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Pinaki Pal

Senior Research Scientist


Dr. Pinaki Pal is a Senior Research Scientist in the Department of Advanced Propulsion and Power (DAPP) within Argonne’s Transportation and Power Systems (TAPS) division. His research interests broadly lie in the areas of computational fluid dynamics (CFD), turbulent multiphase combustion modeling, near-wall turbulence modeling, scientific machine learning (SciML), uncertainty quantification (UQ), multifidelity modeling, high-order numerical methods, high performance computing (HPC), alternative fuels (hydrogen, ammonia, biofuels), and extreme/rare combustion events with relevance to a wide range of applications pertaining to design and optimization of propulsion/power systems (gas turbine engines, rotating detonation engines, piston engines) 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 currently leading the research effort on developing novel machine learning (ML) algorithms and reduced-order modeling techniques to accelerate stiff chemical kinetics computations, turbulent combustion CFD simulations as well as engineering 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, ML-GA and ActivO, co-developed by Pinaki have been widely leveraged by industry to accelerate product design at low cost. In January 2021, 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 2022 Federal Laboratory Consortium (FLC) National Award for Excellence in Technology Transfer. Dr. Pal and his team won the 2021 R&D 100 Award for ML-GA and were selected as 2023 R&D 100 Finalist for ActivO.

Dr. Pal also leads 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. On the other hand, Pinaki is leading turbulent combustion modeling efforts to enable predictive CFD simulations of hydrogen/ammonia-fueled stationary gas turbines, with a particular focus on extreme events (flame flashback, lean blow-out) and NOx emissions. Dr. Pal received the Impact Argonne Award in 2019, 2021, 2022, and 2023 for his computational research on RDEs, gas turbine engines, and hypersonic propulsion. In 2022-2023, he participated in the Argonne 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 led 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.  


  • PhD, Mechanical Engineering, University of Michigan - Ann Arbor, USA (2015)
  • MS, Mechanical Engineering, University of Michigan - Ann Arbor, USA (2012)
  • Bachelor of Technology (B. Tech), Mechanical Engineering, Indian Institute of Technology (IIT) Kharagpur, India (2011)

Active Research Areas of Interest

  1. High-fidelity CFD modeling of combustion dynamics in rotating detonation engines (RDEs) and RDE-turbomachinery systems for power generation and rocket propulsion applications
  2. Turbulent combustion models for prediction of flame flashback in hydrogen-enriched gas turbine engines
  3. Predictive chemical kinetic and CFD modeling tools for ammonia & ammonia/hydrogen combustion and NOx emissions in gas turbine engines
  4. Neural ODE based physics-informed deep learning (ChemNODE) for accelerating detailed chemistry computations
  5. ML-assisted flamelet modeling of turbulent non-premixed and partially-premixed combustion
  6. On-the-fly multi-regime turbulent combustion modeling of rotating detonation engines and gas turbine engines
  7. Physics-guided data-driven near-wall turbulence modeling for gas turbine film cooling flows
  8. Active machine learning algorithms for rapid design optimization of energy systems and manufacturing processes
  9. Deep learning multi-fidelity surrogate modeling and UQ of the impact of surface roughness on the efficiency of gas turbine film cooling
  10. High-order spectral-element simulations of gas turbine combustors and RDEs using Nek5000/NekRS
  11. Integration of non-equilibrium wall models and combustion models in high-order spectral-element CFD solver, Nek5000, for multi-mode engine simulations
  12. Generative AI for fuel design
  13. UQ of deep learning models for prediction of fuel properties
  14. Causal analysis of combustion instabilities using high-fidelity CFD simulations and SciML 
  15. CFD-driven numerical investigation of fuel-engine interactions and co-optimization of advanced engines and alternative fuels
  16. CFD 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 HPC Collaboration (2023)
  • HPCwire Editors’ Choice Award, Best HPC Collaboration (2023)
  • R&D 100 Award Finalist, ActivO (2023)
  • Impact Argonne Award for Innovation, Argonne National Laboratory (2023)
  • 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 Winner, ML-GA (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)

Patent & Invention Disclosure

Professional Activities

  • Associate Editor: Frontiers in Thermal Engineering journal
  • Member: Pressure Gain Combustion (PGC) Technical Committee, American Institute of Aeronautics and Astronautics (AIAA
  • Member: Combustion, Fuels & Emissions (CFE) Technical Committee, American Society of Mechanical Engineers (ASME) International Gas Turbines Institute
  • Member: Engine Combustion Technical Committee, Society of Automotive Engineers (SAE)
  • Member: USA Central States Section of the Combustion Institute
  • Member: Internal Combustion Engine Division (ICED), ASME
  • Member: American Physical Society (APS) 
  • Guest Editor: Computational and Data-driven Modeling of Turbulent Combustion and Engine Combustion Dynamics (Special Issue), Energies Journal(2021): https://​www​.mdpi​.com/​j​o​u​r​n​a​l​/​e​n​e​r​g​i​e​s​/​s​p​e​c​i​a​l​_​i​s​s​u​e​s​/​c​o​m​b​u​s​t​i​o​n​_​e​n​gines
  • Co-Editor: Current Status and Key Future Trends in Heat Engines (Special Issue), Frontiers in Thermal Engineering Journal (2024)
  • Session Chair: 38th International Symposium on Combustion (2021), ASME ICEF (2019-2023), ASME Turbo Expo (2019, 2022-2024), SAE World Congress (2023), US National Combustion Meeting (2019, 2021), AIAA SciTech (2020, 2024), ASME IMECE (2014)