A review on recent progress in computational and empirical studies of compression ignition internal combustion engine

• Published in: Fuel (Guildford) Vol. 279; p. 118469
• Main Authors: Salam, Satishchandra, Choudhary, Tushar, Pugazhendhi, Arivalagan, Verma, Tikendra Nath, Sharma, Abhishek
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2020; Elsevier BV
• Subjects: Algorithms; Alternative fuels; Analytical modelling; Artificial neural networks; Biodiesel fuels; Biofuels; Combustion; Compression; Computer applications; Empirical analysis; Experimental methods; Experimental research; Fuel economy; Genetic algorithm; Genetic algorithms; IC engine; Internal combustion engines; Learning algorithms; Machine learning; Mathematical models; Neural networks; Optimization; Regression analysis; Research methods; Response surface methodology; Statistical analysis; Statistical methods; Sustainable energy; Taguchi method; Taguchi methods; IC engine; Response surface methodology; Analytical modelling; Genetic algorithm; Artificial neural networks; Taguchi method
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2020.118469

The use of biodiesel as an alternative fuel in the pursuit of renewable and sustainable energy has raised new technological, economic and environmental concerns. Although it has been almost 150 years since the introduction of internal combustion engine, researches seeking engineering solutions still continue. Driving quality, performance and fuel economy have been improved while emissions have been lowered significantly. But there has not been any unified analytical model that can capture the internal combustion engine as a complete system from thermodynamic, mechanical or chemical perspective per se. With experimental research methods usually too involving in terms of engineering costs, computational approaches to deliver numerical solutions have been inevitable as a research methodology – or even sometimes, is left as the only feasible method. In light of these concerns, this article reviews a few trending modelling methods of (1) analytical, (2) regression and (3) artificial neural network methods, and optimisation methods of (1) response surface methodology, (2) Taguchi method and (3) genetic algorithm which have been popularly employed in internal combustion engine research. The review recommends to confluence of advanced statistical methods and emerging popular machine learning algorithms to engine research for deriving comprehensive pragmatic models as empirical compromise.