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...
The National Science Foundation (NSF) awarded a $220,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Pittsburgh. The 3-year grant supports research on improving the accuracy, decreasing the complexity, and exploring promising computational algorithms for Unsteady Reynolds Averaged Navier-Stokes turbulence models. This research aims to advance the modeling and numerical simulation of turbulent fluid flows, which is essential for...
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...
The University of Pittsburgh was awarded a three-year $338,526 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) to develop structure-preserving finite element methods for incompressible fluid flow modeling applications. Under the award, set to run from July 2023 through June 2026, the University will conduct research focused on improving existing divergence-conforming finite element methods for solving Navier-Stokes equations...
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 program (CFDA 47.041) provides $350,000 in funding to Harvard University to develop reliable and predictive computational models for simulating complex turbulent flows. The objective is to create generalized Reynolds Averaged Navier-Stokes (RANS) turbulence models that can accurately predict turbulent flows, which are essential for advancing key technologies in sectors such as aerospace, automotive, and power...
This $154,994 project grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to deepen the understanding of pulsatile turbulent flows over rough surfaces. The project will conduct high-accuracy simulations to reveal detailed flow physics and develop new predictive tools with improved real-time accuracy and off-design adaptability. The research will integrate comprehensive engagement initiatives to spark interest and cultivate STEM skills among...
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 $319,971 federal Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) is supporting research at Florida State University (FSU) to evaluate the complexity of unsteady turbulent flows using advanced mathematical methods. The project aims to establish a new theoretical framework for quantifying the complexity of these flow patterns, which often contain both predictable and random elements. The research will utilize large-scale flow structures as...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $360,555 to Rutgers, The State University to develop machine learning tools for improving simulations of magnetohydrodynamic (MHD) turbulence and Rayleigh-Bénard convection. The project aims to leverage machine learning to uncover fundamental equations governing turbulent systems and develop new models for star formation. The grant also supports enhancing the...