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Sibendu Som

Sibendu Som

Section Manager, Multi-Physics Computation

Biography

Dr. Sibendu Som is the manager of the Computational Multi-Physics Research Section in the Energy Systems Division at Argonne National Laboratory and a senior scientist at the Consortium for Advanced Science and Engineering, University of Chicago.

Dr. Som has over a decade of experience in enabling technologies for more efficient engine combustion using computational tools. He leads a Computational Fluid Dynamics (CFD) team at Argonne National Laboratory with a research focus on the development of nozzle-flow, spray, and combustion models, using high-performance computing (HPC) for internal combustion engine (ICE) applications. His team is responsible for developing predictive simulation capabilities to enable OEMs to develop advanced high-efficiency, low-emission engines. Dr. Som’s group is pioneering the implementation of machine learning (ML) techniques to further speed up piston engine and gas turbine simulations. He is a co-founder and technical lead of Argonne’s Virtual Engine Research Institute and Fuels Initiative (VERIFI) program, which is aimed at providing predictive simulations for industry. Dr. Som and his team are recognized worldwide for improving the predictive capability of simulation tools and applying these tools using HPC to reduce time to design. This outcome has been achieved through a decade of work in improving sub-models and HPC tools and, more recently, applying ML tools. The improved predictive capability and reduction of simulation time have benefited several industries.

Dr. Som received a Bachelor of Engineering degree in Mechanical Engineering from Osmania University (India) in 2003. He then moved to Chicago and received Master of Science (2005) and Doctor of Philosophy (2009) degrees in Mechanical Engineering from the University of Illinois at Chicago. He worked as a student at Argonne from 2006 to 2009 while finishing his Ph.D. thesis. He joined Argonne as a postdoc in January 2010 and was appointed to a staff position in May 2011. He became a PI in 2012 and currently manages a section consisting of 25+ researchers, including staff scientists and postdocs.

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

Professional Experience

Manager and Principal Computational Scientist - May 2017-present
Group Leader and Lead Computational Scientist - May 2015-May 2017
Lead Computational Scientist - November 2013-May 2015
Mechanical Engineer - May 2011-November 2013
Postdoctoral Appointee - January 2010-April 2011

Awards and Recognition

  • ASME ICED award for most valuable technical paper in Numerical Simulations track (2018)
  • Federal Laboratory Consortium Award from DOE for Excellence in Technology Transfer (August 2018)
  • Nominee for Strategic Laboratory Leadership Program at Booth School of Business, University of Chicago (2018)
  • Energy and Global Security Excellence Award for Project Performance, Argonne National Laboratory (2018)
  • Outstanding Postdoctoral Supervisor Award, Argonne National Laboratory (2016)
  • ASME ICED Outstanding Presenter Award (2015)
  • SAE Engineering Meetings Board Outstanding Oral Presentation Award, SAE World Congress (2015)
  • Federal Laboratory Consortium Award from DOE for Excellence in Technology Transfer (January 2015)
  • Norman Chigier Award for Reviewing Excellence, Institute for Liquid Atomization and Spray Systems (20142015)
  • Invitee to U.S. Frontiers of Engineering Workshop organized by the National Academy of Sciences (only 100 people under the age of 45 were invited) (Fall 2014)
  • High Performance Computing Innovation Excellence Award from International Data Corporation (June 2014)
  • Computational Fellow at The University of Chicago Computational Institute (20132019)

Technical Society Involvement and Leadership

  • ASME Internal Combustion Engine Division
    • Executive Committee member (June 2018–present)
    • Chair of Numerical Simulations” track: Columbus 2014; Houston 2015; Greenville 2016; Seattle 2017
    • Session Organizer for Fuels” track: Torino 2012; Vancouver 2012; Dearborn 2013
    • Session Organizer for Numerical Simulations” track: Morgantown 2011
  • Society of Automotive Engineers (SAE)
    • Chair of Engine Modeling and Controls” track, SAE International Conference on Engines and Vehicles; Capri, Italy (ICE2017)
    • Chair of Multi-dimensional Modeling” and Fuel Injection and Spray” sessions, 20152019
  • Institute for Liquid Atomization and Spray Systems (ILASS)
    • Session chair/organizer for multiple ILASS conferences
    • Guest Editor for Atomization and Sprays, journal published by Begell House. Edited Issues 4 & 5 for Volume 25, Sprays in Automotive Applications,” in 2015.
  • Scientific Advisory Committee Member for LES4ICE conference organized by IFP Energies Nouvelles, France

Publications Summary

  • Journal publications: 79
  • Book chapters: 5
  • Keynote & other invited talks: 25
  • Peer-reviewed conference publications: 76
  • Other conference publications: 63
  • Total citations: 4638 (Google Scholar, January 2020)
  • H-index: 40 (Google Scholar, January 2020)
  • i10-index: 90 (Google Scholar, January 2020)

