Prediction of an optimum engine response based on different input parameters on common rail direct injection diesel engine: A response surface methodology approach

Document Type : Article

Authors

1 Department of Mechanical Engineering, Delhi Technological University, Delhi, India

2 Department of Mechanical Engineering, G L Bajaj Institute of Technology and Management, Greater Noida, UP, India

3 Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

Abstract

As the pollution standards have been raised, the reduction of harmful pollutants is of considerable significance to the transportation industries. The primary aim of this study is to use the response surface methodology (RSM) to optimize pollutants and efficiency to achieve the optimum configuration parameters for the engine. The findings reveal that the RSM technique is a highly desirable optimization approach that can save a lot of repetitive testing. The optimization of variables was conducted using RSM's desirability method such that the performance of the engine gets maximized along with CO2 while reducing the emission factor to a minimum range such as hydrocarbon (HC). After RSM modelling it was observed that the optimized engine settings of input factors are diesel/linseed blend ratio 8.10%, FIP 600 bar, EGR 4.667%, and load on engine 9.33kg. On these constant hold values, the optimized output torque, BTE, BMEP, mechanical efficiency, HC, and CO2 have obtained 20.04 Nm, 26.035%, 3.474 bar, 52.503%, 28.14ppmv, and 7.319 %vol. respectively. Aforesaid predicted values were validated experimentally and errors in predicted values are in a limited range. To achieve improved results, the arrangement of other biodiesel blends from different sources can be carried out for future work.

Keywords

References

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Scientia Iranica
Volume 28, Issue 6 - Serial Number 6
Transactions on Mechanical Engineering (B)
November and December 2021
Pages 3181-3200

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History

  • Receive Date: 20 October 2020
  • Revise Date: 12 January 2021
  • Accept Date: 19 April 2021

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