Arash's main research interests are in the area of Computational Fluid Dynamics (CFD) and High Performance Computing (HPC). Specifically developing mathematical models and CFD codes to study:
- Compressible flows (gas dynamics)
- Turbulent flows and turbulence modelling
- Multiphase flows and phase change
- Aerodynamics (subsonic to hypersonic)
- Combustion and reacting flows
- Flow through porous media
- Advanced propulsion systems
- Hydrogen-based energy systems
Arash is also interested in Machine Learning (ML) techniques and particularly Deep Neural Networks (DNNs) for engineering and CFD applications.
Supercritical injection modelling: density field of supercritical nitrogen at 136 K injected into a nitrogen filled ambient at 300 K and ~40 bar (LES with OpenFOAM and with real fluid equation of state and transport properties).
LES of an under-expanded air jet with NPR=4.2.
Density gradient of a Mach 7 air flow over a double wedge (OpenFOAM with fourth-order Runge-Kutta temporal discretization).
Flow around a bluff body with an incoming velocity of 60 m/s. Velocity vector visualisation with line integral convolution.