Application of surrogate-based global optimization to aerodynamic design. (2016)

• Record Type: Book
• Further Information: Note: Emiliano Iuliano and Esther André́́́s Pérezm editors.
• Editors: Iuliano, Emiliano; Pérez, Esther Andrés
• Contents: 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.
• Publisher Details: Cham : Springer
• Publication Date: 2016
• Extent: 1 online resource
• Subjects: 620.1/064; Engineering; Aerodynamics -- Mathematical models; Aerodynamics -- Computer simulation; Nonconvex programming; TECHNOLOGY & ENGINEERING -- Engineering (General); TECHNOLOGY & ENGINEERING -- Reference; Aerodynamics -- Computer simulation; Aerodynamics -- Mathematical models; Nonconvex programming; Engineering; Aerospace Technology and Astronautics; Engineering Fluid Dynamics; Engineering Design; Simulation and Modeling; Technology & Engineering -- Mechanical; Technology & Engineering -- Industrial Design -- Product; Computers -- Computer Simulation; Mechanics of fluids; Technical design; Computer modelling & simulation; Astronautics; Hydraulic engineering; Engineering design; Computer simulation; Technology & Engineering -- Aeronautics & Astronautics; Aerospace & aviation technology; Electronic books
• Languages: English
• ISBNs: 9783319215068
• Related ISBNs: 331921506X; 9783319215051; 3319215051
• Notes: Note: Includes bibliographical references.; Note: Online resource; title from PDF title page (EBSCO, viewed October 19, 2015).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.