Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool

Abstract

Hydrogen and its careers have a lot of potential in transportation industry due to lesser emissions and better performance. Performance of hydrogen enriched compressed natural gas (HCNG) fueled engine is limited by Knock. The purpose of this study is to investigate knock intensity (KI) by different convective heat transfer models. This study can be used to train electronic control unit (ECU) of engine operating on different loads from low to high speed. In present study, experimentation have been performed on HCNG fueled spark ignition engine at different operating conditions by varying hydrogen amount in HCNG, by varying load of engine, by varying exhaust gas recirculation (EGR) rate in air and by varying speed of engine to calculate in-cylinder pressure, knock intensity (KI) and heat transfer rate (HTR). Six convective heat transfer models (Assanis, Eichelberg, Han, Hohenberg, Nusselt and Woschni) have been incorporated in quasi dimensional combustion model (QDCM) to calculate the parameters of HCNG engine. Comparative and predictive analysis using artificial neural network fitting tool (ANNFT) of aforementioned simulated models have been performed with experimental findings to attain the efficient model for knock intensity of HCNG engine. Woschni model, predicts the minimum error of 0.37 % & 0.86 % b/w experimental and simulated in-cylinder pressure and indicated mean effective pressure respectively. Han model predicts the minimum error of 3.8 % b/w experimental and simulated knock intensity operating at 11.2 % EGR. Maximum error of 20.3 % at 2.4 % EGR is attained by using Woschni model in knock intensity calculation. The best suited algorithm for present data set is Bayesian regularization utilized in ANNFT to predict knock intensity effectively. The findings of this study can be utilized in the development of HCNG engine.