Top Publications with Comments

  1. S. Som, S.K. Aggarwal, Effect of primary breakup modeling on spray and combustion characteristics of compression ignition engines,” Combustion and Flame 157: 11791193, 2010.
    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 the assumption 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 commercial CFD code CONVERGE; 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.
  2. J. Kodavasal, K. Harms, P. Srivastava, S. Som, S. Quan, K.J. Richards, M. Garcia, 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 JERT-16-1022, 2016 (originally an ASME conference paper in 2015).
    Comment: Prior to Dr. Som’s involvement in the 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 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 routinely used by industry and academia. This work also featured, to the best of our knowledge, the first-ever and largest (more than 50 million CFD cells) diesel engine simulation on a supercomputer.
  3. Y. Pei, P. Pal, Y. Zhang, M. Traver, D. Cleary, C. Futterer, M. Brenner, D. Probst, S. Som, 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 Paper No. 2019-01-0001, SAE 2019 International Powertrains, Fuels and Lubricants Meeting, San Antonio, TX, January 2019.
    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 which were simultaneously evaluated in days on Mira in 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 greatly reduced emissions and increased fuel efficiency, suggesting that this approach could have far-reaching implications for cleaner heavy-duty transport globally.
  4. A.A. Moiz, P. Pal, D. Probst, Y. Pei, Y. Zhang, S. Som, J. Kodavasal, A Machine Learning-Genetic Algorithm (MLGA) approach for rapid virtual optimization using high-performance computing,” SAE Paper No. 2018-01-0190, SAE International Journal of Commercial Vehicles, 2018.
    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 CFD-based approach. Significant time savings were demonstrated by combining HPC with ML. Artificial intelligence and ML are going to be key areas of growth in the near future. This paper is going to be one of the pioneering works that get cited extensively. The MLGA approach is now being commercialized and implemented into two commercial platforms, namely, CONVERGE (which is a CFD code) and Swift (which is a workflow management tool on supercomputers).
  5. Senecal, P. K., Pomraning, E., Richards, K. J. and Som, S., Grid-convergent spray models for internal combustion engine CFD simulations,” Journal of Energy Resource Technology 136 (1), 2014 (originally an ASME conference paper in 2012).
    Comment: This is the first paper (to the best of our knowledge) that demonstrated 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 very important 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 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.
  6. S. Som, A.I. Ramirez, D.E. Longman, S.K. Aggarwal, Effect of nozzle orifice geometry on spray, combustion, and emission characteristics under diesel engines conditions,” Fuel 90, 12671276, 2011.
  7. S. Som, D.E. Longman, A.I. Ramirez, S.K. Aggarwal, A comparison of injector flow and spray characteristics of biodiesel with petrodiesel,” Fuel 89, 40144024, 2010.
  8. S. Som, D.E. Longman, S.M. Aithal, R. Bair, M. Garcia, S. Quan, K.J. Richards, P.K. Senecal, T. Shethaji, M. Weber, A numerical investigation on scalability and grid convergence of internal combustion engine simulations,” SAE Paper No. 2013-01-1095, SAE 2013 World Congress, Detroit, MI, April 2013.
  9. A.A. Moiz, J. Kodavasal, S. Som, R. Hanson, F. Redon, R. Zermeno, 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, ICEF2018-9713, San Diego, CA, November 2018.
  10. Z. Yue, M. Battistoni, S. Som, Spray characterization for engine combustion network Spray G injector using high-fidelity simulation with detailed injector geometry,” International Journal of Engine Research (special issue) 21 (1), 226238, 2020.
  11. O. Owoyele, P. Kundu, M. Ameen, T. Echekki, S. Som, Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames,” International Journal of Engine Research 21 (1), 151168, 2020.
  12. L. Zhao, A. A. Moiz, S. Som, N. Fogla, M. Bybee, S. Wahiduzzman, M. Mirzaeian, F. Millo, J. Kodavasal, Multi-cycle large eddy simulation to capture cycle-to-cycle variation (CCV) in spark ignition (SI) engines,” International Journal of Engine Research 18, 119, 2017.
  13. P. Kundu, M. Ameen, S. Som, Importance of turbulence chemistry interaction at low temperature engine conditions,” Combustion and Flame 183, 283298, 2017.
  14. Y. Pei, S. Som, E. Pomraning, P.K Senecal, S.A. Skeen, J. Manin, L. Pickett, Large eddy simulation of a reacting spray flame with multiple realizations under compression ignition engine conditions,” Combustion and Flame 162 (12), 44424455, 2015.
  15. P. Pal, G. Kumar, S.A. Drennan, B.A. Rankin, S. Som, Multi-dimensional numerical simulations of reacting flow in a non-premixed rotating detonation engine,” GT2019-91931, Proceedings of ASME Turbo Expo, Phoenix, AZ, June 2019.