Investigation on the effect of boost pressure on performance characteristics of a diesel engine

References

• P. Dimitriou and T. Tsujimura, “A review of hydrogen as a compression ignition engine fuel,” Int. J. Hydrogen Energy, vol. 42, no. 38, pp. 24470–24486, 2017. DOI: 10.1016/j.ijhydene.2017.07.232.
   Web of Science ®Google Scholar
• J. Haller and T. Link, “Thermodynamic concept for an efficient zero-emission combustion of hydrogen and oxygen in stationary internal combustion engines with high power density,” Int. J. Hydrogen Energy, vol. 42, no. 44, pp. 27374–27387, 2017. DOI: 10.1016/j.ijhydene.2017.08.168.
   Web of Science ®Google Scholar
• J. Krishnanunni, D. Bhatia, and L. M. Das, “Experimental and modelling investigations on the performance and emission characteristics of a single cylinder hydrogen engine,” Int. J. Hydrogen Energy, vol. 42, no. 49, pp. 29574–29584, 2017. DOI: 10.1016/j.ijhydene.2017.10.018.
   Web of Science ®Google Scholar
• H. B. Luan, J. P. Kuang, Z. Cao, Z. Wu, W. Q. Tao, and B. Sundén, “CFD analysis of two types of welded plate heat exchangers,” Numer. Heat Transf., Part A: Appl., vol. 71, no. 3, pp. 250–269, 2017. DOI: 10.1080/10407782.2016.1264761.
   Web of Science ®Google Scholar
• X. H. Xue, “Prediction of daily diffuse solar radiation using artificial neural networks,” Int. J. Hydrogen Energy, vol. 42, no. 47, pp. 28214–28221, 2017. DOI: 10.1016/j.ijhydene.2017.09.150.
   Web of Science ®Google Scholar
• P. Yi, M. Jia, W. Q. Long, L. Qiao, T. H. Yang, and L. Y. Feng, “Evaporation of pure and blended droplets of diesel and alcohols (C2–C9) under diesel engine conditions,” Numer. Heat Transf., Part A: Appl., vol. 71, no. 3, pp. 311–326, 2017. DOI: 10.1080/10407782.2016.1264749.
   Web of Science ®Google Scholar
• R. S. Markadeh, and H. Ghassemi, “A discrete multicomponent droplet evaporation model; effects of O2-enrichment, steam injection, and EGR on evaporation of diesel droplet,” Numer. Heat Transf., Part A: Appl., vol. 73, no. 10, pp. 721–742, 2018. DOI: 10.1080/10407782.2018.1476437.
   Web of Science ®Google Scholar
• H. Y. Hu, and Q. Wang, “Improved spectral absorption-coefficient grouping strategies in radiation heat transfer calculations for combustion gases with pressure and temperature inhomogeneity,” Numer. Heat Transf., Part B: Fundam, vol. 75, no. 3, pp. 178–197, 2019. DOI: 10.1080/10407790.2019.1612663.
   Web of Science ®Google Scholar
• S. Sivalakshmi, and T. Balusamy, “Influence of ethanol addition on a diesel engine fuelled with neem oil methyl ester,” Int. J. Green Energy, vol. 9, no. 3, pp. 218–228, 2012. DOI: 10.1080/15435075.2011.621477.
   Web of Science ®Google Scholar
• M. Aldhaidhawi, R. Chiriac, V. Bǎdescu, G. Descombes, and P. Podevin, “Investigation on the mixture formation, combustion characteristics and performance of a diesel engine fueled with diesel, biodiesel B20 and hydrogen addition,” Int. J. Hydrogen Energy, vol. 42, no. 26, pp. 16793–16807, 2017. DOI: 10.1016/j.ijhydene.2017.01.222.
   Web of Science ®Google Scholar
• J. G. Morales, M. C. Bobadilla, R. F. E. Jimenez, J. F. G. Agilar, and V. H. O. Peregrino, “Experimental implementation of a control scheme to feed a hydrogen-enriched E10 blend to an internal combustion engine,” Int. J. Hydrogen Energy, vol. 42, pp. 25026–25036, 2017. DOI: 10.1016/j.ijhydene.2017.08.110.
