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Saeed Salehi
Associate Professor of Fluid Mechanics
Division of Applied Thermodynamics and Fluid Mechanics
Department of Management and Engineering
Linköping University
Background
I am Associate Professor of Fluid Mechanics at Linköping University. I study complex fluid flows using high-fidelity Computational Fluid Dynamics (CFD). My research combines data-driven approaches and machine learning with CFD to develop efficient and reliable tools for simulation, model order reduction, control, and uncertainty quantification.
I am first and foremost a CFD specialist. To me, CFD is more than a black box, and I am particularly engaged in open-source development, especially with OpenFOAM. Additinally, I am also passionate about data-driven modeling, and how it can be combined with CFD. My doctoral research focused on uncertainty quantification of turbulent flows in turbomachinery, where I developed sparse and efficient methods to assess operational and geometrical uncertainties and applied robust optimization under uncertainty. During my postdoc at Chalmers University of Technology, I developed numerical methods, in OpenFOAM, for transient simulations of hydraulic turbines and expanded into reduced-order modeling, studying approaches such as POD, SPOD, DMD, and sparsity-promoting DMD.
More recently, my research has focused on integrating Artificial Intelligence (AI) with CFD. I have investigated flow control using Deep Reinforcement Learning (DRL) in OpenFOAM and developed multi-fidelity physics-informed neural networks (PINNs) for solving PDEs. This line of work places my research at the intersection of high-fidelity CFD and data-driven methods, with the aim of creating efficient, robust, and trustworthy approaches for the simulation, analysis, and control of complex flows.