysis
146(4)
7.10.1 Lagrange Multiplier A as a Shadow Price
149(1)
7.11 Automatic Differentiation
150(3)
7.12 Presenting Sensitivity as Logarithmic Derivatives
153(1)
References
154(1)
8 Multidisciplinary Design Optimization Architectures 155(53)
8.1 Introduction
155(1)
8.2 Consolidated Statement of a Multidisciplinary Optimization Problem
156(2)
8.3 The MDO Terminology and Notation
158(3)
8.3.1 Operands
159(1)
8.3.2 Coupling Constraints
159(1)
8.3.3 Operators
160(1)
8.4 Decomposition of the Optimization Task into Subtasks
161(1)
8.5 Structuring the Underlying Information
162(5)
8.6 System Analysis (SA)
167(3)
8.7 Evolving Engineering Design Process
170(3)
8.8 Single-Level Design Optimizations (S-LDO)
173(3)
8.8.1 Assessment
175(1)
8.9 The Feasible Sequential Approach (FSA)
176(2)
8.9.1 Implementation Options
177(1)
8.10 Multidisciplinary Design Optimization (MDO) Methods
178(21)
8.10.1 Collaborative Optimization (CO)
179(10)
8.10.2 Bi-Level Integrated System Synthesis (BLISS)
189(3)
8.10.3 BLISS Augmented with SM
192(7)
8.11 Closure
199(6)
8.11.1 Decomposition
199(1)
8.11.2 Approximations and SM
200(1)
8.11.3 Anatomy of a System
200(1)
8.11.4 Interactions of the System and Its BBs
201(1)
8.11.5 Intrinsic Limitations of Optimization in General
202(1)
8.11.6 Optimization across a Choice of Different Design Concepts
202(1)
8.11.7 Off-the-Shelf Commercial Software Frameworks
203(2)
References
205(3)
9 Knowledge Based Engineering 208(50)
9.1 Introduction
208(1)
9.2 KBE to Support MDO
209(1)
9.3 What is KBE
210(3)
9.4 When Can KBE Be Used
213(1)
9.5 Role of KBE in the Development of Advanced MDO Systems
214(6)
9.6 Principles and Characteristics of KBE Systems and KBE Languages
220(2)
9.7 KBE Operators to Define Class and Object Hierarchies
222(8)
9.7.1 An Example of a Product Model Definition in Four KBE Languages
226(4)
9.8 The Rules of KBE
230(6)
9.8.1 Logic Rules (or Conditional Expressions)
230(1)
9.8.2 Math Rules
231(1)
9.8.3 Geometry Manipulation Rules
232(2)
9.8.4 Configuration Selection Rules (or Topology Rules)
234(1)
9.8.5 Communication Rules
235(1)
9.8.6 Beyond Classical KBS and CAD
236(1)
9.9 KBE Methods to Develop MMG Applications
236(5)
9.9.1 High-Level Primitives (HLPs) to Support Parametric Product Modeling
237(1)
9.9.2 Capability Modules (CMs) to Support Analysis Preparation
238(3)
9.10 Flexibility and Control: Dynamic Typing, Dynamic Class Instantiation, and Object Quantification
241(1)
9.11 Declarative and Functional Coding Style
241(2)
9.12 KBE Specific Features: Runtime Caching and Dependency Tracking
243(3)
9.13 KBE Specific Features: Demand-Driven Evaluation
246(1)
9.14 KBE Specific Features: Geometry Kernel Integration
247(5)
9.14.1 How a KBE Language Interacts with a CAD Engine
248(4)
9.15 CAD or KBE?
252(1)
9.16 Evolution and Trends of KBE Technology
253(3)
Acknowledgments
256(1)
References
256(2)
10 Uncertainty-Based Multidisciplinary Design Optimization 258(29)
10.1 Introduction
258(1)
10.2 Uncertainty-Based Multidisciplinary Design Optimization (UMDO) Preliminaries
259(5)
10.2.1 Basic Concepts
259(4)
10.2.2 General UMDO Process
263(1)
10.3 Uncertainty Analysis
264(8)
10.3.1 Monte Carlo Methods (MCS)
265(1)
10.3.2 Taylor Series Approximation
266(2)
10.3.3 Reliability Analysis
268(3)
10.3.4 Decomposition-Based Uncertainty Analysis
271(1)
10.4 Optimization under Uncertainty
272(10)
10.4.1 Reliability Index Approach (RIA) and Performance Measure Approach (PMA) Methods
273(2)
10.4.2 Single Level Algorithms (SLA)
275(3)
10.4.3 Approximate Reliability Constraint Conversion Techniques
278(2)
10.4.4 Decomposition-Based Method
280(2)
10.5 Example
282(3)
10.6 Conclusion
285(1)
References
285(2)
11 Ways and Means for Control and Reduction of the Optimization Computational Cost and Elapsed Time 287(23)
11.1 Introduction
287(1)
11.2 Computational Effort
288(1)
11.3 Reducing the Function Nonlinearity by Introducing Intervening Variables
289(1)
11.4 Reducing the Number of the Design Variables
289(3)
11.4.1 Linking by Groups
290(2)
11.5 Reducing the Number of Constraints Directly Visible to the Optimizer
292(6)
11.5.1 Separation of Well-Satisfied Constraints from the Ones Violated or Nearly Violated
292(1)
11.5.2 Representing a Set of Constraints by a Single Constraint
293(1)
11.5.3 Replacing Constraints by Their Envelope in the Kreisselmeier—Steinhauser Formulation
293(5)
11.6 Surrogate Methods (SMs)
298(3)
11.7 Coordinated Use of High- and Low-Fidelity Mathematical Models in the Analysis
301(7)
11.7.1 Improving LF Analysis by Infrequent Use of HF Analysis
301(2)
11.7.2 Reducing the Number of Quantities Being Approximated
303(1)
11.7.3 Placement of the Trial Points xT in the Design Space x
304(4)
11.8 Design Space in n Dimensions May Be a Very Large Place
308(1)
References
309(1)
Appendix A Implementation of KBE in an MDO System 310(39)
Appendix B Guide to Implementing an MDO System 349(11)
Index 360
Jaroslaw Sobieszczanski-Sobieski NASA Langley Research Center, USA

Alan Morris Cranfield University, UK

Michel van Tooren University of South Carolina, USA
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811188970962e.html
Keywords: