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.

Dr. Gao earned her PhD in Aerospace Sciences and Engineering from University of Toronto Institute for Aerospace Studies in November 2008. Prior to joining UVA in late July 2023, she was a professor at Colorado State University in the Department of Mechanical Engineering, a postdoctoral scientist at Lawrence Berkeley National Laboratory working with two groups: the Applied Numerical Algorithms Group and the Center for Computational Sciences and Engineering


  • 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)
  • Development of Convergent Algorithms of HPC + Data Analytics + Optimization for Aerospace Engineering Designs
  • Development of Quantum Computing Algorithms for Fluids
  • 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
  • PAST RESEARCH EXPERTISE: Adaptive Mesh Refinement, Parallel Computing, Complex Geometry with Embedded Boundary, Mapped Blocks, Fourth-Order CFD Methods for Turbulent Combustion

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.  

    Read More
  • 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.

    Read More
  • 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.