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...
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 $365,274 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of novel mathematical techniques and algorithms for designing cost-effective space-time sampling strategies and reconstruction methods for time-evolving functions on graphs. A diverse group of researchers from Northern Illinois University will work to analyze and manage various time-evolving processes sampled under realistic conditions 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...
The National Science Foundation awarded a three-year $450,000 Project Grant to Duke University under the Mathematical and Physical Sciences program (CFDA 47.049) beginning July 1, 2023. The grant will support research into small-scale formation in fluid motions and mathematical modeling of chemotaxis. Specifically, the university will analyze fluid mechanics, singularities in solutions to equations modeling weather phenomena and fluids, and the interaction of diffusion, fluid flow and chemotaxis...
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 $120,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research at the University of Georgia to develop nonlinear control and observer designs for flow-transport systems. The three-year award beginning August 15, 2022 will fund the investigation of feedback control approaches to understand mass transport, fluid mixing, and their asymptotic behaviors in flow advection. The research aims to advance knowledge of...
The National Science Foundation (NSF) awarded a $257,692 Project Grant under the Integrative Activities (CFDA 47.083) program to the University of Mississippi. The 5-year award, commencing on December 1, 2024, aims to advance the understanding of pulsatile turbulent flows over rough surfaces and develop new predictive modeling tools. The project will conduct high-resolution simulations to elucidate the detailed flow physics and establish a comprehensive dataset on this phenomenon, which is...
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 project aims to extend and improve the recently developed Azouani-Olson-Titi algorithm as a fast, robust, and computationally inexpensive data assimilation method. Researchers will adapt the technique to dynamically learn model parameters and reconstruct models based on data. They will also numerically test extensions for intermittent observations from fixed or moving sensors. Implementation in large-scale geophysical models is planned to validate the approaches. Outcomes have the potential to significantly advance predictive capabilities for chaotic natural systems through lower-cost data assimilation simulations.