Semi-Predictive Modeling of Diluted Ethanol and Methanol Combustion in Conventional Spark Ignition Operation

• Main Authors: Mahendar, Senthil Krishnan, Erlandsson, Anders Christiansen
• Format: Report
• Language: English
• ISSN: 0148-7191; 2688-3627
• DOI: 10.4271/2021-01-0386

Alcohols offer high resistance to autoignition which is necessary to attain the required load in heavy duty (HD) spark ignition (SI) engines. Dilution increases thermal efficiency and reduces propensity to autoignition making it an important combustion strategy. Reliable and robust prediction at increased dilution is necessary to support development of high efficiency spark ignition engines and the transition to renewable fuels. A previous experimental study demonstrated 25 bar gross IMEPg for ethanol and methanol at λ=1.4 excess air ratio and over 48% indicated efficiency at λ=1.6 on a single cylinder engine. Based on this dataset, a semi-predictive model (SITurb) was fitted for a range of excess air ratios and engine loads. With the default model, poor accuracy was observed above λ=1.4. Ignition delay was incorrectly predicted at λ=1.6 and λ=1.8. To improve the prediction at high dilution, an improved laminar flame speed correlation was included which reduced the ignition delay error to within ±3 CAD over the range of tested excess air ratios. To improve prediction of burn duration at high dilution, the turbulent flame speed calibration constant was made dependent on dilution level similar to previous research. With both improvements, under ±5% error in IMEPg and under ±3 CAD error in burn duration was achieved at all dilution levels. Finally, the Douaud and Eyzat knock model was evaluated with respect to full load operation of ethanol and methanol and its agreement to knock limited phasing discussed. Semi-predictive models are simple to implement and will be instrumental in gas exchange modeling and optimization of HD SI engines using future alcohol fuels. This study provides the accuracy of standard models and improvements needed to predict performance at diluted conditions.