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Sibendu Som

Director of the Center for Advanced Propulsion and Power Systems and Director of the AI Applications Initiative

Biography

Dr. Sibendu Som is the Interim Director for the Center for Advanced Propulsion and Power (CAPP) in the Transportation and Power Systems Division at Argonne National Laboratory. From 2017 to 2021, he was manager of the Multi-physics Computational Research section. That team develops predictive simulation capabilities for industry in order to develop advanced high-efficiency, low-emission propulsion systems. Several computational models that the team developed are part of commercial computational codes. The group leverages the latest developments in high-performance computing (HPC) and machine learning (ML) to accelerate these high-fidelity simulations.

Dr. Som has more than a decade of experience in multi-physics and multi-scale modeling of piston engines and gas turbines using HPC systems and artificial intelligence/ML tools. His current research focuses on the development of two-phase models and reacting flow models for propulsion and manufacturing applications. He also manages a large group of scientists working on topics ranging from piston engines, gas turbines, energy storage, offshore wind, external aerodynamics, and more. 

Dr. Som is a cofounder of Argonne’s Virtual Engine Research Institute and Fuels Initiative (VERIFI) program, which is aimed at providing predictive simulations for original equipment manufacturers (OEMs). His research interests include working at the interface of fundamental and applied research, and he is driven to move the needle” through large multi-institutional programs. 

Dr. Som also has an affiliation with the University of Chicago as a Senior Scientist at the Consortium for Advanced Science and Engineering. He is a current participant in Argonne’s Launchpad Program, 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, his team is developing a virtual Carbon Management Center at Argonne to accelerate optimization and adoption of carbon capture techniques by leveraging HPC and ML tools.

More information is available on Dr. Som’s LinkedIn and Google Scholar profiles.

Education

  • Strategic Laboratory Leadership Program training — Booth School of Business, University of Chicago, 2018
  • Postdoctoral Appointee — Energy Systems Division, Argonne National Laboratory, 2010
  • Ph.D. in Mechanical Engineering — University of Illinois at Chicago, 2009
  • M.S. in Mechanical Engineering — University of Illinois at Chicago, 2005
  • B.Eng. in Mechanical Engineering — Osmania University, India, graduated with honors, 2003

Select Honors

  • Cohort 6 Selectee of Argonne’s Launchpad Program, 2021
  • Fellow of the Society of Automotive Engineers (SAE), Inducted 2021
  • Recipient of the American Society of Mechanical Engineers (ASME) George Westinghouse silver medal for eminent achievement and distinguished service, 2020
  • HPCwire Editor’s Choice award for Best Use of HPC in Industry” and Best Use of HPC in Energy,” Supercomputing 2020, November 2020
  • Recipient of the University of Illinois at Chicago Distinguished Alumni Award” for the College of Engineering, 2020
  • Outstanding Postdoctoral Supervisor Award, Argonne National Laboratory, 2016
  • ASME Internal Combustion Engine Division Outstanding Presenter” award, 2015
  • SAE Engineering Meetings Board Outstanding Oral Presentation” award, SAE World Congress, 2015
  • Winner of the Federal Laboratory Consortium Award for Excellence in Technology Transfer, 2015
  • Recipient of the HPC Innovation Excellence Award from the International Data Corporation, 2014
  • Recipient of the Provost’s award for Excellence in Graduate Research, University of Illinois at Chicago, 2008

Technical Society Involvement and Leadership

  • Executive Committee member of the ASME Internal Combustion Engine Division, 2018–present
    • Chair of the Numerical Simulations track, Seattle 2017, Greenville 2016, Houston 2015, Columbus 2014, Morgantown 2011
    • Session Organizer for the Fuels track, Dearborn 2013, Torino 2012, Vancouver 2012
  • Chair for sessions for the SAE International Conference on Engines and Vehicles
    • Chair of the Engine Modeling and Controls track, Capri, Italy 2017
    • Chair of the Multi-dimensional Modeling and Fuel Injection and Spray sessions, 2015–2019
  • Guest Editor for Sprays in Automotive Applications,” Atomization and Sprays, Begell House, 25(4–5), 2015.

