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Pinaki Pal

Senior Research Scientist

Biography

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), high performance computing (HPC), multiphysics & multiscale modeling of turbulent multiphase combustion, alternative fuels, extreme combustion events, physics-informed machine learning (ML), multi-fidelity ML, and uncertainty quantification (UQ) for a wide range of applications, such as propulsion (automotive and aerospace), stationary power generation, and manufacturing processes.

At Argonne, Dr. Pal developed a turbulent combustion modeling framework to predict 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. These 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 and proper orthogonal decomposition (POD) analysis 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. 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 to accelerate 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 R&D effort at Argonne pertaining to computational 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. Currently, he is a participant in Argonne’s Launchpad Program which is designed to provide motivated early- and mid-career researchers with enhanced training and mentoring for developing multimillion-dollar sponsored research programs. As part of the Launchpad Program, he is leading Argonne’s lab-wide effort to develop a research program centered around advancing end-use applications of hydrogen and other carbon-neutral fuels (such as, ammonia) in advanced gas turbine and rotating detonation engines to enable deep decarbonization of the stationary power generation sector by leveraging the expertise of his multi-disciplinary team in fuel/combustion science, computational modeling, HPC, experimental diagnostics, and scientific ML along with Argonne’s unique state-of-the-art advanced photon source (APS) and leadership supercomputing user facilities.  

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 an Associate Editor of Frontiers in Thermal Engineering (Heat Engines) journal, 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) and The Combustion Institute.  

Active Research Areas of Interest:

  1. High-fidelity CFD modeling for investigation of combustion dynamics in rotating detonation engines (RDEs) and RDE-turbine integrated systems
  2. Physics-informed deep learning techniques for accelerating chemical kinetics and turbulent combustion simulations
  3. Reduced-order flamelet modeling of non-premixed and partially-premixed turbulent combustion in gas turbine and ramjet engines
  4. Dynamic adaptive multi-regime turbulent combustion modeling of gas turbine engines
  5. Physics-guided data-driven near-wall turbulence modeling for gas turbine film cooling flows
  6. Active machine learning algorithms for rapid design optimization of energy systems and manufacturing processes
  7. Causal analysis of combustion instabilities using high-fidelity CFD simulations and machine learning
  8. UQ of deep learning models for prediction of fuel properties
  9. Deep learning multi-fidelity surrogate modeling and UQ of the impact of surface roughness on the efficiency of gas turbine film cooling 
  10. CFD-driven numerical investigation of fuel-engine interactions and co-optimization of advanced engines and alternative fuels
  11. Integration of non-equilibrium wall models and combustion models in a high-order spectral element CFD code, Nek5000, for multi-mode engine simulations
  12. Computational modeling of nanoparticle synthesis in flame spray pyrolysis (FSP) reactors

Google Scholar: https://​schol​ar​.google​.com/​c​i​t​a​t​i​o​n​s​?​u​s​e​r​=​5​z​p​s​l​S​k​A​A​A​A​J​&​hl=en

Awards and Recognition

  • HPCwire Readers’ Choice Award, Best Use of HPC in Industry (2022)
  • Cohort 7 Selectee of Launchpad Program, Argonne National Laboratory (2022-2023)
  • Impact Argonne Award for Program Development, Argonne National Laboratory (2022)
  • 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)
  • HPCwire Editors’ Choice Award, Best Use of HPC in Automotive (2019)
  • 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 the Department of Mechanical Engineering (B. Tech), Indian Institute of Technology (IIT) Kharagpur (2011)