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

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.