Dr. Pinaki Pal is a senior research scientist in the Center for Advanced Propulsion and Power (CAPP) within Argonne’s Transportation and Power Systems (TAPS) division. His research interests broadly lie in the areas of computational fluid dynamics (CFD), turbulent combustion modeling, physics-informed machine learning (ML), multi-fidelity ML, uncertainty quantification (UQ), low temperature combustion, combustion dynamics, extreme combustion events, and high-performance computing for a wide range of applications, such as propulsion (automotive and aerospace), stationary power generation, and manufacturing.
At Argonne, Dr. Pal developed a turbulent combustion modeling framework to capture knocking combustion and extended the state-of-the-art engine CFD models by incorporating fuel composition effects on turbulent flame propagation. He has led multiple numerical studies utilizing this combustion modeling approach in a first-of-its-kind virtual cooperative fuel research (CFR) engine configuration to investigate the impact of gasoline-biofuel blending on knock-limited performance of boosted spark-ignition (SI) combustion and autoignition propensity in advanced compression ignition (ACI) mode. This research works won the ASME International Combustion Engine Division Best Paper Award (in Fuels category) in 2020 and 2021. Another research paper co-authored by Dr. Pal, published in Physics of Fluids (in 2020) and focusing on high-fidelity numerical modeling of noise, vibration, and harshness (NVH) in compression-ignition engines was chosen and highlighted as an Editor’s Pick by the journal on account of its novelty and noteworthy contribution.
Dr. Pal is also leading the research effort on developing novel machine learning (ML) algorithms and reduced-order modeling techniques to accelerate stiff chemical kinetics, turbulent combustion CFD simulations as well as engine design optimization, within Argonne’s Multi-Physics Computation section. These research efforts have led to six software copyrights and one patent (pending). His group’s ML research pioneered the application of neural ordinary differential equations (NODEs) for predicting stiff chemical kinetics of fuels (known as ChemNODE) significantly faster than traditional chemistry solvers, thereby enabling significant acceleration of detailed chemistry calculations in CFD simulations of reacting flows. On the other hand, the ML-based design optimization algorithms co-developed by Pinaki have been widely leveraged by industry for rapid product design at low cost. In January 2021, an innovative ML-based design optimization software technology co-developed by Dr. Pal, known as ML-GA, was commercially licensed by a U.S. startup company, Parallel Works Inc., and integrated within their commercial high-performance computing platform as part of a U.S. DOE Technology Commercialization Fund (TCF) project, thereby offering a powerful one-of-its-kind artificial intelligence-driven design optimization technology across a wide range of U.S. industries, such as automotive, aerospace, oil and gas, building energy and materials, etc. This technology demonstration and commercialization effort won the 2021 HPCwire Readers’ Choice Award (for Best Use of High Performance Data Analytics and Artificial Intelligence) and 2022 Federal Laboratory Consortium (FLC) National Award for Excellence in Technology Transfer. Dr. Pal and his team also won the prestigious R&D 100 Award (in Software/Services category) in 2021 for ML-GA.
Dr. Pal initiated and is leading the computational modeling and research effort at Argonne pertaining to numerical modeling and design of full-scale rotating detonation engines (RDEs), which are promising candidates for stationary power generation and hypersonic propulsion. His work pioneered the application of chemical explosive mode analysis (CEMA) technique for combustion mode detection and quantification of non-ideal deflagrative losses in RDEs. Dr. Pal received the Impact Argonne (formerly Pacesetter) Award in 2019 and 2021 for his computational research on RDEs, gas turbine engines, and hypersonic propulsion.
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 turbine combustors. He also holds a Bachelor of Technology in Mechanical Engineering from the Indian Institute of Technology Kharagpur (India) (2011). Pinaki is currently a member of the AIAA Technical Committee on Pressure Gain Combustion and SAE Technical Committee on Engine Combustion. He is also a member of the American Society of Mechanical Engineers (ASME), American Physical Society (APS), and The Combustion Institute.
Active Research Areas of Interest:
- High-fidelity CFD modeling for investigation of combustion dynamics in rotating detonation engines (RDEs)
- Deep learning techniques for accelerating chemical kinetics and turbulent combustion simulations
- Flamelet modeling of non-premixed and partially-premixed turbulent combustion in stationary power generation and high-speed propulsion systems
- Analysis of stochastic combustion instabilities using CFD simulations and machine learning
- Active machine learning algorithms for rapid design optimization of energy systems and manufacturing processes
- Physics-informed data-driven near-wall turbulence modeling for gas turbine film cooling flows
- Uncertainty quantification of deep learning models for prediction of fuel properties
- Deep learning-based multi-fidelity surrogate modeling and uncertainty quantification of the impact of manufacturing uncertainties on the film cooling efficiency of gas turbines
- CFD-driven numerical investigation of fuel-engine interactions and co-optimization of advanced engines and alternative fuels
- Development of non-equilibrium wall models and combustion models in a high-order spectral element CFD code, Nek5000, for multi-mode engine simulations
- Computational modeling of nanoparticle synthesis in flame spray pyrolysis (FSP) reactors
Honors and Awards
- Federal Laboratory Consortium (FLC) National Award for Excellence in Technology Transfer (2022)
- HPCwire Readers’ Choice Award, Best Use of High Performance Data Analytics & AI (2021)
- R&D 100 Award, Software/Services Category (2021)
- Best Paper Award (Fuels Track), ASME Internal Combustion Engine Division (2021)
- Federal Laboratory Consortium (FLC) Excellence in Technology Transfer Award, Midwest Region (2021)
- Impact Argonne Award for Innovation, Argonne National Laboratory (2021)
- Best Paper Award (Fuels Track), ASME Internal Combustion Engine Division (2020)
- Editor’s Pick, Physics of Fluids Journal (2020)
- Pacesetter Award for Program Development, Argonne National Laboratory (2019)
- 1st place in ‘Fluid Dynamics, Acoustics, and Thermal Science’ technical session, Engineering Graduate Symposium (EGS), University of Michigan (2015)
- Mechanical Engineering Graduate Fellowship, University of Michigan (2011-2012)
- Institute Silver Medal, Rank 1 in Mechanical Engineering Department, Indian Institute of Technology (IIT) Kharagpur (2011)