Project Grant 2341393
- 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...
- 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 National Science Foundation (NSF) Project Grant award under CFDA 47.041 (Engineering) provides $490,525.00 to The Regents of the University of Minnesota, doing business as the Office of Sponsored Projects Administration, to conduct research on stochastic modeling of turbulence over rough surfaces. The goal is to develop a new model that can accurately represent the velocity field and dynamics of near-surface turbulence, which is critical for applications like wind energy and pollution...
- This $300,000 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop physics-guided generative artificial intelligence models for inverting chaotic advection-diffusion dynamics. The research aims to enable more accurate source identification from limited observations of complex physical processes like pollution transport, virus spread, and wildfire evolution, which are...
- This $554,609 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) is funding the development of a novel "Immersed Boundary-Modeled Large Eddy Simulations" framework at Arizona State University (ASU). The project aims to create accurate, computationally efficient models for predicting the dynamics of turbulent fluid flows around complex and deforming surfaces, with applications in areas such as sediment transport, biomedical flows, and...
- 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 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 $325,003 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports fundamental research to understand the flow patterns and turbulent structures of bubble plumes. The project aims to develop a comprehensive model of the entrainment process in bubble plumes under different conditions, which could help improve computer simulations of bubble plumes in natural and engineered systems. The research will include laboratory experiments to measure bubble...
- 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...
- This $220,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to advance the understanding of how soft, deformable particles, such as oil droplets and cells, move through complex environments consisting of narrow constrictions and obstacles. The award to Emory University will systematically investigate the flow of bubbles and droplets through obstacle arrays, studying the effects of particle surface tension, obstacle density, size, and...
This National Science Foundation (NSF) Engineering program (CFDA 47.041) award of $200,000 to the San Diego State University Research Foundation provides funding for a 2-year project to develop a novel framework for computing and utilizing the probability distribution of the domain of dependence in turbulent, uncertain flows. The project aims to enhance the ability to track and locate the sources of scalar fields like chemical leaks, oil spills, and heat emissions, which are often obscured by complex environmental conditions. By aggregating multiple domains of dependence, the project will design a geometric approach to more accurately and reliably identify the origin of these pollutants, leveraging the probabilistic nature of the data to better interpret sensor measurements. This research has the potential to advance environmental monitoring, weather forecasting, and disaster response capabilities.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $200.0k | 5/21/24 |