Journal of Mechanical Engineering and Sciences (Mar 2022)

Optimization of combustion parameters for CRDI small single cylinder diesel engine by using response surface method

  • D.K. Dond,
  • N.P. Gulhane

DOI
https://doi.org/10.15282/jmes.16.1.2022.07.0690
Journal volume & issue
Vol. 16, no. 1
pp. 8730 – 8742

Abstract

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Limited fossil fuel’s reservoir capacity and pollution caused by them are the big problem today in the world. The small diesel engine, working with a conventional fuel injection system was the major contributor to this. The current study represented a statistical investigation of such a small diesel engine. A mechanical fuel injection system of the small diesel engine was retrofitted with a simple version of the electronic common rail diesel injection (CRDI) system in the present study. The effect of combustion parameters such as compression ratio (CR), injection pressure (IP) and start of injection timing (IT) was considered in the study. The study was performed to optimize these parameters with respect to performance and emission aspects. The reduction in parameters such as carbon monoxide (CO), nitrogen oxides (NOx), smoke and hydrocarbon (HC) from engine exhaust gases were considered in the emission aspect. Improve brake thermal efficiency (BTE) and fuel economy was considered in the performance aspect. The response surfaced method (RSM) was used to optimise these combustion parameters. The regression equations were obtained for measurable performance and emission parameters using the RSM model. The surface plots derived from the regression equations were used to analyse the effect of considered combustion parameters. Diesel injected at a pressure 600 bar, with retarded injection timing 15° crank angle (CA) before top dead center (bTDC) and compression ratio set at 15 was found to be optimum for this CRDI small diesel engine. The further validation of optimum parameters was done by conducting a confirmatory test on the engine. The maximum error in prediction was found to be 2.7%, which shows the validation of the RSM model.

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