Advances in Automobile Engineering
Open Access

The Impact of Artificial Neural Networks in Internal Combustion Engines

Pierce Healy^{* *}Correspondence: Pierce Healy, Department of Mechatronics, University of Technology Sydney, South wales, Australia, Email:

Author info »

About the Study

Randomized On the road there are 1.1 billion light-duty vehicles and 380 million trucks. By 2040, these figures are expected to exceed 1.7 to 1.9 billion. The majority of the rise is likely to be seen in China and India. The existing internal combustion-powered vehicle industry is concerned about the future of technology. The future of transportation will be powered by hydrogen, electricity, and biofuels. The entire changeover of internal combustion engines to new technologies is difficult due to socio-technological difficulties requiring significant capital investment. Transport electrification alone will not solve the environmental problems. For the first time in the last decade as a result, internal combustion engines are projected to serve the transportation sector for the foreseeable future.

Future internal combustion engines must address statutory pollution restrictions, energy security, engine efficiency, cost, and consumer performance expectations. Internal combustion engines powered by next-generation biofuels are the focus of the research community. Biofuelled engine studies revealed better performance as well as a considerable reduction in CO emissions, unburned hydrocarbons, and particulate matter. There is no single best-suggested biofuel for the engine. The availability of local feedstock or waste biomass, the qualities of bio-diesel and the methods for improving the attributes all contribute to a sustainable and cost-effective biofuel solution.

Many parameters are involved in the performance, combustion, pollution, and other characteristics of the IC engine. The combustion phenomenon and exhaust emissions are extremely hard to comprehend, necessitating multiple studies to determine the relationship between various parameters. To measure the many operational characteristics of these biofuels, high-precision instruments and equipment are necessary. These exams are both costly and time-consuming.

The recent economic crisis caused by the global COVID-19 outbreak resulted in massive revenue losses for the sector. Traditional research has been hampered by social alienation and limited study funding. As a result, there is a need to produce a different alternative option that saves money and time on experimentation. In the current and long-uncertain COVID-19 scenario, enhanced artificial intelligence-based research may supplant traditional research in order to minimize costs and aid the social alienation caused by work-from-home culture. In recent years, some computational modelling techniques have been utilized to relate the numerous engine parameters and anticipate the varied features of IC engines such as performance, combustion, and pollution, and they appear to be useful due to their fairly accurate predictions. In this method, the artificial neural network is one of the available techniques to anticipate the distinct features of the IC engine with different sets of inputs.

The artificial neural network is the most widely used type of artificial intelligence. Artificial intelligence is the development and analysis of intelligence in machines. An intelligent machine is a quick-thinking noetic agent that detects its surroundings and takes action to maximize its chances of succeeding at an arbitrary objective. Much work has been done with the ANN throughout the last decade. It can connect complex and non-linear problems that are either complete or incomplete.

Conclusion

ANN has its vast applicability in diverse areas like as engineering, science, pharmaceutics. Some of the significant fields include sound and pattern identification, market trend prediction, bankruptcy, military targets, and mineral exploration sites. Neural networks eliminate the need for expensive and inefficient physical models, complicated mathematical formulas, and computer models. The neural network can readily manage numerical or analogue data that is difficult to handle due to the presence of many variables.