Multi-Dimensional Simulations, Combustion Modeling and Data Analysis in Engines or under Engine-Relevant Conditions

Capability Title Multi-dimensional simulations, combustion modeling and data analysis in engines or under engine-relevant conditions
Laboratories Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory (ORNL), Sandia National Laboratories (SNL)
Capability experts Marco Arienti (SNL), Dean Edwards (ORNL), Simon Lapointe (LLNL), Matt McNenly (LLNL), Juliane Mueller (LBNL), Pinaki Pal (ANL), Riccardo Scarcelli (ANL), Sibendu Som (ANL), Roberto Torelli (ANL), Russell Whitesides (LLNL), Shashank Yellapantula (NREL)
Suggested tags (e.g., technoeconomic analysis, fuel property prediction, bioblendstock characterization) – up to five High-Fidelity Simulations of Fuel-Engine Interactions; Reduced Order Models for Capturing Fuel-Engine Interactions; Optimization of fuel composition; Global and Local Sensitivity Analysis; Fuel load range and robustness prediction
  • Capability to perform computational fluid dynamics (CFD) and simulate the behavior of the fuel within an injector, followed by injection, vaporization, ignition, combustion and emission characteristics has been developed and validated against metal and optical engines, flow reactors, rapid compression machines, etc.
  • High-fidelity simulations provide key insights into fuel-engine interactions and enable experimentalists to further improve engine performance.
  • Lower order engineering level, one-dimensional and/or surrogate to multi-dimensional models, are available.
  • Simulation tools can now accurately predict key fuel behavior in combustion vessels and boosted spark ignition, multi-mode, and advanced compression ignition engines.
  • Available models also include
    • Reduced chemical kinetic models for predicting combustion,
    • Machine learning-based octane number predictor model,
    • Machine learning-based turbulent combustion model, and
    • Knock limited spark advance prediction.
  • Local and global sensitivity analyses are routinely performed to understand key physical and chemical properties of fuels that effect engine performance and emissions.
  • Adaptive efficient optimization for automatically finding the best fuel compositions that maximize load range (or robustness) subject to constraints on RON using Gaussian process model approximations
  • Large scale global sensitivity analysis to understand fuel-engine interaction is subject to the availability of computational core hours.
  • Optimization aims at solving problems that involve simulations with non-trivial compute times (minutes or more per evaluation) and for which analytic functional descriptions and derivatives are not available. The number of parameters that can be optimized should be less than 20.
Unique aspects
  • High fidelity simulations provide unique insights into fuel-engine interactions, including a priori prediction of octane number for blends.
  • In contrast to off-the-shelf optimization methods, the number of required simulation model runs to find optimal fuels is significantly reduced by our method, thus yielding significant reductions of required compute resources.
  • Simulation capabilities are limited by availability of staff and computational resources; these limitations vary at each lab.
  • Optimizer: available from the authors, on GitHub in the future after completion of IP review.
Citations/references 1. C. Xu, M. Sjöberg, S. Som, Large eddy simulation of lean mixed-mode combustion assisted by partial fuel stratification in a spark-ignition engine, Proceedings of the ASME 2020 Internal Combustion Engine Division Fall Technical Conference, ICEF2020-3003, Denver, CO, Nov. 1-4, 2020.
2. C. Xu, P. Pal, X. Ren, M. Sjöberg, N. Van Dam, Y. Wu, T. Lu, M. McNenly, S. Som, Numerical investigation of fuel property effects on mixed-mode combustion in a spark-ignition engine, Journal of Energy Resources Technology, 2020, accepted.
3. H. Guo, L. Nocivelli, R. Torelli, S. Som, “Towards understanding the development and characteristics of under-expanded flash boiling jets,” International Journal of Multiphase Flows 129 103315 (2020)
4. Z. Yue, S. Som, “A Study of Fuel Property Effects on Knock Propensity and Thermal Efficiency in a Direct-Injection Spark-Ignition (DISI) Engine,” Applied Energy 2019, 114221
5. Z. Yue, M. Battistoni, S. Som, “Spray Characterization for ECN Spray G Injector Using High-fidelity Simulation with Detailed Injector Geometry,” International Journal of Engine Research (special issue) 2020, vol. 21 (1) 226-238 DOI: 10.1177/1468087419872398
6. Pal, P., Kolodziej, C., Choi, S., Som, S. et al., “Development of a Virtual CFR Engine Model for Knocking Combustion Analysis,” SAE Int. J. Engines 11(6):1069-1082, 2018,