Application of surrogate-based global optimization to aerodynamic design [electronic resource] / Emiliano Iuliano and Esther André́́́s Pérezm editors.
Iuliano, Emiliano, editor.; Pérez, Esther Andrés, editor.
2016
TA358
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Title Application of surrogate-based global optimization to aerodynamic design [electronic resource] / Emiliano Iuliano and Esther André́́́s Pérezm editors.
ISBN 9783319215068 (electronic book)
331921506X (electronic book)
9783319215051
3319215051
DOI https://doi.org/10.1007/978-3-319-21506-8
Published Cham : Springer, 2016.
Language English
Description 1 online resource.
Other Standard Identifiers 10.1007/978-3-319-21506-8 doi
Call Number TA358
Dewey Decimal Classification 620.1/064
Summary Aerodynamic design, like many other engineering applications, is increasingly relying on computational power. The growing need for multi-disciplinarity and high fidelity in design optimization for industrial applications requires a huge number of repeated simulations in order to find an optimal design candidate. The main drawback is that each simulation can be computationally expensive - this becomes an even bigger issue when used within parametric studies, automated search or optimization loops, which typically may require thousands of analysis evaluations. The core issue of a design-optimization problem is the search process involved. However, when facing complex problems, the high-dimensionality of the design space and the high-multi-modality of the target functions cannot be tackled with standard techniques. In recent years, global optimization using meta-models has been widely applied to design exploration in order to rapidly investigate the design space and find sub-optimal solutions. Indeed, surrogate and reduced-order models can provide a valuable alternative at a much lower computational cost. In this context, this volume offers advanced surrogate modeling applications and optimization techniques featuring reasonable computational resources. It also discusses basic theory concepts and their application to aerodynamic design cases. It is aimed at researchers and engineers who deal with complex aerodynamic design problems on a daily basis and employ expensive simulations to solve them.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (viewed October 19, 2015).
Added Author Iuliano, Emiliano, editor.
Pérez, Esther Andrés, editor.
Series Springer tracts in mechanical engineering.
Available in Other Form Print version: 9783319215051
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Preface; Contents; Contributors; Acronyms; 1 Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines; 1.1 Introduction; 1.2 Literature Review; 1.3 Proposed SBGO Approach; 1.3.1 Geometry Parameterization with Non-rational Uniform B-Splines; 1.3.2 The DLR TAU Solver; 1.3.3 SVMs as Surrogate Model; 1.3.4 Evolutionary Optimization Algorithm; 1.3.5 Intelligent Estimation Search with Sequential Learning; 1.4 Numerical Results; 1.4.1 Test Cases Definition; 1.4.2 Parameterization and Design Space Definition; 1.4.3 Grid Sensitivity Analysis; RAE2822 Airfoil; DPW-W1 Wing

1.4.4 Metamodel Obtention (SVMr)1.4.5 Multi-Point Optimization of the RAE2822 with Geometric Constraints; 1.4.6 Multi-Point Optimization of the DPW-W1 with Geometric Constraints; Conclusions; References; 2 Adaptive Sampling Strategies for Surrogate-Based AerodynamicOptimization; 2.1 Introduction; 2.2 Literature Review; 2.3 Surrogate Model; 2.3.1 SVD Solution; 2.3.2 Pseudo-Continuous Global Representation; 2.4 In-Fill Criteria; 2.4.1 Error-Driven In-Fill Criteria; 2.4.2 Objective-Driven Criteria; 2.5 Surrogate-Based Shape Optimization Approach

2.6 Application: Multi-Point Shape Optimization of a Two-Dimensional Airfoil2.6.1 Problem Definition; 2.6.2 Optimization Setup; 2.6.3 Surrogate Model Validation; 2.6.4 Optimization Results; Conclusions; References; 3 PCA-Enhanced Metamodel-Assisted Evolutionary Algorithms for Aerodynamic Optimization; 3.1 Introduction; 3.2 PCA-Enhanced EAs and MAEAs; 3.2.1 PCA-Enhanced Evolution Operators; 3.2.2 EA with PCA-Assisted Metamodels; 3.3 Applications; 3.3.1 Preliminary Design of a Supersonic Business Jet; 3.3.2 Aeroelastic Design of a Wind Turbine Blade; 3.3.3 Optimization of an Isolated Airfoil

ConclusionsReferences; 4 Multi-Objective Surrogate Based Optimization of Gas Cyclones Using Support Vector Machines and CFD Simulations; 4.1 Introduction; 4.1.1 Cyclone Geometry; 4.1.2 Cyclone Performance; 4.1.3 Literature Review; 4.1.4 Target of This Study; 4.2 Least Squares: Support Vector Regression; 4.2.1 LS-SVR Parameter Optimization; 4.3 Results and Discussion; 4.3.1 The Training Dataset; 4.3.2 Geometry Effect; 4.3.3 Geometry Optimization; Conclusions; References

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