A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels

Sultan Alqahtani, Kevin M. Gitushi and Tarek Echekki ()
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Sultan Alqahtani: Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA
Kevin M. Gitushi: Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA
Tarek Echekki: Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA

Energies, 2024, vol. 17, issue 3, 1-15

Abstract: The oxidation of complex hydrocarbons is a computationally expensive process involving detailed mechanisms with hundreds of chemical species and thousands of reactions. For low-temperature oxidation, an accurate account of the fuel-specific species is required to correctly describe the pyrolysis stage of oxidation. In this study, we develop a hybrid chemistry framework to model and accelerate the low-temperature oxidation of complex hydrocarbon fuels. The framework is based on a selection of representative species that capture the different stages of ignition, heat release, and final products. These species are selected using a two-step principal component analysis of the reaction rates of simulation data. Artificial neural networks (ANNs) are used to model the source terms of the representative species during the pyrolysis stage up to the transition time. This ANN-based model is coupled with C 0 –C 4 foundational chemistry, which is used to model the remaining species up to the transition time and all species beyond the transition time. Coupled with the USC II mechanism as foundational chemistry, this framework is demonstrated using simple reactor homogeneous chemistry and perfectly stirred reactor (PSR) calculations for n-heptane oxidation over a range of composition and thermodynamic conditions. The hybrid chemistry framework accurately captures correct physical behavior and reproduces the results obtained using detailed chemistry at a fraction of the computational cost.

Keywords: chemistry reduction; data-based hybrid chemistry model; artificial neural network; principal component analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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