@article{a75736fc0944480b9d6c61ae01ceae0b,
title = "Data-driven chemical kinetic reaction mechanism for F-24 jet fuel ignition",
abstract = "A data-driven chemical kinetic mechanism for the military version of Jet A, F-24, is developed for numerical simulations of the ignition process. The main purpose of this study is to obtain a practical F-24 mechanism across wide temperature and equivalence ratio ranges, with a particular focus on the negative temperature coefficient and low temperature regions. The new mechanism (ARLMech-HC-F24) is based on the HyChem model of a similar fuel and optimized using a micro-genetic algorithm against an experimental ignition delay data set of the target fuel. The development and optimization processes include reaction selection, population creation, shuffled tournament implementation based on a merit function, and child-individual creation for the next generation. Several techniques and parameters are proposed to generate an accurate mechanism through an efficient process. The newly introduced data-driven mechanism based on these techniques shows better merit value convergence and represents the ignition behavior more accurately than that without the techniques. This practical mechanism is suitable for the numerical simulations of the F-24 or Jet A ignition problem, and the suggested strategies can be employed in similar problems of rate coefficients estimation.",
keywords = "ARLMech, F-24, Genetic Algorithm, HyChem, Jet A, Jet Fuel",
author = "Ryu, {Je Ir} and Keunsoo Kim and Kyungwook Min and Riccardo Scarcelli and Sibendu Som and Kim, {Kenneth S.} and Temme, {Jacob E.} and Kweon, {Chol Bum M.} and Tonghun Lee",
note = "Funding Information: Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-16-2-0220, W911NF-19-2-0239, and W911NF-18-2-0282 (postdoctoral fellowship). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The submitted manuscript also has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. The authors also acknowledge Professor Hai Wang at Stanford University providing the HyChem model funded by the Air Force Office of Scientific Research with Dr. Chiping Li as technical monitor. Moreover, the discussion on the genetic algorithm in CONVERGE software with Dan Probst at Convergent Science is appreciated. Funding Information: Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-16-2-0220, W911NF-19-2-0239, and W911NF-18-2-0282 (postdoctoral fellowship). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The submitted manuscript also has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (?Argonne?). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. The authors also acknowledge Professor Hai Wang at Stanford University providing the HyChem model funded by the Air Force Office of Scientific Research with Dr. Chiping Li as technical monitor. Moreover, the discussion on the genetic algorithm in CONVERGE software with Dan Probst at Convergent Science is appreciated. Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",
year = "2021",
month = apr,
day = "15",
doi = "10.1016/j.fuel.2020.119508",
language = "English (US)",
volume = "290",
journal = "Fuel",
issn = "0016-2361",
publisher = "Elsevier BV",
}