Saeed Salehi

Associate Professor of Fluid Mechanics, Docent

Division of Applied Thermodynamics and Fluid Mechanics · Department of Management and Engineering · Linköping University, Sweden

I study complex fluid flows with high-fidelity computational fluid dynamics (CFD), and combine CFD with data-driven methods and machine learning to build efficient and reliable tools for simulation, model reduction, flow control and uncertainty quantification. My applications range from canonical flows to engineering systems such as turbomachinery and energy systems.

I am first and foremost a CFD specialist and an active OpenFOAM developer. My current focus is deep reinforcement learning for active flow control, coupled directly to CFD.

Research themes

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Schematic of a reinforcement learning agent interacting with a turbulent flow simulation through states, rewards and actions

Reinforcement-learning flow control

Deep reinforcement learning coupled with CFD for active control of unsteady and turbulent flows.

Vortex rope in the draft tube of a Francis turbine during shutdown

Hydraulic turbine transients

Resolved simulation of start-up, shutdown and load changes, with new mesh-motion methods in OpenFOAM.

Coherent flow structures of a vortex rope extracted by dynamic mode decomposition

Modal analysis and reduced-order models

POD, SPOD and DMD to extract coherent structures and dominant frequencies from simulation data.

Architecture of a physics-informed neural network with periodic input layer and loss function

Physics-informed neural networks

Multi-fidelity PINNs for solving partial differential equations, including free-surface waves.

Temperature contours on a baseline and a robust-optimum cooled turbine vane

Uncertainty quantification

Sparse and multi-fidelity polynomial chaos methods and robust design under uncertainty.

Kaplan turbine at best efficiency point and at part load with a rotating vortex rope

Flexible hydropower

Flow-induced loads, turbine lifetime and contra-rotating pump-turbines for energy storage.

News

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Sep 2026

The application period for the PhD position in AI-based flow control for hydropower has closed, and assessment and recruitment are under way. We received a large number of applications, so this will take some time. Thank you to all applicants for your patience.

Aug 2026

New review paper: State-of-the-art implementations of PINNs for the lid-driven cavity problem, in Results in Engineering.

Jul 2026

Talk on closed-loop control of the vortex rope in a swirl generator with deep reinforcement learning, and session chair, at the 21st OpenFOAM Workshop in Guimarães, Portugal.

Jun 2026

Appointed Section Editor of the OpenFOAM Journal.

Jun 2026

New PhD position in AI-based active flow control for hydraulic turbines, funded by the ÅForsk Foundation (see the project page). Application deadline: 31 August 2026.

Current project

ÅForsk Early-Career Research Grant

Enabling flexible hydropower operation by AI-based active flow control

Deep reinforcement learning coupled with high-fidelity CFD to suppress harmful flow instabilities in hydraulic turbines at off-design operation. A PhD student is being recruited at Linköping University.

About the project →

Students

Thesis projects and PhD opportunities

I supervise MSc and BSc theses in CFD, OpenFOAM development and machine learning for fluid flows, and I support applications for external PhD and postdoc fellowships.

Opportunities →

Recent publications

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State-of-the-art implementations of PINNs for the lid-driven cavity problem – a critical review with future perspectivesM. Sheikholeslami, S. Salehi, W. Mao, A. Eslamdoost and H. NilssonResults in Engineering, 32, 112399 (2026) DOI
Lifetime analysis of hydro turbines with focus on fatigue damage in a renewable energy system – A reviewM. Nobilo, S. Salehi and H. NilssonRenewable and Sustainable Energy Reviews, 228, 116578 (2026) DOI
Physics-informed neural networks with hard and soft boundary conditions for linear free surface wavesM. Sheikholeslami, S. Salehi, W. Mao, A. Eslamdoost and H. NilssonPhysics of Fluids, 37(8), 087158 (2025) DOI
An efficient intrusive deep reinforcement learning framework for OpenFOAMS. SalehiMeccanica, 60(6), 1673–1693 (2025) DOI
Transfer learning strategies for accelerating reinforcement-learning-based flow controlS. SalehiarXiv preprint arXiv:2510.16016 (2025) arXiv