Dr. Pinaki Pal is a research scientist in Argonne’s Energy Systems division. His research interests lie in the areas of computational fluid dynamics (CFD), turbulent combustion modeling, abnormal combustion, advanced internal combustion engines and gas turbines, particle synthesis in spray flames, cycle-to-cycle variability (CCV) in spark-ignition engines, design optimization and machine learning.
At Argonne, he developed a turbulent combustion modeling framework to capture knocking combustion and extended the capability of state of the art models by incorporating fuel composition effects on turbulent flame propagation. He also collaborated on the development of a novel numerical approach called ML-GA, coupling machine learning (ML), genetic algorithms (GA) and high-performance computing, that accelerates simulation-driven design optimization by an order of magnitude (from months to a few days). This work led to a software copyright. In addition, he has developed predictive and computationally efficient CFD modeling tools to capture supersonic combustion in full-scale non-premixed rotating detonation engines (RDEs).
Dr. Pal received his PhD from University of Michigan-Ann Arbor (2015) in Mechanical Engineering, with specialization in turbulent combustion modeling and CFD for low temperature combustion applications in both internal combustion engines and gas turbines. He also holds a Bachelor of Technology in Mechanical Engineering from the Indian Institute of Technology Kharagpur (India) (2011).