This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) Project Grant award of $299,862 to the University of Nevada, Reno will support research to develop a new framework for integrating machine learning and physics-based computational models to create "digital twins" of dynamic systems. The research aims to address limitations in current hybrid data-driven modeling approaches by embedding neural networks within physics-based models to better account for modeling...
This Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $269,979 to the Santa Fe Institute of Science to conduct collaborative research on developing robust digital twin models that can accurately predict the behavior of complex systems under unexpected conditions. The key objectives are to: Investigate the generalization abilities of digital twins by combining mathematical tools from nonlinear dynamics and machine...
This $265,971 federal Project Grant award is funded by the National Science Foundation (NSF) through its Engineering (CFDA 47.041) program. The award, with a performance period from September 1, 2024 to August 31, 2027, supports research to advance the fundamental understanding of complex multilevel performance and comprehensive environmental impacts of floating offshore wind (FOW) technology. The key objectives are to: 1) develop advanced computational models for simulating FOW turbine dynamics...
The National Science Foundation (NSF) Directorate for Engineering's "Engineering" program (CFDA 47.041) awarded a $735,872 Project Grant to the University of Pittsburgh to develop a novel digital twin modeling framework for evaluating and minimizing greenhouse gas emissions associated with vertical infrastructure operations. The grant will support research to create high-fidelity 3D digital representations of buildings augmented with real-time sensor data, enabling the analysis of...
This $149,983 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070) will support research by Baylor University to develop integrated models and digital twins to assist rural gateway communities near national parks with long-term resilience planning. The research aims to link multisector dynamic models with digital twins to provide data-driven visualizations and projections of future population,...
This $599,262 National Science Foundation award under the Engineering (47.041) program will fund research at Princeton University from June 2023 to May 2026 to advance knowledge in water-shell structure interaction and enable innovative coastal resilience approaches. The research aims to discover efficient hydrodynamic thin-shell structural forms through integrative modeling, experiments, and machine learning. Specific objectives are to determine forms suited for coastal structures like seawalls...
This National Science Foundation (NSF) Project Grant award under the Engineering program (CFDA 47.041) provides $365,447 to the University of Massachusetts to conduct research on modeling the influence of turbulence on flow-induced instabilities of large flexible structures, with a focus on wind turbine blades. The project aims to develop a novel dynamic model for fluid-structure interaction systems that can accurately account for the effects of turbulence on the onset and post-critical behavior...
The National Science Foundation Division of Behavioral and Cognitive Sciences awarded a $298,982 Project Grant to Texas A&M University on September 1, 2021 with a completion date of August 31, 2023. The grant supports the EAGER project "SYNCHRONIZING DECISION-SUPPORT VIA HUMAN- AND SOCIAL-CENTERED DIGITAL TWIN INFRASTRUCTURES FOR COASTAL COMMUNITIES" under the Social, Behavioral, and Economic Sciences program (CFDA 47.075). The grant will fund the development of digital twin...
The National Science Foundation awarded North Carolina State University $282,315 under the Engineering (47.041) federal grant program to develop new digital twin calibration methods using stochastic optimization techniques. The two-year project will contribute to national prosperity by providing robust estimation approaches for parameter calibration of digital twins with large, complex datasets. Key activities include developing stochastic optimization reconciled with statistical theories to...
This $347,172 National Science Foundation project grant supports research at Northeastern University to develop integrated numerical modeling of hurricane impacts on natural and hybrid coastal infrastructure. The Division of Civil, Mechanical, and Manufacturing Innovation award falls under the NSF Engineering program (CFDA 47.041) to foster innovation and excellence in engineering research. Researchers will leverage field data from Hurricanes Laura and Delta in 2020 to model wetland...
This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) project grant award in the amount of $339,337 supports research to enhance the safety and sustainability of floating civil structures through advanced digital twinning technology. The research, led by the University of New Hampshire, aims to develop a novel stochastic system identification framework called Bayesian Load-Agnostic Continuous Estimation for Digital Twinning (B-LACE4DT). This framework is designed to accurately predict uncertain environmental forces, such as wind, waves, and currents, which significantly impact the safety and durability of floating structures like offshore energy infrastructure, floating transportation systems, and floating homes. By integrating physics-based models with real-time data assimilation, the project will validate the B-LACE4DT approach through experimental and operational testing, with the goal of advancing the reliability and predictive capabilities of digital twins for large-scale floating structures. This research also includes an outreach strategy to engage K-12 students, teachers, and university-level participants in STEM workforce development.