Mahdi Abkar leads an interdisciplinary research group focused on fluid mechanics, turbulence, and transport phenomena, with applications ranging from renewable energy systems to complex industrial processes. His work is centered on the development and validation of advanced computational fluid dynamics (CFD) models, including multi-fidelity approaches that capture complex interactions such as turbulence, heat transfer, and multiphase flows.

A key aspect of his research is the integration of data-driven modeling and machine learning into thermo-fluid systems. By combining physics-based modeling with modern data science techniques, his group works on improving predictive capabilities, optimizing system performance, and enabling more robust and scalable engineering solutions.

Within CORC, Abkar contributes to strengthening modeling and digitalization efforts for CO₂ capture and conversion technologies. His research focuses on integrated, multiscale process modeling, which supports the design, evaluation, and scale-up of efficient and sustainable CO₂ technologies. This work plays an important role in bridging fundamental research and real-world deployment.

As part of the Modeling & Digitalization program, Abkar is involved in projects such as the development of unified, AI-driven frameworks for CO₂ conversion. These efforts aim to integrate experimental data across systems and enable predictive modeling and process optimization. For example, ongoing work explores how machine learning can be used to standardize data and improve the efficiency of microbial CO₂ conversion processes, ultimately accelerating the development of scalable solutions.