Towards Quantitative Prediction of Ignition-Delay-Time Sensitivity on Fuel-to-Air Equivalence Ratio

Peter St. John, Mohammad Rahimi, Jon Luecke, Nabila Huq, Thomas Foust, Bradley Zigler, Robert McCormick, Seon Ah Kim, Ji-Woong Park, Tianfeng Lu, Richard Messerly

Research output: Contribution to journalArticlepeer-review

16 Scopus Citations


Several compression-ignition and low-temperature combustion strategies require a fuel where the ignition-delay-time (IDT) is highly sensitive to the fuel-to-air equivalence ratio (ϕ). Quantitative prediction of ϕ-sensitivity (i.e., the change in IDT with respect to ϕ) would enable rapid screening of the numerous possible (bio)fuel candidates for this desired high ϕ-sensitivity characteristic. We propose a new ϕ-sensitivity metric (η), which is primarily a function of only temperature (T) and pressure (P). We assess the reliability of 0-D (perfectly homogeneous) simulation and state-of-the-art reaction mechanisms for two well-studied fuels, namely, iso-octane and a primary reference fuel (PRF80). 0-D simulation results are in good agreement with experimental constant volume IDT data at low- and intermediate-temperatures, while systematic deviations are observed at higher temperatures (where full 3-D computational fluid dynamics simulations are required for accurate prediction). We also perform a traditional single-parameter sensitivity analysis to determine the key reactions that affect ϕ-sensitivity. This is followed by a more rigorous Bayesian uncertainty quantification (UQ) analysis to elucidate the possible sources for the discrepancies at high T. Due to the computational cost of UQ, we train artificial neural networks to rapidly predict η for randomly perturbed sets of low- and intermediate-temperature reaction rate parameters. The primary implications of this study are that experimental ϕ-sensitivity data can be used to refine and validate proposed reaction mechanisms, while machine learning and uncertainty quantification of 0-D simulations are essential for quantitative prediction of ϕ-sensitivity in order to rapidly screen fuel candidates.

Original languageAmerican English
Pages (from-to)103-115
Number of pages13
JournalCombustion and Flame
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2019

NREL Publication Number

  • NREL/JA-2700-75351


  • Kinetic mechanisms
  • Machine learning
  • Uncertainty quantification
  • ϕ-sensitivity


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