International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
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Modeling and Grey Relational Multi-response Optimization Performance Efficiency of Diesel Engine Nitesh Kumar1, Purushottam Sahu2, Ghanshyam Dhanera3 1Reseach scholar, BM College of Technology, Indore
2Professor and HEAD BM College of Technology, Indore 3 Professors, BM College of Technology, Indore
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - In conclusion the optimal settings of those factors for better performance of the tested engine which are 25% engine load 20% hydrogen 50 ppm mwcnt 220 bar ignition pressure and 21 obTDC ignition timing In conclusion the figure shows the optimal settings of those factors for better performance of the tested engine which are 25% engine load 20% hydrogen 50 ppm mwcnt 220 bar ignition pressure and 21 obTDC ignition timing. Table 6 displays the variance analysis (ANOVA) of the engine performance. It shows the influence and importance of each component on the performance as a whole. Engine load is discovered to be the most important factor, contributing 71.47% to the total, followed by hydrogen (15.36%) and MWCNTs (8.66%). Ignition pressure and timing made a very small contribution to the other two elements. To increase engine performance efficiency, substantial focus should be given to the aspects that have a significant impact.
Key Words: Diesel engine, grey relationship optimisation, thermal efficiency of the brakes, fuel use, and emissions 1. INTRODUCTION Both human health and the ecosystem are negatively impacted by this phenomenon (Lin et al., 2011; Arbab et al., 2013). Numerous studies have demonstrated that fossil fuels have a considerable impact on the thinning of the ozone layer (Oparanti et al., 2022). According to the International Energy Agency (IEA), the rate of energy consumption would be approximately 53% by 2030 (Taufiqurrahmi & Bhatia, 2011). Meaning that the negative impact of using fossil fuels on the ozone layer's deterioration by 2030 is likely to be intolerable. Numerous studies have been done on the various approaches to overcome these difficulties. A single-cylinder, direct-injection diesel engine's potential for increased performance efficiency and decreased combustion emissions was investigated by Abu-Jrai et al. in 2009. In their research, they introduced simulated reformer product gas to to compare engine performance, combustion, and emissions under various operating situations to a normal ultra-low Sulphur diesel (ULSD) and a substitute ultra-clean synthetic GTL (gas-to-liquid) fuel. They came to the conclusion that a perfect mixture of GTL and simulated reformer product gas greatly reduced NOx and smoke emissions. An investigation on the combustion and emissions of a diesel direct engine injection (DI) running on diesel-oxygenate mixes was conducted by Ren et al. in 2008. They found that regardless of the types of oxygenating additions, there was a reduction in smoke concentration; however, the smoke decreased when the oxygen mass fraction within the blends was increased without raising the NOx and engine thermal efficiency. On the other hand, it had been shown that when the oxygen mass fraction increased in the blends, the amounts of CO and HC decreased. To study the combustion and emissions of the compression ignition of the engine, Li et al. (2015) fueled an instant injection diesel with pentanol. Additionally, there are numerous studies on the optimisation of input variables for diesel engine emissions and performance efficiency. The performance and emissions of a diesel engine running on biodiesel were optimised by Sivaramakrishnan and Ravikumar in 2014. It had been discovered that the test engine performed best at a compression ratio of 17.9, a fuel blend of 10, and a power output of 3.81 kW. Leung and colleagues (2006) optimised the injection pressure, timing, and plunger diameter of the fuel pump.
2 Methodologies: The study by Manigandan et al. (2020) was followed by this one. Their effort provided the experimental data that was needed for analysis. The experimental factors, experimental runs, and corresponding data for the analysis in this work are displayed in Numbers 1, 2, and 3, respectively. Software called Minitab 16 was used for the Taguchi design and modelling, and Origin 19 was used for interaction plots and other types of plots. The experimental data shown in Table 3 underwent a grey relational analysis. With the use of grey relational generation, the data was first normalized. According to Equation 1, the higher-thebetter normalization condition was used to normalize the break thermal efficiency (BTE).
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