Design space exploration using uncertainty-based bounding methods in computational fluid dynamics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A method is proposed to use computational fluid dynamics (CFD) as the analysis tool in a sequential decision process (SDP). The SDP efficiently explores and reduces the trade space by repeatedly bounding the prediction of the design parameters through multiple analysis iterations of increasing fidelity. In the context of fluid-dynamic shape design with CFD, the trade space containing the optimal design is reduced using a sequence of computational meshes, each having reduced error bounds compared to those prior. Earlier iterations, with higher numerical uncertainty but lower computational time, are used to eliminate regions not of interest within the trade space. The reduced subset is then further evaluated using CFD with tighter bounds, achieved through a more costly, refined computational mesh. This process is demonstrated on an aerodynamic shape design in a two-parameter, drag minimization study of a generic fuselage pod.

Original languageEnglish (US)
Title of host publication2018 Fluid Dynamics Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105531
DOIs
StatePublished - Jan 1 2018
Event48th AIAA Fluid Dynamics Conference, 2018 - Atlanta, United States
Duration: Jun 25 2018Jun 29 2018

Publication series

Name2018 Fluid Dynamics Conference

Other

Other48th AIAA Fluid Dynamics Conference, 2018
CountryUnited States
CityAtlanta
Period6/25/186/29/18

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Engineering (miscellaneous)

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  • Cite this

    Valenti, J. D., Miller, S. W., Yukish, M. A., & Kinzel, M. P. (2018). Design space exploration using uncertainty-based bounding methods in computational fluid dynamics. In 2018 Fluid Dynamics Conference [AIAA 2018-3552] (2018 Fluid Dynamics Conference). American Institute of Aeronautics and Astronautics Inc, AIAA. https://doi.org/10.2514/6.2018-3552