This $445,930 National Science Foundation project grant through the Engineering (47.041) program will fund the development of efficient methods to calibrate models for missing physics in simulations of complex turbulent reacting flows. The University of Notre Dame will optimize turbulence closures and develop an adjoint-based optimization method for canonical flows and a co-optimization framework leveraging adjoint and ensemble Kalman-based optimization for geometrically complex flows. The...
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 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) 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 $229,965 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research at the University of Notre Dame focused on developing mathematical theory and numerical methods for the diffuse interface method applied to coupled fluid dynamics systems. Over a three year period from September 2022 through August 2025, the grantee will study a hierarchy of coupled flow models including fluid-porous medium interaction,...
The National Science Foundation awarded a $224,923 project grant to the University of Notre Dame under the Mathematical and Physical Sciences program (CFDA 47.049) to develop adaptive time-stepping methods for coupled fluid-structure interaction and fluid-porous medium problems from August 15, 2022 to July 31, 2025. The university will focus on creating monolithic and partitioned numerical methods using the recast Cauchy-one legged theta-like method with a variable time step for fluid-porous...
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...
The Massachusetts Institute of Technology (MIT) received a $320,000 project grant award from the National Science Foundation Division of Chemical, Bioengineering, Environmental, and Transport Systems under the Engineering (47.041) federal grant program. The award will support the development of a unified closure model for computational fluid dynamics capable of accounting for diverse flow phenomena through May 2026. Specifically, MIT will couple fundamental physics and machine learning...
The National Science Foundation (NSF) awarded a $536,019 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Notre Dame's Notre Dame Research division. The grant funds the development of new software that can significantly reduce computational time and storage requirements for climate and weather modeling, which are critical for understanding future climate change impacts. The software will leverage advancements in applied...
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...