PIPages
Labs

Xinfeng Gao

University of Virginia, VA·Engineering

Engineers rely on computer simulations to design products, but simulation results often disagree with physical experiments. When they do, design work can stall because the cause of the mismatch is unclear.

Fields

Engineering

Computer Science

Mathematics & Statistics

Topics

data assimilation

computational fluid dynamics

uncertainty quantification

Grants
1 known grant, through Aug 2027

What they work on

  1. Develop rigorous data assimilation methods that combine measurements with computational fluid dynamics (CFD) simulations, to uncover why simulated fluid flows disagree with physical experiments.
  2. Quantify the uncertainty in CFD predictions of engineering fluid flows by using data assimilation within simulation software systems.
  3. Package CFD plus data assimilation into a tool usable by non-expert engineers, and have industry partners assess it within their current product-design workflows.

More about the research

Data assimilation (DA) is a set of methods for combining measured data with simulations. It can reveal why a simulation and an experiment disagree and estimate how uncertain a prediction is.

Why it matters

Practicing engineers who design products involving fluid flow could find out why their simulations disagree with tests and how much to trust a prediction. Easy-to-use data assimilation in commercial CFD software could reduce costly design setbacks and speed up the development of new designs.

Known grants

From NIH and NSF research grants. Labs often have other funding we can't see.

PFI (MCA): Developing Data-Assimilation Capability in Engineering Simulation Software Systems

NSF · Oct 2023 – Aug 2027

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The school

University of Virginia: 43 labs on PI Pages, $814M a year on research, 413 research doctorates a year.

More about University of Virginia

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