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E-raamat: Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design: Applications to Aeronautics and UAV Design

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Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design: Applications to Aeronautics and UAV DesignSuurem pilt
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Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies.

This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named Hybridized Nash-Pareto games.

Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with civil aircraft and UAV, UCAV systems are implemented numerically and discussed.  Applications of increasing optimization complexity are presented as well as two hands-on test cases problems. These examples focus on aeronautical applications and will be useful to the practitioner in the laboratory or in industrial design environments. The evolutionary methods coupled with games presented in this volume can be applied to other areas including surface and marine transport, structures, biomedical engineering, renewable energy and environmental problems.





This book will be of interest to students, young scientists and engineers involved in the field of multi physics optimization.
1 Introduction
1(8)
1.1 Background
3(3)
1.2 Motivation
6(1)
1.3 Summary of
Chapters
7(2)
References
7(2)
2 Evolutionary Methods
9(12)
2.1 Overview
9(1)
2.2 Fundamentals of Evolutionary Algorithms (EAs)
10(1)
2.3 Evolutionary Algorithms (EAs)
11(1)
2.4 Benefits of EAs
11(2)
2.4.1 General Presentation of EAs Using Binary Coding
12(1)
2.4.2 Description of a Simple EA
13(1)
2.5 Mechanics of EAs
13(3)
2.5.1 Representation of Individuals
13(3)
2.6 Evolution Strategies (ESs)
16(1)
2.7 Application of EAs to Constrained Problems
17(2)
2.8 Summary of
Chapter
19(2)
References
20(1)
3 Multi-Objective EAs And Game Theory
21(18)
3.1 Generalities
21(1)
3.2 Definition of A Mult- Objective Problem
22(1)
3.3 Cooperative Games: Pareto Optimality
22(1)
3.4 Competitive Games: Nash Equilibrium
23(4)
3.4.1 Definition of Nash Equilibrium
24(1)
3.4.2 Coupling Nash Games and GAs
24(2)
3.4.3 Generalization to N Nash Players
26(1)
3.5 Hierarchical Game: Stackelberg
27(1)
3.5.1 Coupling a Stackelberg Game with GAs
28(1)
3.6 Comparison of Analytical Solutions and Numerical Game Solutions for Solving a Two Mathematical Functions Minimisation Problem
28(4)
3.6.1 Analytical Solution
29(1)
3.6.2 Nash/Gas and Stackleberg/Gas Numerical Solutions
30(2)
3.7 Hybridized Games
32(6)
3.7.1 Algorithms for HAPMOEA and Hybridized Games
34(4)
3.8 Summary of
Chapter 3
38(1)
References
38(1)
4 Advanced Techniques for Evolutionary Algorithms (EAs)
39(14)
4.1 Generalities
39(1)
4.2 Distributed and Parallel EAS
39(2)
4.3 Hierarchical EAS (HEAs)
41(1)
4.4 Asynchronous Evolutionary Algorithmss (EAS)
42(1)
4.5 Advanced Operators
43(2)
4.5.1 Covariance Matrix Adaptation (CMA)
43(1)
4.5.2 Pareto Tournament Selection
44(1)
4.6 Advanced Games
45(4)
4.6.1 Virtual And Real Nash-Games
46(1)
4.6.2 Nash-Game and Hierarchical Asynchronous Parallel EAs (NASH-HAPEA)
47(1)
4.6.3 Hybrid-Game Coupled with Single-Objective or Multi-Objective Evolutionary Algorithms
48(1)
4.7 Meta Model Assisted EAS
49(1)
4.8 Summary of
Chapter
50(3)
References
51(2)
5 Multidisciplinary Design Optimisation and Robust Design in Aerospace Systems
53(16)
5.1 Generalities
53(1)
5.2 Conceptual, Preliminary and Detailed Design
53(1)
5.3 Multi-Disciplinary Design Analysis (MDA) and Optimisation
54(2)
5.3.1 Definition
54(1)
5.3.2 Challenges and needs for MDO
54(2)
5.3.3 MDO Application Using Gradient-Based Methods
56(1)
5.4 Approaches to MDO
56(5)
5.4.1 Multi-Disciplinary Design Feasible (MDF)
57(1)
5.4.2 Individual Discipline Feasible (IDF)
58(1)
5.4.3 Collaborative Optimization (CO)
59(2)
5.4.4 Criteria and Performance of MDO Implementations
61(1)
5.5 Uncertainty Based Robust Design
61(4)
5.5.1 Robust/Uncertainty Method
61(2)
5.5.2 From Single-Objective to Multi-Objective Design Optimisation Using the Robust Design Method
63(2)
5.5.3 Robust Multi-Objective/Multi-Disciplinary Design Optimization
65(1)
5.6 Limitations of Traditional Optimisation Techniques for MDO and Robust Design
65(2)
5.6.1 MDO Using Traditional Method and Evolutionary Algorithms
65(1)
5.6.2 Advantages and Drawbacks of Robust Design
66(1)
5.7 Summary of
Chapter
67(2)
References
67(2)
6 A Framework for Numerical Design and Optimization Algorithms
69(20)
6.1 Overview
69(1)
6.2 An Optimization Framework
69(2)
6.3 Implementation of the Framework
71(3)
6.4 Optimization Methodology
74(2)
6.5 Optimization Algorithms
76(10)
