A computational methodology for formulating gasoline surrogate fuels with accurate physical and chemical kinetic properties. (1st March 2015)

• Record Type: Journal Article
• Authors: Ahmed, Ahfaz; Goteng, Gokop; Shankar, Vijai S.B.; Al-Qurashi, Khalid; Roberts, William L.; Sarathy, S. Mani
• Is Part Of: Fuel. Volume 143(2015)
• Page Start: 290
• Page End: 300
• Publication Date: 2015-03-01
• Subjects: Gasoline -- Combustion -- Surrogate fuel -- Formulation -- Chemical kinetic simulation; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2014.11.022
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: A novel computational methodology was developed to formulate gasoline surrogate fuels. Regression modeling was combined with physical and chemical kinetics simulations. Various target properties were considered to match physical and kinetic targets. Engine experiments were conducted to compare the surrogates with gasoline fuels. Abstract: Gasoline is the most widely used fuel for light duty automobile transportation, but its molecular complexity makes it intractable to experimentally and computationally study the fundamental combustion properties. Therefore, surrogate fuels with a simpler molecular composition that represent real fuel behavior in one or more aspects are needed to enable repeatable experimental and computational combustion investigations. This study presents a novel computational methodology for formulating surrogates for FACE (fuels for advanced combustion engines) gasolines A and C by combining regression modeling with physical and chemical kinetics simulations. The computational methodology integrates simulation tools executed across different software platforms. Initially, the palette of surrogate species and carbon types for the target fuels were determined from a detailed hydrocarbon analysis (DHA). A regression algorithm implemented in MATLAB was linked to REFPROP for simulation of distillation curves and calculation of physical properties of surrogate compositions. The MATLAB code generates a surrogate composition at each iteration, which is then used to automatically generate CHEMKIN input files that are submitted to homogeneous batch reactor simulations for prediction of research octane number (RON). The regression algorithm determines the optimal surrogate composition to match the fuel properties of FACE A and C gasoline, specifically hydrogen/carbon ( H / C ) ratio, density, distillation characteristics, carbon types, and RON. The optimal surrogate fuel compositions obtained using the present computational approach was compared to the real fuel properties, as well as with surrogate compositions available in the literature. Experiments were conducted within a Cooperative Fuels Research (CFR) engine operating under controlled autoignition (CAI) mode to compare the formulated surrogates against the real fuels. Carbon monoxide measurements indicated that the proposed surrogates accurately reproduced the global reactivity of the real fuels across various combustion regimes.