Modeling and simulation of a turbulent jet diffusion flame of a biodiesel surrogate composed of MD, n-Hept, MC and EtOH

• Published in: Fuel (Guildford) Vol. 313; p. 122649
• Main Authors: De Bortoli, A.L., Pereira, F.N.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-04-2022; Elsevier BV
• Subjects: Biodiesel fuels; Biodiesel surrogate; Biofuels; Carbon dioxide; Chemical kinetics; Coupling (molecular); Diesel; Diffusion; Ethanol; Heptanes; Jet diffusion flames; Layerless neural networks; Methyl esters; Molecular weight; Neural networks; Reaction kinetics; Shear layer; Simulation; Turbulence; Turbulent jets; Biodiesel surrogate; Methyl esters; Turbulence; Layerless neural networks; Jet diffusion flames; Shear layer
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2021.122649

The more properties of biodiesel one wishes to represent, the more complex the surrogate mixture becomes. Often the aim is to represent the H/C and O/C ratios and molecular weight using a small number of pure components. In this article, a turbulent jet diffusion flame of a biodiesel surrogate, composed of 50% Methyl Decanoate, 40% n-heptane, 9% methyl crotonate and 1% ethanol is modeled and simulated. The strategy used is to apply the Directed Relation Graph technique for a first reduction, and Layerless Neural Network to define the main chain and obtain a skeletal mechanism. The results obtained for temperature and mass fractions of CO2, CO and H2O agree reasonably with literature data for a mechanism of 147 reactions and 45 species. The small number of species facilitates the simulation of the coupling between turbulence and chemical kinetics, a desirable feature of reduced mechanisms.
•A skeletal mechanism is obtained for a biodiesel surrogate.•The cost to resolve reactive flow is reduced by two orders of magnitude.•The skeletal mechanism is defined using Layerless Neural Networks.•AI provides the reactions of the ester group to the biodiesel surrogate.