2026-09-20T07:03:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1578102024-12-12T03:04:11Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lozano-Durán, Adrián
Costa, Samuel Thomas
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2024-12-11T15:03:49Z
2024-12-11T15:03:49Z
2024-09
2024-12-05T14:12:08.679Z
https://hdl.handle.net/1721.1/157810
0009-0008-9664-4471
Computational Fluid Dynamics (CFD) is an key tool in the design of aircraft, allowing engineers to predict the performance of a configuration without having to conduct expensive physical tests. However, in order to move to a greater reliance on CFD, the industry requires a high level of accuracy and fast turnaround time, which current methods cannot deliver. In recent years, the rapid development of the GPU industry has led to an explosion of computational power with the GPU architecture. This has allowed wall-modeled large eddy simulation (WMLES), a higher fidelity simulation technique, to become practical for industry use. WMLES requires the use of both a sub-grid scale (SGS) model and a wall model in order to close the system of equations for integration. Although WMLES delivers an improvement over previous methods, classical SGS and wall models do not deliver the accuracy required by the aviation industry. To help close this gap, we introduce a GPU-compatible version of the Building-Block Flow Model (BFM), a machine learning based unified sub-grid scale and wall model for LES introduced in [1]. In this thesis, we discuss the implementation of the BFM for GPU, timing of the BFM versus other closure models for WMLES, and a variety of tests with the BFM designed to evaluate its performance, and possible avenues of improvement.
S.M.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
A GPU-Enabled Building Block Flow Model for Computational Fluid Dynamics
Thesis
application/pdf
Master
Master of Science in Aeronautics and Astronautics
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e76841da-c0bd-49eb-b860-dcc02eda6a89">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>A GPU-Enabled Building Block Flow Model for Computational Fluid Dynamics</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2024-09</PublicationDate>
<Authors>
<Author>
<DisplayName>Costa, Samuel Thomas</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://creativecommons.org/licenses/by-nc-nd/4.0/</License>
<Abstract>Computational Fluid Dynamics (CFD) is an key tool in the design of aircraft, allowing engineers to predict the performance of a configuration without having to conduct expensive physical tests. However, in order to move to a greater reliance on CFD, the industry requires a high level of accuracy and fast turnaround time, which current methods cannot deliver. In recent years, the rapid development of the GPU industry has led to an explosion of computational power with the GPU architecture. This has allowed wall-modeled large eddy simulation (WMLES), a higher fidelity simulation technique, to become practical for industry use. WMLES requires the use of both a sub-grid scale (SGS) model and a wall model in order to close the system of equations for integration. Although WMLES delivers an improvement over previous methods, classical SGS and wall models do not deliver the accuracy required by the aviation industry. To help close this gap, we introduce a GPU-compatible version of the Building-Block Flow Model (BFM), a machine learning based unified sub-grid scale and wall model for LES introduced in [1]. In this thesis, we discuss the implementation of the BFM for GPU, timing of the BFM versus other closure models for WMLES, and a variety of tests with the BFM designed to evaluate its performance, and possible avenues of improvement.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>