Project Grant FA95501810486

This $499,624 National Science Foundation project grant supports research at the University of Pittsburgh to develop physics-guided machine learning methods for turbulent flow simulation. Funded under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), the three-year award aims to advance computational fluid dynamics capabilities. Specifically, the university researchers will create a new deep learning model incorporating physical constraints to reconstruct...

This National Science Foundation (NSF) Project Grant award under the Engineering program (CFDA 47.041) for $259,701 aims to develop a novel hierarchical adjoint-based data assimilation framework to improve the accuracy and efficiency of turbulence flow modeling and prediction. The key products/services to be delivered include: Development of open-source software tools encapsulating the hierarchical adjoint-based data assimilation (HADA) framework, which will be made available to researchers...

This $204,884 Project Grant award from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences (CFDA 47.049) supports research by Towson University to study various subgrid scale turbulence models and their connections to the Navier-Stokes equations. The research aims to explore the mathematical properties of these turbulence models, apply data assimilation algorithms, and leverage deep learning methods for parameter estimation. Key focus areas include...

This Project Grant award from the National Science Foundation (NSF) Engineering Directorate (CFDA 47.041) provides $523,552 to the University of Texas at Austin to develop advanced computational models for simulating complex turbulent fluid flows. The research aims to create reliable, broadly applicable turbulence models for use in Large Eddy Simulation (LES) to enable more practical and accurate simulations across fields like aeronautics, propulsion, power generation, and wind energy. In...

This collaborative research project, funded by the National Science Foundation's (NSF) Geosciences program (CFDA 47.050) and awarded to Massachusetts Institute of Technology on October 1, 2025, delivers fundamental research aimed at advancing understanding of turbulent processes within coupled air-sea boundary layers. With total obligated funding of $280,247 through September 30, 2028, the project combines high-resolution laboratory experiments, high-fidelity numerical simulations, and...

This $173,392 two-year Project Grant from the National Science Foundation's Integrative Activities program (CFDA 47.083) supports research and training focused on developing predictive computational fluid dynamics models for flow control via anisotropic surface textures. The University of Mississippi awardee will collaborate with Stanford University's Center for Turbulence Research to generate unprecedented datasets of flows over movable microstructures and gain new understanding of nonlinear...

The National Science Foundation (NSF) Engineering program (CFDA 47.041) has awarded a $349,999 Project Grant to the University of Texas at Austin to develop reliable Reynolds Averaged Navier-Stokes (RANS) turbulence models that can generalize to complex turbulent flows. The objective is to improve the predictive capabilities of computational fluid dynamics simulations, which have applications in aerospace, automotive, power generation, and wind energy sectors. The approach involves developing...

This three-year, $299,999 National Science Foundation project grant in the Engineering program (CFDA 47.041) will develop a physics-inspired framework for turbulent flow simulations at the University of California, Irvine. The grantee will introduce the concept of physics-inspired coarsening to address challenges with large eddy simulation modeling, including inconsistent grid resolution and treatment of boundaries. Through direct numerical simulations and LES, the grantee will rigorously test...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program award of $175,000 will fund the development of an open-source, GPU-enabled computational fluid dynamics (CFD) framework at the University of Wyoming. The project aims to create advanced numerical methods and software for accurately and efficiently simulating fluid dynamics problems, with a focus on modeling compressible Navier Stokes equations using the high-order accurate discontinuous...

This Project Grant from the National Science Foundation's Division of Chemical, Bioengineering, Environmental, and Transport Systems will fund $900,000 over three years to develop new physics-informed deep learning methods for discovering probabilistic turbulence closure models. The University of Pittsburgh will utilize machine learning algorithms and high-performance computing to solve the forward and inverse probability density function transport equations governing turbulent flows. If...