6.5.1 Overall Optimization Algorithm
76(2)
6.5.2 A Generic Problem for Analysis Algorithm
78(2)
6.5.3 Single-Objective Design Optimization Algorithm
80(1)
6.5.4 Multi-Objective Design Optimization Algorithm
80(1)
6.5.5 Optimization Algorithm with Multi-objective Hierarchical Evolutionary Algorithms
80(4)
6.5.6 Multi-Disciplinary Design Optimization Algorithm
84(2)
6.6 Robust Design Optimization
86(1)
6.7 Summary of
Chapter
86(3)
References
86(3)
7 Single Objective Model Test Case Problems
89(34)
7.1 Overview
89(1)
7.2 Wing Reconstruction Using Hierarchial Asynchronous Parallel Multi-Objective Evolutionary Algorthms (HAPMOEA) and Nash-Evolutionary Algorithms
89(3)
7.3 Active Flow Control Bump Design Optimization
92(21)
7.3.1 Suction Side SCB Design Optimization
95(3)
7.3.2 Suction and Pressure Sides SCB Design Optimization
98(6)
7.3.3 Double SCB Design using HAPMOEA
104(2)
7.3.4 Double SCB Design using Hybridized Game
106(7)
7.4 Generic Aircraft Wing Aerofoil Section Design Optimization
113(5)
7.5 Summary of
Chapter 7
118(5)
References
121(2)
8 Multi-Objective Optimization Model Test Case Problems
123(72)
8.1 Overview
123(1)
8.2 Pareto Reconstruction: Two Airfoils at Two Different Design Points
123(5)
8.3 Multi-Element Airfoil Reconstruction: Two- Dimensional Two Objective Aircraft High Lift System Design and Optimization
128(8)
8.4 Unmanned Combat Aerial Vehicle Configuration: Conceptual Design Optimisation
136(10)
8.5 Unmanned Aerial Vehicle Mission Path Planning System (Hybridized Game/NSGA-II)
146(13)
8.5.1 Test 1: Start to Target to Start Position Trajectory Optimization
150(6)
8.5.2 Test 2: Start to Target to End Position Trajectory Optimization
156(3)
8.6 Unmanned Aerial Vehicle (Uav) Configuration: Detailed Design Optimisation
159(18)
8.6.1 Multi-Objective Design Optimisation of UCAV Using Hybridized Games
174(3)
8.7 Aerostructural Optimisation of a Medium Alitude Long Endurance (Male) UAS
177(8)
8.8 Aero-Electromagnetic Optimization of a UAS
185(6)
8.9 Summary of
Chapter 8
191(4)
References
193(2)
9 Robust Multi-Objective and Multi-Disciplinary Model Optimization Test Cases
195(70)
9.1 Overview
195(1)
9.2 Robust Active Flow Control Design Optimization
195(16)
9.2.1 SCB Shape Design Optimisation at 45% of the chord Boundary Layer Transition
196(3)
9.2.2 Robust SCB Shape Design Optimization with Uncertainty Boundary Layer Transitions
199(12)
9.3 Robust Multi-Objective Generic Aircraft Wing Optimization
211(7)
9.4 Robust Aero-Structural Generic Aircraft Wing Optimization
218(11)
9.5 Robust Aero-Electromagnetic Design Optimization of UAS
229(23)
9.5.1 Robust Multi-Disciplinary Design Optimisation of UCAS Using HAPMOEA Software
231(8)
9.5.2 Robust Multi-Disciplinary Design Optimization of UCAS Using Hybridized Games
239(13)
9.6 Robust Multi-Disciplinary Aero-Electro-Structural UCAV Design Optimization
252(9)
9.7 Summary of
Chapter
261(3)
9.8 Appendix
264(1)
References
264(1)
10 Robust Airfoil Design Optimization with Morphing Techniques
265(20)
10.1 Overview
265(1)
10.2 Morphing Airfoil/Wing Design Mechanism: Leading and Trailing Edge Deformation
266(1)
10.2.1 Parameterization of Morphing Aerofoil/Wing: Leading and Trailing Edge Deformation
266(1)
10.2.2 Baseline Design
266(1)
10.3 Morphing Airfoil/Wing Design Optimisation at Cruise Flight Conditions
267(10)
10.3.1 Trailing Edge Deformation (TED) Design Optimisation
267(4)
10.3.2 Robust Leading and Trailing Edge Deformation (LTED) Design Optimization
271(6)
10.4 Morphing Airfoil/Wing Design Optimization at Take-Off and Landing Conditions Using Moga and Hybridized Game with Moga
277(7)
10.5 Conclusion and Future Research
284(1)
10.6 Summary of
Chapter 10
284(1)
References
284(1)
Appendix: Two "Hands-On" Examples of Optimization Problems
285(20)
A.1 Overview
285(1)
A.2 Reconstruction of BI-NACA Using HAPEA and NASH-EA
285(10)
A.2.1 Introduction
285(1)
A.2.2 Definition of the Test Case
285(1)
A.2.3 Optimization
286(4)
A.2.4 Software and Computer Facilities Needed for Solving the Inverse Problem
290(1)
A.2.5 A Step by Step Design Optimisation Procedure
291(1)
A.2.6 Analysis and Synthesis of Results Obtained by HAPEA and Nash-EAs Software
292(2)
A.2.7 Conclusion
294(1)
A.3 Unmanned Aerial Vehicle Design: Multi-Objective Optimization
295(10)
A.3.1 Introduction
295(1)
A.3.2 Definition of the Test Case
296(1)
A.3.3 Optimization
297(3)
A.3.4 Software Needed for Solving the Optimization Problem and Computer Used
300(1)
A.3.5 A Step-by-Step Design Optimization Procedure
301(1)
A.3.6 Analysis and synthesis of results obtained by HAPMOEA software
301(3)
A.3.7 Conclusion
304(1)
References 305
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