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Data-Driven Hypersonic Turbulence Modeling Toolset

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-24-C-0081
Agency Tracking Number: N22A-T016-0187
Amount: $997,409.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N22A-T016
Solicitation Number: 22.A
Solicitation Year: 2022
Award Year: 2024
Award Start Date (Proposal Award Date): 2023-10-23
Award End Date (Contract End Date): 2025-10-30
Small Business Information
13290 Evening Creek Drive South
San Diego, CA 92128-4695
United States
DUNS: 133709001
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Timothy Palmer
 (585) 480-2066
Business Contact
 Heather Wilkens
Phone: (858) 480-2043
Research Institution
 University of Arkansas
 Amber Hutchinson
1125 W. Maple Street, 316 Administration Building
Fayetteville, AR 72701-7174
United States

 (479) 575-5427
 Nonprofit College or University

Development of hypersonic aircraft and weapon systems has become a critical focus for the Department of Defense to maintain global strike and projection of force capabilities. Despite decades of research, traditional computational fluid dynamics (CFD) methods are either incapable of adequately predicting complex features in hypersonic flows or too expensive to be of practical use for vehicle design in this regime. Therefore, a new modeling methodology is required that approaches the accuracy of scale-resolved CFD simulations at a cost similar to Reynolds-averaged Navier-Stokes (RANS). ATA Engineering, in partnership with the University of Arkansas, has developed a data-driven framework, known as the HYpersonic Physics-informed Energy-tracing RANS (HYPER) Tuner, to tune RANS turbulence closures using machine learning (ML) to modify several terms in a standard RANS turbulence model to improve its accuracy in hypersonic flows. The term modifications will use genetically programmed symbolic regression and constant tuning algorithms to derive the functional form of each term from scale-resolved CFD and experimental data from representative flow configurations. In Phase I, ATA demonstrated the improved accuracy of a tuned RANS turbulence closure on simple, high-speed attached boundary layer cases. In Phase II, ATA will expand and refine the ML algorithms in HYPER Tuner and validate the HYPER Tuner framework against several training cases, including both CFD and experimental data sets and running new high-fidelity CFD simulations on some of those experimental configurations. The Phase II project will also include development and validation of a laminar-to-turbulent transition model for hypersonic flows to further improve RANS predictions of wall shear stress and wall heat flux in the early portion of the developing boundary layer. At the completion of Phase II, HYPER Tuner will be an adaptable, general framework for improving the accuracy of RANS turbulence closures in the hypersonic regime, including various ML algorithms and automated features to enhance its efficacy and ease-of-use for general CFD practitioners.

* Information listed above is at the time of submission. *

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