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Methods for design optimization using high fidelity turbulent flow simulations

Author(s)
Talnikar, Chaitanya Anil
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Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Advisor
Qiqi Wang.
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MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Design optimization with high-fidelity turbulent flow simulations can be challenging due to noisy and expensive objective function evaluations. The noise decays slowly as computation cost increases, therefore is significant in most simulations. It is often unpredictable due to chaotic dynamics of turbulence, in that it can be totally different for almost identical simulations. This thesis presents a modified parallel Bayesian optimization algorithm designed for performing optimization with high-fidelity simulations. It strives to find the optimum in a minimum number of evaluations by judiciously exploring the design space. Additionally, to potentially augment the optimization algorithm with the availability of a gradient, a massively parallel discrete unsteady adjoint solver for the compressible Navier-Stokes equations is derived and implemented. Both the methods are demonstrated on a large scale transonic fluid flow problem in a turbomachinery component.
Description
Thesis: S.M., Massachusetts Institute of Technology, School of Engineering, Center for Computational Engineering, Computation for Design and Optimization Program, 2015.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 75-79).
 
Date issued
2015
URI
http://hdl.handle.net/1721.1/106965
Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
Publisher
Massachusetts Institute of Technology
Keywords
Computation for Design and Optimization Program.

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