   Google Scholar
• S. K. Dash, and P. Lingfa, “Performance evaluation of Nahar oil-diesel blends in a single cylinder direct injection diesel engine,” Int. J. Green Energy, vol. 15, no. 6, pp. 400–405, 2018. DOI: 10.1080/15435075.2018.1464926.
   Web of Science ®Google Scholar
• N. Jamsran, and O. Lim, “Effects of EGR and boosting on the auto-ignition characteristics of HCCI combustion fueled with natural gas,” J. Nat. Gas Sci. Eng., vol. 35, pp. 1015–1024, 2016. DOI: 10.1016/j.jngse.2016.09.016.
   Web of Science ®Google Scholar
• I. Al-Hinti, M. Samhouri, A. Al-Ghandoor, and A. Sakhrieh, “The effect of boost pressure on the performance characteristics of a diesel engine: a neuro-fuzzy approach,” Appl. Energy, vol. 86, no. 1, pp. 113–121, 2009. DOI: 10.1016/j.apenergy.2008.04.015.
   Web of Science ®Google Scholar
• H. Mohamed Ismail, H. K. Ng, C. W. Queck, and S. Gan, “Artificial neural networks modeling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends,” Appl. Energy, vol. 92, pp. 769–777, 2012. DOI: 10.1016/j.apenergy.2011.08.027.
   Web of Science ®Google Scholar
• F. Mariani, C. N. Grimaldi, and M. Battistoni, “Diesel engine NOx emissions control: an advanced method for the O2 evaluation in the intake flow,” Appl. Energy, vol. 113, pp. 576–588, 2014. DOI: 10.1016/j.apenergy.2013.07.067.
   Web of Science ®Google Scholar
• S. Roy, R. Banerjee, and P. K. Bose, “Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network,” Appl. Energy, vol. 119, pp. 330–340, 2014. DOI: 10.1016/j.apenergy.2014.01.044.
   Web of Science ®Google Scholar
• J. Rezaei, M. Shahbakhti, B. Bahri, and A. A. Aziz, “Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks,” Appl. Energy, vol. 138, pp. 460–473, 2015. DOI: 10.1016/j.apenergy.2014.10.088.
   Web of Science ®Google Scholar
• S. Lotfan, R. A. Ghiasi, M. Fallah, and M. H. Sadeghi, “ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II,” Appl. Energy, vol. 175, pp. 91–99, 2016. DOI: 10.1016/j.apenergy.2016.04.099.
   Web of Science ®Google Scholar
• S. R. Shabanian, S. Iashgari, and T. Hatami, “Application of intelligent methods for the prediction and optimization of thermal characteristics in a tube equipped with perforated twisted tape,” Numer. Heat Transf., Part A: Appl., vol. 70, no. 1, pp. 30–47, 2016. DOI: 10.1080/10407782.2016.1139982.
   Web of Science ®Google Scholar
• R. Beigzadeh, M. Rahimi, O. Jafari, and A. A. Alsairafi, “Computational fluid dynamics assists the artificial neural network and genetic algorithm approaches for thermal and flow modeling of air-forced convection on interrupted plate fins,” Numer. Heat Transf., Part A: Appl., vol. 70, no. 5, pp. 546–565, 2016. DOI: 10.1080/10407782.2016.1177329.
   Web of Science ®Google Scholar
• V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
   Google Scholar
• J. A. K. Suykens, J. Vandewalle, and B. De Moor, “Optimal control by least squares support vector machines,” Neural Netw., vol. 14, no. 1, pp. 23–35, 2001. DOI: 10.1016/S0893-6080(00)00077-0.
   PubMed Web of Science ®Google Scholar
• J. F. Wang, Z. X. Sun, Y. P. Dai, and S. L. Ma, “Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network,” Appl. Energy, vol. 87, no. 4, pp. 1317–1324, 2010. DOI: 10.1016/j.apenergy.2009.07.017.
   Web of Science ®Google Scholar
• S. Blaifi, S. Moulahoum, I. Colak, and W. Merrouche, “An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications,” Appl. Energy, vol. 169, pp. 888–898, 2016. DOI: 10.1016/j.apenergy.2016.02.062.
   Web of Science ®Google Scholar
• T. Gentils, L. Wang, and A. Kolios, “Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm,” Appl. Energy, vol. 199, pp. 187–204, 2017. DOI: 10.1016/j.apenergy.2017.05.009.
   Web of Science ®Google Scholar