Bio

University of Toronto, Aerospace Science and Engineering, PhD 2008Lawrence Berkeley National Laboratory, Applied Numerical Algorithms, Postdoc 2009-2011

"A Simulation Is Only As Good As Its Algorithms"

Xinfeng Gao

Xinfeng Gao's research is focused on the development of high-performance computing (HPC) computational fluid dynamics (CFD) algorithms for a wide range of applications in aerospace and mechanical engineering, involving shock waves, turbulence, combustion, plasma, and multifluids. Her research is categorized into three areas:

  1. The development of numerical algorithms for complex partial differential equations (PDEs),
  2. HPC and software engineering,
  3. The development of enhanced CFD modeling & simulation (M & S) tools by data analytics.

Integrating these research capabilities together, she has been collaborating with national labs, universities, aerospace industry and software companies to transform research across scientific disciplines for solving complex problems of national and societal interest. Her research has been supported by the National Science Foundation, Air Force Office of Scientific Research, Department of Energy Laboratories, e.g., Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), etc, and Aerospace Industry, e.g., the Boeing Company, the Woodward Inc., etc.

Dr. Gao earned her PhD in Aerospace Science and Engineering from the University of Toronto Institute for Aerospace Studies in November 2008. After graduation, she became a postdoctoral scientist with two groups in LBNL from January 2009 to August 2011: the Applied Numerical Algorithms Group and the Center for Computational Sciences and Engineering. Prior to joining UVA in late July 2023, she was a professor at Colorado State University in the Department of Mechanical Engineering, where she started as an assistant professor in mid-August 2011 and established the CFD and Propulsion Laboratory. She spent her sabbatical from August 2018 to May 2019 reinvigorating her research by working with the Mathematical Algorithms & Computing Group in LLNL's Center for Applied Scientific Computing.

Awards

  • National Science Foundation (NSF) Mid-Career Advancement (MCA) Award 2022
  • Outstanding Achievement Award, Women in Aerospace 2022

Research Interests

  • Development of High-Order CFD Methods for High-Speed Flows (e.g., Supersonic and Hypersonic Regimes)
  • Parallel Adaptive Algorithms for Both Spatial and Temporal Domains
  • Development of Convergent Algorithms of HPC + Data Analytics + Optimization for Aerospace Engineering Designs
  • Applications of Above Algorithms to Derive Reduced-Order Models for Large Eddy Simulation, Improve Fundamental Understanding of Physics in Complex Fluid Dynamical Systems, or Innovate Practical Propulsion Devices
  • Exploratory Research: Quantum Computing Algorithms for Fluids
  • Base Research Expertise: Adaptive Mesh Refinement, Parallel Computing, Complex Geometry with Embedded Boundary, Mapped Grids, Fourth-Order Finite-Volume CFD Methods for Complex Fluid Dynamics (e.g., Turbulence, Chemical Reactions, and/or, Plasma)

In the News

Courses Taught

  • MAE 6720 Computational Fluid Dynamics Fall 2023

Featured Grants & Projects

  • NSF Mid-Career Advancement (MCA) Award: Developing Data-Assimilation Capability in Engineering Simulation Software Systems

    Directorate for Technology, Innovation and Partnerships Translational Impacts


    This MCA project aims to transfer computational fluid dynamics (CFD) and data assimilation (DA) technology from a research setting to commercial industry, partnering with Convergent Science who has experience with fast, predictive CFD for innovation in engineering. This partnership will allow Gao to make CFD+DA a design and analysis tool, accessible to non-experts. Industrial engineers will apply this reliable, accurate, and efficient technology in their product-design workflows for enhanced productivity.  

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  • NSF Collaborative Research: Adaptive Data Assimilation for Nonlinear, Non-Gaussian, and High-Dimensional Combustion Problems on Supercomputers

    Division of Mathematical Sciences


    The project creates a new adaptive data assimilation methodology by confronting the mathematical challenges of applying data assimilation to combustion.

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  • AFOSR Research: Accurately-Posed Direct Numerical Simulation of Turbulent Combustion at High-Reynolds Numbers by Data Assimilation

    Energy, Combustion, and Non-Equilibrium Thermodynamics


    This project applies Bayesian data assimilation algorithms with our high-order inhouse CFD software (named Chord) for improving the predictability of turbulent reacting flows in combustors relevant to Air Force, featuring challenging operating conditions and complex geometries.