Saleh Rezaeiravesh

Saleh Rezaeiravesh is a Lecturer in Engineering Simulation and Data Science within the Department of Mechanical and Aerospace Engineering. He holds a PhD in Scientific Computing (Numerical Analysis) from Uppsala University (2018) and previously worked at the FLOW Centre, KTH Royal Institute of Technology. His research focuses on Computational Fluid Dynamics (CFD), uncertainty quantification (UQ), and data-driven techniques applied to turbulent flows. He teaches courses such as Numerical Methods & Computing and Data-driven Modelling & Simulation. He is part of the active Fluids Research Group, contributing to projects on turbulent flow dynamics and CFD applications.

Education: PhD in Scientific Computing (Uppsala University, 2018); MSc and BSc in unspecified fields.

Research interests include scale-resolving simulations of wall-bounded turbulent flows, Bayesian optimization, and multifidelity modeling. He develops tools like the UQit Python package for uncertainty quantification in CFD. His work aligns with UN Sustainable Development Goals through contributions to efficient fluid dynamics modeling, aiding sustainable engineering solutions.

Recent research trends in his articles emphasize numerical uncertainties, precision effects in simulations, and advanced machine learning techniques for turbulence modeling. His contributions address challenges in CFD accuracy, robustness, and computational efficiency. He actively supervises PhD students in areas like uncertainty quantification and data-driven optimization of fluid dynamics problems.

He collaborates on interdisciplinary projects, including porous-fluid systems and flow control using Bayesian methods. His team participates in global research networks, with notable collaborations on turbulence statistics and LES modeling.