A data-based hybrid model for complex fuel chemistry acceleration at high temperatures

Abstract

During their high-temperature oxidation, complex hydrocarbons and their early fragments are short-lived and figure prominently only during the pyrolysis stage. However, they are quickly replaced by smaller hydrocarbons at the onset of the oxidation stage, resulting in simpler chemistry requirements past pyrolysis. In this study, we develop a data-based hybrid chemistry approach to accelerate chemistry integration for complex fuels. The approach is based on tracking the evolution of chemistry through representative species for the pyrolysis and coupling their reactions with simpler foundational chemistry. The selection of these representative species is implemented using principal component analysis (PCA) based on simulation data. The description of chemistry for the representative species is implemented using an artificial neural network (ANN) model for their reaction rates followed by the description of their chemistry using a foundational chemistry model. The selection of the transition between these models is trained a priori using an ANN pattern recognition classifier. This data-based hybrid chemistry acceleration model is demonstrated for three fuels: n-dodecane, n-heptane and n-decane and investigated with two foundational chemistry, C0–C2 and C0–C4, models. The hybrid scheme results in computational saving, up to one order of magnitude for n-dodecane, two orders of magnitudes for n-heptane, and three orders of magnitudes for n-decane. The accuracy and saving in computational cost depend on the number of selected species and the size of the used foundational chemistry. The hybrid model coupled with the more detailed C0–C4 foundational performs, overall, better than the one coupled with the C0–C2 foundational chemistry.