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Modeling and Digitalization - Mahdi Abkar

Associate Professor Mahdi Abkar

Mahdi Abkar is an Associate Professor and Head of the Fluids & Energy (FLEN) Section in the Department of Mechanical and Production Engineering (MPE) at Aarhus University, Denmark. 

Since 2017, he has been leading the Fluid Mechanics and Turbulence Research Group, with research expertise spanning computational fluid dynamics (CFD), turbulence and transport phenomena, renewable energies, and data-driven modeling and machine learning. He received his PhD in 2014 from the Swiss Federal Institute of Technology in Lausanne (EPFL), followed by a postdoctoral position at EPFL until 2015, and subsequently a postdoctoral appointment at the Center for Turbulence Research (CTR) at Stanford University. His research focuses on the development and validation of advanced computational models to predict the complex interactions between turbulent flows and their environment, with a strong emphasis on energy and process systems. The scope of his work spans from fundamental research in thermo-fluid mechanics and turbulence to applied engineering challenges with direct industrial and societal relevance, particularly in the context of the green transition and digitalization. Current research activities include:

  • Multi-fidelity CFD for complex geometries and multi-physics problems, including turbulence, heat transfer, and multiphase flows
  • Design optimization of thermo-fluid processes to improve efficiency and robustness
  • Data-driven modeling and machine learning for fluid flow applications

Research summary

Process and systems modeling are fundamental enablers for the development, assessment, and large-scale deployment of CO₂ capture and conversion technologies. Robust modeling and digitalization frameworks allow technologies to be designed, evaluated, and scaled in a more efficient, cost-effective, and sustainable manner. Within CORC, I contribute to this mission by advancing integrated, multiscale process modeling. The aim of this research is to strengthen CORC’s modeling and digitalization capabilities and to support the translation of fundamental research into impactful, deployable CO₂ technologies.