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Minsavage_thesisMS_vF.pdf (3.95 MB)

ETD Abstract Container

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Neural Networks as Surrogates for Computational Fluid Dynamics Predictions of Hypersonic Flows

Minsavage, Kaitlyn Emily
http://orcid.org/0000-0003-4287-8527
http://rave.ohiolink.edu/etdc/view?acc_num=osu1610017352981371

Abstract Details

2020, Master of Science, Ohio State University, Aero/Astro Engineering.
Surrogates for computational fluid dynamics (CFD) offer the potential to significantly reduce computational expense associated with multi-discipline interactions in highly complex flow conditions. Motivated by this, and an increase in broad use of neural networks, this thesis project seeks to systematically assess accuracy, robustness, and efficiency of neural network surrogates for CFD predictions of surface pressure, heat flux, and shear stress distributions across an axisymmetric double cone and cylinder in hypersonic flow.
Jack McNamara (Advisor)
Jen-Ping Chen (Committee Member)
Daniel Reasor (Committee Member)
126 p.
Aerospace Engineering
computational fluid dynamics; surrogate models; neural networks; hypersonic flows

Recommended Citations

Citations

  • Minsavage, K. E. (2020). Neural Networks as Surrogates for Computational Fluid Dynamics Predictions of Hypersonic Flows [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610017352981371

    APA Style (7th edition)

  • Minsavage, Kaitlyn. Neural Networks as Surrogates for Computational Fluid Dynamics Predictions of Hypersonic Flows. 2020. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1610017352981371.

    MLA Style (8th edition)

  • Minsavage, Kaitlyn. "Neural Networks as Surrogates for Computational Fluid Dynamics Predictions of Hypersonic Flows." Master's thesis, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610017352981371

    Chicago Manual of Style (17th edition)

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