This National Science Foundation project grant of $167,505 will fund research on data assimilation techniques for turbulent fluid flows from July 2022 to June 2025 at the University of Nebraska-Lincoln. Under the Mathematical and Physical Sciences program (CFDA 47.049), the grant supports the development and testing of new algorithms to incorporate observational data into mathematical models of complex multi-scale phenomena like weather, ocean dynamics, and groundwater flow. Specifically, the...
The National Science Foundation (NSF) Geosciences program has awarded a $236,421 EAGER grant to the University of Utah to develop new approaches for representing land-atmosphere interactions in numerical weather prediction and climate models. The goal is to generalize the Monin-Obukhov Similarity Theory (MOST) to better account for turbulence anisotropy and facilitate its application across diverse surface configurations, such as mountainous terrain and forests. This research aims to improve the...
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 Project Grant from the National Science Foundation Division of Mathematical Sciences provides $149,999 to support the development of new algorithms for ensemble data assimilation in large-scale applications that do not rely on Gaussian approximations. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the award will be carried out from August 1, 2022 to July 31, 2025 by researchers at the University of Colorado Boulder. The project aims to advance data assimilation...
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's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $184,985 to Brigham Young University (BYU) to conduct research on the dynamics of weakly hyperbolic dynamical systems. The 3-year project, running from September 1, 2024 to August 31, 2027, will investigate the statistical properties of these chaotic systems that are inherently difficult to predict. The research aims to advance understanding of the existence and...
This $399,583 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research to develop numerical algorithms that can estimate solutions to partial differential equations (PDEs) without full boundary condition information. The research aims to enable improved modeling and forecasting capabilities across various applications, including meteorology, biology, and engineering design. The primary awardee, Texas...
This five-year $154,264 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research and educational activities at Drexel University related to uncertainty quantification, long-time statistics, and singularity formation in fluid flow models. The Principal Investigator will address topics including parameter recovery from sparse noisy observations using Bayesian inverse problems and Markov chain Monte Carlo algorithms applied to...
This Project Grant award for $257,457 from the National Science Foundation (NSF) Division of Mathematical Sciences supports research by the University of Alabama at Birmingham (UAB) to investigate three key problems in fluid mechanics. The overarching objective is to utilize a novel mathematical framework developed by the Principal Investigator to study the spatial intermittency of turbulent flows, which is critical for understanding phenomena like vortex structures in turbulence. The three...
This $313,202 federal Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports a 5-year research project to model and analyze particle-laden fluid flows. The project involves developing novel mathematical models and computational methods to better understand the complex physics underlying particle-laden flows, which have important applications in industries like food processing, mining, and environmental remediation. Key...
This three-year National Science Foundation project grant of $174,703 supports research at Brigham Young University to develop new data assimilation techniques for turbulent fluid flow modeling and prediction. The award is made through the Mathematical and Physical Sciences program, which aims to strengthen the nation's scientific enterprise through increased mathematical and physical sciences knowledge and understanding of major national challenges.
Specifically, the university researchers will extend and improve the recently developed Azouani-Olson-Titi data assimilation algorithm to enable dynamic model learning and solution capturing for complex multi-scale phenomena like weather, ocean dynamics, and groundwater flow. In addition to further refining the computationally inexpensive AOT technique, the team will explore using it to reconstruct mathematical models from data and recover system parameters and states. They will test applications in geoscience domains like coupled ocean-atmosphere modeling and soil moisture transport. The project also seeks to advance theoretical justification for AOT algorithm convergence under real-world noisy and sparse observational conditions. Overall, the work is intended to significantly enhance predictive capabilities for chaotic natural systems through innovative data assimilation methodologies.