Airfoil shape optimization using variable-complexity methods.
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Airfoil shape optimization using variable-complexity methods. by Praveen Thokala

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Published .
Written in English


  • Aerofoils -- Mathematical models.,
  • Mathematical optimization.

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About the Edition

This thesis deals with variable-complexity optimization, which consists of either using high- and low-fidelity models of the analysis or using variable parameterization of the optimization problem to reduce the computational cost of the optimization process. In this thesis, we present a variable-fidelity framework that is mathematically robust. We then present the results on analytical test cases for the framework and variable-parameterization method, which involves using different design variables during the course of the optimization process. The framework and the variable-parameterization method are then used to perform airfoil shape optimization. The variable-fidelity framework performed satisfactorily for most cases while a thorough mathematical study is needed for the variable-parameterization method.

The Physical Object
Pagination59 leaves.
Number of Pages59
ID Numbers
Open LibraryOL19216107M
ISBN 100494070935

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Abstract. Aerodynamic shape optimization (ASO) plays an important role in the design of aircraft, turbomachinery and other fluid machinery. Simulation-driven ASO involves the coupling of computational fluid dynamics (CFD) solvers with numerical optimization by: Ray, T., Tsai, H., Tan, C.: Effects of Solver Fidelity on a Parallel Search Algorithm’s Performance for Airfoil Shape Optimization Problems. In: 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization Conference, , Atlanta, Georgia () Google ScholarCited by: Using the conformal mapping method and the prescribed velocity distribution, the shape of the candidate airfoil was designed. Compared with other inverse methods, the conformal mapping method was used in the Eppler code and it allowed the velocity distribution to be specified along the airfoil surface at different angles of attack [ 4 – 8 ].Cited by: 1. VARIABLE-COMPLEXITY RESPONSE SURFACE APPROXIMATIONS FOR AERODYNAMIC PARAMETERS IN HSCT OPTIMIZATION By Oleg B. Golovidov athesissubmittedtothefacultyof virginia polytechnic institute and state university in partial fulfillment of the requirements for the degree of master of science in Aerospace Engineering William H. Mason, Chairman.

Hicken and Zingg adopted similar methods for the high-fidelity aerodynamic optimisation of non-planar wings addressing the non-linearity of wake shape and how it can impact the induced drag. Several non-planar geometries, inherently creating non-planar wake-wing interactions, are optimised using discrete adjoint sensitivities and the SNOPT Cited by:   Aerodynamic Shape Optimization Using a Morphing- Body Optimization Method. Multidisciplinary Regional Jet Aircraft Design Optimization Using Advanced Variable Complexity Techniques. Comparison of Traditional, Optimization and Stochastic Methods for Structural Design.   We demonstrate the link between feasibility and fidelity and the key features of the proposed approach using two design optimization examples: a cantilever flexible beam subject to high accelerations and an airfoil in transonic flow : Ahmed H. Bayoumy, Michael Kokkolaras. 1 CFD Open Series Revision Aerodynamic Design & Optimization Ideen Sadrehaghighi, Ph.D. Optimized Baseline Optimized Baseline ANNAPOLIS, MD.

BOOKS AND PROCEEDINGS. HUGHES, K., PRESTRIDGE, K., KIM, N., HAFTKA, R. and BALACHANDAR, S., () “PROTON RADIOGRAPHY OF EXPLOSIVELY DISPERSED METAL PARTICLES WITH. Control theory based airfoil design using the Euler equations. Variable-complexity interlacing of weight equation and structural optimization for the design of the High Speed Civil Transport. Shape optimization of Navier-Stokes flows with application . Full text of "She Yang - Computational Optimization, Methods and Algorithms" See other formats. @article{Bouhlela, author = {Bouhlel, Mohamed Amine and He, Sicheng and Martins, Joaquim R. R. A.}, doi = {/s}, journal = {Structural and Multidisciplinary Optimization}, pages = {}, title = {Scalable gradient-enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes}, volume = {61}, year = .