Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


burke_evan_thesis_final_format_approved_LW_7-24-2024.pdf (4.29 MB)

Digital Accessibility Report

File List


Acrobat Accessibility Report.pdf (69.96 KB)

ETD Abstract Container

Abstract Header

Surrogate Modeling of a Generic Hypersonic Vehicle Through a Novel Extension of the Multi-fidelity Polynomial Chaos Expansion

Burke, Evan J
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1722326248458662

Abstract Details

2024, Master of Science (M.S.), University of Dayton, Aerospace Engineering.
Traditional conceptual-level aerodynamic analysis is limited to empirical and/or inviscid models due to considerations of computational cost and complexity. There is a distinct desire to incorporate higher-fidelity analysis into the conceptual-design process as early as possible. This work seeks to enable the use of high-fidelity data by developing and applying multi-fidelity surrogate models that can efficiently predict the underlying response of a system with high accuracy. To that end, a novel form of the multi-fidelity polynomial chaos expansion (PCE) method is introduced, extending the surrogate modeling technique to accept three distinct fidelities of input. The PCE implementation is evaluated for a series of analytical test functions, showing excellent accuracy in creating multi-fidelity surrogate models. Aerodynamic analysis of a generic hypersonic vehicle (GHV) is performed using three codes of increasing fidelity: CBAERO (panel code), Cart3D (Euler), and FUN3D (RANS). The multi-fidelity PCE technique is used to model the aerodynamic responses of the GHV over a broad, five-dimensional input domain defined by Mach number, dynamic pressure, angle of attack, and left and right control surface settings. Mono-, bi-, and tri-fidelity PCE surrogates are generated and evaluated against a high-fidelity “truth” database to assess the global error of the surrogates focusing on the prediction of lift, drag, and pitching moment coefficients. Both monofidelity and multi-fidelity surrogates show excellent predictive capabilities. Multi-fidelity PCE models show significant promise, generating aerodynamic databases anchored to RANS fidelity at a fraction of the cost of direct evaluation.
Markus Rumpfkeil (Advisor)
Jose Camberos (Committee Member)
Timothy Eymann (Committee Member)
179 p.
Aerospace Engineering
surrogate modeling; polynomial chaos expansion; hypersonics; multifidelity; conceptual design; aerodynamics; computational fluid dynamics;

Recommended Citations

Citations

  • Burke, E. J. (2024). Surrogate Modeling of a Generic Hypersonic Vehicle Through a Novel Extension of the Multi-fidelity Polynomial Chaos Expansion [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1722326248458662

    APA Style (7th edition)

  • Burke, Evan. Surrogate Modeling of a Generic Hypersonic Vehicle Through a Novel Extension of the Multi-fidelity Polynomial Chaos Expansion. 2024. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1722326248458662.

    MLA Style (8th edition)

  • Burke, Evan. "Surrogate Modeling of a Generic Hypersonic Vehicle Through a Novel Extension of the Multi-fidelity Polynomial Chaos Expansion." Master's thesis, University of Dayton, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1722326248458662

    Chicago Manual of Style (17th edition)

dayton1722326248458662
164
© 2024, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.