A research overview
Our research focuses on the simulation and analysis of multi-scale multi-physics fluid flow problems that leverage high performance computing (HPC) platforms (upto millions of computing units). Specifically, the work involves:
- Development of HPC centric numerical methods and algorithms for solving partial differential equations.
- Design of machine learning methods to analyze and model data generated from simulations.
- Investigation of turbulent flow problems that arise in combustion systems, high-speed aerodynamics and environmental flows.
Our current research includes:
- A scalable asynchronous computing method to solve partial differential equations on massive supercomputers (numerical methods and high performance computing).
- Development of efficient simulation algorithms and workflows for fluid flow solvers (high performance computing).
- Simulations of turbulent combustion phenomena in gas turbine and scramjet engines (energy/aerospace).
- Development of distributed machine learning methods for prediction of anomalous/extereme events in scientific phenomena.