Technical challenges of ic engines
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Studies on bio fuelled engines showed improved performance, significant drop in CO emissions, unburned hydrocarbons, and particulate matters. Internal combustion engines running on next-generation biofuels is the thrust area amongst the research community. The future internal combustion engines have to deal with the legislative emission requirements, energy security, engine efficiency, affordability, and performance requirements of consumers. Thus internal combustion engines are expected to serve the transportation sector for the coming decades. The safety reliability and the long term maintenance of electric vehicles is also a new concern. Transport electrification alone is unable to resolve the environmental challenges. The complete transformation of the internal combustion engines to new technologies is challenging due to socio-technological issues with tremendous capital investment. Hydrogen, electricity, and biofuels are the future of transport. The current internal combustion-based automotive industry is uncertain about future technology. China and India are likely to see most of the rise. These numbers are likely to cross the 1.7 to 1.9 billion figure by 2040. The 1.1 billion light-duty vehicles and 380 million trucks are on-road vehicles worldwide. A comparison of the ANN model with other prediction models is also presented. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The complex nature of ANN may lead to computation time, energy, and space. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. Industries are looking for proven computational solutions to address these issues. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community.
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The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The automotive industry is facing a crucial time.