Matteo Baldelli received his Ph.D. in Industrial Engineering from the University of Rome Tor Vergata, where his doctoral research focused on electrohydrodynamic simulations at the nanoscale. His current research focuses on the development of advanced modeling and simulation methodologies for sustainable energy systems, with particular emphasis on the integration of Computational Fluid Dynamics (CFD), machine learning, and data-driven approaches.
His research interests include thermochemical conversion processes, fluidized-bed reactors, hydrogen and biomass energy systems, digital twins, and similarity-based methodologies for reactor design and scale-up. He develops physics-informed surrogate models and artificial intelligence techniques to improve the prediction, optimization, and control of complex multiphase systems while reducing computational cost.
His current research also addresses the integration of high-fidelity numerical simulations with experimental validation and uncertainty-aware machine learning, aiming to support the design, optimization, and real-time operation of next-generation energy conversion technologies.
Matteo Baldelli received his Ph.D. in Industrial Engineering from the University of Rome Tor Vergata, where his doctoral research focused on electrohydrodynamic simulations at the nanoscale. His current research focuses on the development of advanced modeling and simulation methodologies for sustainable energy systems, with particular emphasis on the integration of Computational Fluid Dynamics (CFD), machine learning, and data-driven approaches.
His research interests include thermochemical conversion processes, fluidized-bed reactors, hydrogen and biomass energy systems, digital twins, and similarity-based methodologies for reactor design and scale-up. He develops physics-informed surrogate models and artificial intelligence techniques to improve the prediction, optimization, and control of complex multiphase systems while reducing computational cost.
His current research also addresses the integration of high-fidelity numerical simulations with experimental validation and uncertainty-aware machine learning, aiming to support the design, optimization, and real-time operation of next-generation energy conversion technologies.