Development and Validation of a Binary Surrogate Model for Biodiesel

2017-01-2326

10/08/2017

Features
Event
International Powertrains, Fuels & Lubricants Meeting
Authors Abstract
Content
In the present study a novel surrogate model for biodiesel including methyl decanoate (MD) and methyl crotonate (MC) was proposed and validated. In the binary mixture of surrogate fuel, MD was chosen to represent saturated methyl esters, which exhibited great low-temperature reactivity with typical negative temperature-coefficient (NTC) behavior and MC represented unsaturated components in real biodiesel, which was mainly responsible for soot formation and evolution. The proportion of MD and MC was determined by matching the characteristics such as derived cetane number (DCN), molecular weight (MW), atom number, H/C ratio and unsaturated degree. All of the criterions were calculated by the least square principles and the calculated surrogate of biodiesel was comprised of 92% MD and 8% MC in mole fraction. Furthermore, detailed kinetic model of the surrogate fuel was constructed and developed with modifications, which was composed of 2918 species and 9164 reactions. To validate the detailed mechanism of the surrogate fuels, oxidation experiments at low to intermediate temperature were conducted in laminar flow reactor at the equivalence ratio of 1.5, 1 and 0.6 at atmospheric pressure. Moreover, the new developed mechanism was employed in shock tube to predict the ignition delay times and in homogeneous charge compression ignition engine to simulate cylinder pressure and heat release rate. Good agreements were found between experimental and computational results, which illustrated the binary mixture of MD and MC was a suitable surrogate fuel for biodiesel and the detailed kinetic model had great accuracy.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-2326
Pages
11
Citation
Li, A., Deng, Z., Zhu, L., and Huang, Z., "Development and Validation of a Binary Surrogate Model for Biodiesel," SAE Technical Paper 2017-01-2326, 2017, https://doi.org/10.4271/2017-01-2326.
Additional Details
Publisher
Published
Oct 8, 2017
Product Code
2017-01-2326
Content Type
Technical Paper
Language
English