Project Grant 2552483
- Federal Project Grant Summary The University of Pittsburgh received a $543,305 Project Grant from the National Science Foundation (NSF) Engineering program (CFDA 47.041) awarded February 1, 2026, with completion scheduled for January 31, 2029. This research project delivers computational modeling and simulation services focused on applying hybrid quantum-classical computing methods to address fluid dynamics problems, specifically turbulence in complex flows. The project develops two primary...
- Federal Project Grant Award Summary The National Science Foundation (NSF) Division of Chemical, Bioengineering, Environmental, and Transport Systems awarded $325,089 to the University of Pittsburgh under the Engineering program (CFDA 47.041) on August 15, 2025, with a completion date of July 31, 2028. This project grant funds fundamental research investigating how collective active migration of small marine organisms generates large-scale flow structures, termed aggregation-scale flows, that may...
- 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...
- Federal Project Grant Award Summary The University of Pittsburgh received a $449,998 Project Grant awarded on December 1, 2025, from the National Science Foundation (NSF) Division of Chemical, Bioengineering, Environmental, and Transport Systems under the Engineering program (CFDA 47.041). The three-year project, concluding November 30, 2028, will develop a novel computational framework for simulating thermal transport near semiconductor heterojunctions—nanometer-scale regions where different...
- Federal Grant Award Summary The University of Pittsburgh received a $465,000 Project Grant from the National Science Foundation's Division of Engineering Education and Centers under the Engineering program (CFDA 47.041), awarded September 1, 2025, with completion targeted for August 31, 2028. This three-year Research Experiences for Undergraduates (REU) Site provides an immersive 10-week summer research program for ten undergraduate students annually (2026-2028), focusing on particle-based...
- 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...
- Federal Grant Award Summary The University of Pittsburgh received a $383,203 Project Grant from the National Science Foundation (NSF) Office of Integrative Activities under the Geosciences program (CFDA 47.050) to develop a machine learning-based emulator that predicts water isotope patterns in global climate models. Funded from November 1, 2025 through October 31, 2028, this collaborative research initiative combines expertise in climate science and artificial intelligence to create an...
- The University of Pittsburgh received a $424,546 project grant award from the National Science Foundation Division of Mathematical Sciences on July 15, 2021 to support work on the TIME ACCURATE PREDICTION OF FLUID MOTION project through June 30, 2024. The grant is part of the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these scientific fields and strengthen the nation's scientific enterprise through increasing knowledge and enhancing...
- 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...
- Federal Project Grant Award Summary The University of Pittsburgh received a $328,712 Project Grant awarded July 15, 2025, through the Division of Mathematical Sciences under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). This award supports fundamental research in geometric function theory, focusing on the analysis, geometry, and topology of mappings and functions with limited differentiability, including convex functions, Sobolev functions, and Lipschitz and...
The University of Pittsburgh received a $372,882 Project Grant from the National Science Foundation (NSF) Division of Chemical, Bioengineering, Environmental, and Transport Systems (CFDA 47.041) awarded February 1, 2026, with completion targeted for January 31, 2029. This grant supports fundamental research on turbulence dynamics, specifically investigating self-competition and weak asymmetry phenomena in fluid turbulence through data-enabled diagnostics and machine learning approaches. The research develops a physics-guided, data-driven framework to uncover previously unexplained mechanisms controlling energy transfer across different scales of turbulent motion—a critical gap in current turbulence theory that relies on oversimplified statistical tools. The project delivers research outputs and educational products designed to advance both scientific understanding and practical applications. Deliverables include development of improved turbulence models applicable to weather prediction and engineering system design, production of publicly available software tools, and training of graduate and undergraduate students in engineering, data science, and physics disciplines. The research outcomes will support broader adoption of artificial intelligence and machine learning methods in fluid dynamics and contribute to advanced manufacturing applications in the transportation sector.Federal Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $372.9k | 1/26/26 |