Publications Summary

  • Journal Publications: 87
  • Book Chapters: 6
  • Keynote and Invited Talks: 26
  • Peer-reviewed Conference Publications: 84
  • Other Conference Publications: 72
  • Total Citations: 6,350 (Google Scholar, November 2021)
  • H-index: 46 (Google Scholar, November 2021)
  • i10-index: 123 (Google Scholar, November 2021)

Select Publications with Comments

  • Pei, Y.; Pal, P.; Zhang, Y.; Traver, M.; Cleary, D.; Futterer, C.; Brenner, M.; Probst, D.; Som, S. CFD-guided Combustion System Optimization of a Gasoline Range Fuel in a Heavy-duty Compression Ignition Engine Using Automatic Piston Geometry Generation and a Supercomputer.” SAE 2019 International Powertrains: Fuels and Lubricants Meeting, January 2019, SAE Paper No. 2019-01-0001.
  • Comment: This paper discusses the world’s first engine design and optimization performed on the IBM Blue Gene/Q-Mira supercomputer for a heavy-duty diesel engine running gasoline fuel for improved efficiency and performance. Combustion engines are extremely challenging to simulate accurately, owing to their disparate length and time scales, combined with a multitude of physical sub-processes (injection, evaporation, etc.), intertwined with complicated fuel and emission chemistry. The traditional approach to engine design involves only a few parameters over a small design space and involves significant a priori engineering knowledge. The current approach involves a significantly larger design space, with thousands of high-fidelity engine design combinations that would ordinarily require months on a typical cluster but were simultaneously evaluated in days on Mira in a capacity” computing fashion. The accelerated simulation time allowed the evaluation of an unprecedented number of variations within a short time span. ML was used to develop a surrogate model for the simulations using a superlearner” approach. This approach further offered the potential for reducing design time in subsequent iterations. The ability to perform many high-fidelity calculations for a complex device on a supercomputer reduces time to science and opens a new frontier in the automotive industry. Finally, a real-world engine built according to the optimal design recommended by the simulations exhibited a greatly increased fuel efficiency and reduced emissions, suggesting that this approach could have far-reaching implications for cleaner heavy-duty transport globally.
  • Moiz, A. A.; Pal, P.; Probst, D.; Pei, Y.; Zhang, Y.; Som, S.; Kodavasal, J. A Machine Learning-genetic Algorithm (MLGA) Approach for Rapid Virtual Optimization Using High-performance Computing.” SAE International Journal of Commercial Vehicles, 2018, SAE Paper No. 2018-01-0190.
  • Comment: This is the first paper, to the best of our knowledge, that implements robust ML techniques for optimizing combustion processes in piston engines. The paper shows that application of HPC can reduce the overall simulation time by an order of magnitude. The surrogate ML fast-running model, when run through an optimizer, can then produce the same optimum that was captured by the traditional commercial computational fluid dynamics (CFD)-based approach. Significant time savings were demonstrated by combining HPC with ML. In the near future, artificial intelligence and ML will be key areas of growth. This paper is one of the pioneering works that is extensively cited. The MLGA approach is now being commercialized and implemented into two commercial platforms: CONVERGE (which is a CFD code) and Swift (which is a workflow management tool on supercomputers).
  • Kodavasal, J.; Harms, K.; Srivastava, P.; Som, S.; Quan, S.; Richards, K.J.; Garcia, M. Development of Stiffness-based Chemistry Load Balancing Scheme, and Optimization of I/O and Communication, to Enable Massively Parallel High-fidelity Internal Combustion Engine Simulations.” Journal of Energy Resource Technology, 2016, JERT-16-1022 (originally an ASME conference paper in 2015).
  • Comment: Prior to Dr. Som’s involvement in this field, engine simulations were performed on 4-32 processors, and the run times were intractable for the complex two-phase flow and turbulence models that are of interest. In collaboration with Convergent Science Inc., Dr. Som tracked the bottlenecks in the CONVERGE code for scaling it on a Mira supercomputer and then devised new algorithms to improve the load balancing of both the computational cells and chemistry load. This methodology is now implemented in the CONVERGE code and is routinely used by industry and academia. Also, this work featured, to the best of our knowledge, the largest (more than 50 million CFD cells) and first-ever diesel engine simulation on a supercomputer.
