Thermodynamic modelling of combustion process in a spark ignition engine and its numerical prediction

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Abstract

This chapter deals with the details of thermodynamic modelling of combustion process in a spark ignition engine. Because of the advent of fast digital computers, various modelling techniques have evolved over the period. They can be classified as thermodynamic, phenomenological and multi-dimensional models. Depending upon the complexities involved appropriate techniques are chosen for predicting the various engine processes. Of the three modelling techniques mentioned, thermodynamic modelling is the simplest one and can provide first cut solutions in the engine development. It may be noted that whatever modelling procedure used combustion is not yet completely amenable to mathematical analysis. In this chapter attempts have made to bring the combustion process under the ambit of thermodynamics. Ten thermodynamic combustion models available in the literature have been assembled and the details are explained. For the simulation of various engine processes from suction to exhaust appropriate thermodynamic equations have been employed and solved. Towards this a software have been developed and is named as 'GANESH'. The acronym 'GANESH' stands for Graphical and Numerical Software Hub. Using the software pressure-volume and pressure-crank angle variations are predicted and compared with experiments. Various engine performance parameters are predicted and analysed using different combustion models. Where ever possible predicted results are compared with experimental results. It is found that Wiebes law with m = 2 along with Woschni's equation for heat transfer is found to give good results. It is concluded that the developed software can be used with confidence for getting fast and reliable results.

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Shree, V., & Ganesan, V. (2017). Thermodynamic modelling of combustion process in a spark ignition engine and its numerical prediction. In Combustion for Power Generation and Transportation: Technology, Challenges and Prospects (pp. 317–366). Springer Singapore. https://doi.org/10.1007/978-981-10-3785-6_15

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