University of Virginia Main Campus

NSF Awards · FY 2023 · 2023-10

The broader impact/commercial potential of this Partnerships for Innovation - Mid-Career Advancement (PFI (MCA)) project seeks to reduce discrepancies between results from computer simulations and those from physical experimentation. Data assimilation (DA) technology will be developed to simulating complex engineering systems that may uncover the root causes for such discrepancies and place an industry back on track in their product design workflow. By partnering with commercial industry, this PFI (MCA) project will make the DA technology accessible to practicing engineers working in areas of computational fluid dynamics (CFD), accelerating advances for innovative designs. This project may also enhance the academic curricula at both undergraduate and graduate levels by enabling discussion of engineering design and analysis use cases. The proposed PFI (MCA) project aims to transfer and scale up the CFD+DA technology from a research setting to commercial industry. This project proposes rigorous methods for uncovering the causes of discrepancies between computer simulation and physical experimentation and quantifying the uncertainty in a prediction. For analysis of engineering fluid flows, correct use of DA requires significant expertise in both DA and CFD algorithms, presenting technical barriers for engineers. This project will make CFD+DA a design and analysis tool, accessible to non-experts. Industrial engineers will assess the technology in current product-design workflows. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.