Tools and approaches for model-consistent data-enabled turbulence modeling for hypersonic flows

State-of-the-art computational tools are inadequate in their ability to provide accurate information in a time efficient manner for integration into the design of hypersonic weapons systems. This is especially true of new vehicle configurations that impose extreme challenges on predictive modeling of aerothermal loadings. In design applications of realistic weapons systems, it is well-recognized that Reynolds-Averaged Navier-Stokes models (RANS) offer the only feasible physics-based option for turbulent flow modeling.  However, there are numerous examples where RANS based approaches (that have largely been constructed and calibrated in incompressible flows) are insufficiently accurate to predict critical quantities of interest in hypersonic environments, such as wall heat fluxes. However, recent advances in numerical methods for high speed flows in complex geometries, wall modeling, and use of contemporary compute architectures (e.g., GPUs) has made large eddy simulations (LES) and even direct numerical simulations (DNS) feasible in targeted situations of unit and subsystem problems.  We seek to leverage the availability of selected high-fidelity data relevant to hypersonic flow phenomena (shock/turbulent boundary layer interaction, supersonic boundary layers with strong viscous heating) to make improvements in RANS closure models.  Specifically, we will utilize novel data-driven and machine learning approaches to augment existing RANS models to improve their accuracy in a range of hypersonic flows.  These data-driven augmentations critically consider constraints of model consistency with the governing equations and consider the impacts of erroneous or uncertain input data on the closure model training process.  The efficacy of these improvements to RANS closures will be demonstrated in a variety of hypersonic test cases including shock/boundary layer interaction problems, diabatic turbulent boundary layers, and transitional flows.