  • Senecal, P. K.; Pomraning, E.; Richards, K. J.; Som, S. Grid-convergent Spray Models for Internal Combustion Engine CFD Simulations.” Journal of Energy Resource Technology, 2014, 136(1) (originally an ASME conference paper in 2012).
  • Comment: This is the first paper, to the best of our knowledge, that demonstrates Lagrangian spray simulations down to gas-phase cell sizes on the order of tens of microns, indicating the robustness of the spray-model implementation. Running at such small cell sizes was crucial for demonstrating grid convergence. Before this paper, grid convergence was often overlooked in the engine simulation community. Often, models were run with cell sizes that were far too coarse to be grid-converged, which meant either large errors in the simulations or the need for ad hoc model tuning to make up for the lack of mesh resolution. In other words, grid-convergence studies have become the norm rather than the exception when conducting engine simulations, resulting in a much higher level of confidence in the results.
  • Som, S.; Aggarwal, S.K. Effect of Primary Breakup Modeling on Spray and Combustion Characteristics of Compression Ignition Engines.” Combustion and Flame, 2010, 157, 1179–1193.
  • Comment: Prior to Dr. Som’s involvement in this field, two-phase models were phenomenological and did not incorporate the influence of injector geometry, turbulence, and cavitation effects on the ensuing spray. The turbulent combustion models were based on assumptions of well-mixed phases and did not incorporate the influence of turbulence chemistry interactions for piston engine applications. Dr. Som developed the Kelvin-Helmholtz Aerodynamics Turbulence Cavitation (KH-ACT)-induced primary breakup model and validated it for high-pressure diesel engine applications. This work has received more than 265 citations. The model is part of the CFD code CONVERGE software; academia and industry use this model routinely, as evidenced by their presentations and papers. Students from around the world have further worked on the KH-ACT model and advanced it for gasoline engine applications. This unique model allows researchers to account for the influence of in-nozzle flow characteristics on spray formation and subsequent combustion, thus enhancing the predictive capability of the simulations.
  • Som, S.; Ramirez, A. I.; Longman, D. E.; Aggarwal, S. K. Effect of Nozzle Orifice Geometry on Spray, Combustion, and Emission Characteristics under Diesel Engines Conditions.” Fuel 90, 2011, 1267–1276.
  • Som, S.; Longman, D. E.; Ramirez, A. I.; Aggarwal, S. K. A Comparison of Injector Flow and Spray Characteristics of Biodiesel with Petrodiesel.” Fuel 89, 2010, 4014–4024.
  • Som, S.; Longman, D. E.; Aithal, S. M.; Bair, R.; Garcia, M.; Quan, S.; Richards, K. J.; Senecal, P. K.; Shethaji, T.; Weber, M. A Numerical Investigation on Scalability and Grid Convergence of Internal Combustion Engine Simulations.” SAE 2013 World Congress, April 2013, SAE Paper No. 2013-01-1095.
  • Moiz, A. A.; Kodavasal, J.; Som, S.; Hanson, Redon, R.; F.; Zermeno, R. Computational Fluid Dynamics Simulations of an Opposed-piston Two-stroke Gasoline Compression Ignition Engine.” Proceedings of the ASME 2018 Internal Combustion Engine Division Fall Technical Conference, November 2018, ICEF2018-9713.
  • Yue, Z.; Battistoni, M.; Som, S. Spray Characterization for Engine Combustion Network Spray G Injector Using High-fidelity Simulation with Detailed Injector Geometry.” International Journal of Engine Research (special issue), 2020, 21(1), 226–238.
  • Owoyele, O.; Kundu, P.; Ameen, M.; Echekki, T.; Som, S. Application of Deep Artificial Neural Networks to Multi-dimensional Flamelet Libraries and Spray Flames.” International Journal of Engine Research, 2020, 21(1), 151–168.
  • Zhao, L.; Moiz, A. A.; Som, S.; Fogla, N.; Bybee, M.; Wahiduzzman, S.; Mirzaeian, M.; Millo, F.; Kodavasal, J. Multi-cycle Large Eddy Simulation to Capture Cycle-to-cycle Variation (CCV) in Spark Ignition (SI) Engines.” International Journal of Engine Research, 2017, 18, 1–19.
  • Kundu, P.; Ameen, M.; Som, S. Importance of Turbulence Chemistry Interaction at Low Temperature Engine Conditions.” Combustion and Flame, 2017, 183, 283–298.
  • Pei, Y.; Som, S.; Pomraning, E.; Senecal, P. K.; Skeen, S.A.; Manin, J.; Pickett, L. Large Eddy Simulation of a Reacting Spray Flame with Multiple Realizations under Compression Ignition Engine Conditions.” Combustion and Flame, 2015, 162(12), 4442–4455.
  • Pal, P.; Kumar, G.; Drennan, S.A.; Rankin, B.A.; Som, S. Multi-dimensional Numerical Simulations of Reacting Flow in a Non-premixed Rotating Detonation Engine.” Proceedings of ASME Turbo Expo, June 2019, GT2019-91931.