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000752075 019__ $$a923648183$$a932333132
000752075 020__ $$a9783319215068$$q(electronic book)
000752075 020__ $$a331921506X$$q(electronic book)
000752075 020__ $$z9783319215051
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000752075 0247_ $$a10.1007/978-3-319-21506-8$$2doi
000752075 035__ $$aSP(OCoLC)ocn922965831
000752075 035__ $$aSP(OCoLC)922965831$$z(OCoLC)923648183$$z(OCoLC)932333132
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000752075 049__ $$aISEA
000752075 050_4 $$aTA358
000752075 08204 $$a620.1/064$$223
000752075 24500 $$aApplication of surrogate-based global optimization to aerodynamic design$$h[electronic resource] /$$cEmiliano Iuliano and Esther André́́́s Pérezm editors.
000752075 264_1 $$aCham :$$bSpringer,$$c2016.
000752075 300__ $$a1 online resource.
000752075 336__ $$atext$$btxt$$2rdacontent
000752075 337__ $$acomputer$$bc$$2rdamedia
000752075 338__ $$aonline resource$$bcr$$2rdacarrier
000752075 4901_ $$aSpringer tracts in mechanical engineering
000752075 504__ $$aIncludes bibliographical references.
000752075 5050_ $$aPreface; 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
000752075 5058_ $$a1.4.4 Metamodel Obtention (SVMr)1.4.5 Multi-Point Optimization of the RAE2822 with Geometric Constraints; 1.4.6 Multi-Point Optimization of the DPW-W1 with Geometric Constraints; Conclusions; References; 2 Adaptive Sampling Strategies for Surrogate-Based AerodynamicOptimization; 2.1 Introduction; 2.2 Literature Review; 2.3 Surrogate Model; 2.3.1 SVD Solution; 2.3.2 Pseudo-Continuous Global Representation; 2.4 In-Fill Criteria; 2.4.1 Error-Driven In-Fill Criteria; 2.4.2 Objective-Driven Criteria; 2.5 Surrogate-Based Shape Optimization Approach
000752075 5058_ $$a2.6 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
000752075 5058_ $$aConclusionsReferences; 4 Multi-Objective Surrogate Based Optimization of Gas Cyclones Using Support Vector Machines and CFD Simulations; 4.1 Introduction; 4.1.1 Cyclone Geometry; 4.1.2 Cyclone Performance; 4.1.3 Literature Review; 4.1.4 Target of This Study; 4.2 Least Squares: Support Vector Regression; 4.2.1 LS-SVR Parameter Optimization; 4.3 Results and Discussion; 4.3.1 The Training Dataset; 4.3.2 Geometry Effect; 4.3.3 Geometry Optimization; Conclusions; References
000752075 506__ $$aAccess limited to authorized users.
000752075 520__ $$aAerodynamic design, like many other engineering applications, is increasingly relying on computational power. The growing need for multi-disciplinarity and high fidelity in design optimization for industrial applications requires a huge number of repeated simulations in order to find an optimal design candidate. The main drawback is that each simulation can be computationally expensive - this becomes an even bigger issue when used within parametric studies, automated search or optimization loops, which typically may require thousands of analysis evaluations. The core issue of a design-optimization problem is the search process involved. However, when facing complex problems, the high-dimensionality of the design space and the high-multi-modality of the target functions cannot be tackled with standard techniques. In recent years, global optimization using meta-models has been widely applied to design exploration in order to rapidly investigate the design space and find sub-optimal solutions. Indeed, surrogate and reduced-order models can provide a valuable alternative at a much lower computational cost. In this context, this volume offers advanced surrogate modeling applications and optimization techniques featuring reasonable computational resources. It also discusses basic theory concepts and their application to aerodynamic design cases. It is aimed at researchers and engineers who deal with complex aerodynamic design problems on a daily basis and employ expensive simulations to solve them.
000752075 588__ $$aOnline resource; title from PDF title page (viewed October 19, 2015).
000752075 650_0 $$aAerodynamics$$xMathematical models.
000752075 650_0 $$aAerodynamics$$xComputer simulation.
000752075 650_0 $$aNonconvex programming.
000752075 7001_ $$aIuliano, Emiliano,$$eeditor.
000752075 7001_ $$aPérez, Esther Andrés,$$eeditor.
000752075 77608 $$iPrint version:$$z3319215051$$z9783319215051$$w(OCoLC)911210634
000752075 830_0 $$aSpringer tracts in mechanical engineering.
000752075 852__ $$bebk
000752075 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-21506-8$$zOnline Access$$91397441.1
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