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RANS Turbulence Closure Augmented with Physics-Informed Machine Learning for Hypersonic Flows

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-22-C-0273
Agency Tracking Number: N22A-T016-0187
Amount: $239,573.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N22A-T016
Solicitation Number: 22.A
Timeline
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-06-06
Award End Date (Contract End Date): 2023-11-06
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
 tpalmer@ata-e.com
Business Contact
 Joshua Davis
Phone: (858) 480-2028
Email: jdavis@ata-e.com
Research Institution
 University of Arkansas
 Kathy Scheibel
 
1125 W. Maple Street, 210 Administration Building
Fayetteville, AR 72701-7174
United States

 (479) 575-3845
 Nonprofit College or University
Abstract

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, proposes a data-driven RANS turbulence closure that uses 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 to derive the functional form of each term from scale-resolved CFD data from representative flow configurations. In Phase I, ATA will create and train the ML algorithms and validate the modified turbulence closure against up to three training cases. A detailed development and validation plan to be executed in Phase II will be formulated to expand the prototype model to additional flow configurations and include a laminar-to-turbulent transition model. The proposed ML-based methodology will be packaged as an adaptable, general framework for improving the accuracy of RANS turbulence closures in the hypersonic regime, and it will be known as the HYpersonic Physics-informed Energy-tracing RANS (HYPER) Tuner.

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

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