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Vehicular accidents are life-threatening and result in fatal casualties in developing country such as India. According to estimates, traffic accidents kill more people in India than diseases like Cancer and AIDS. More than 150,000 people are killed every year in traffic accidents in India, which works out to 400 fatalities a day, far higher than developed auto markets like the U.S., which had logged about 40,000 deaths in 2016. The World Health Organization estimates road accidents cost most countries about 3 per cent of their gross domestic product. India being the fastest growing economy will be the world’s third-largest car market after China and the U.S. by 2020, according to automobile researchers. According to research study most of death cause due to not getting help on time to the injured person. Research has proven that if injured person is not found any option of help then they also lose the power to fight such critical situation due to psychological effect.

Fuel economy is becoming one of the key parameter as it does not only account for the profitability of commercial vehicle owner but also has impact on environment. Fuel economy gets affected from several parameters of engine such as Peak firing pressure, reduction in parasitic losses, volumetric efficiency and thermal efficiency. Compression ratio is one of key design criteria which affects most of the above mentioned parameters, which not only improve fuel efficiency but also results in improvement of emission levels. This paper evaluates the optimization of Compression ratio and study its effect on Engine performance. The parameters investigated in this paper include combustion bowl volume in Piston and Cylinder head gasket thickness as these are major contributing factors affecting clearance volume and in turn the compression ratio of engine. Based on the calculation results, an optimum Compression Ratio for the engine is selected.

HVAC system design has an accountability towards acoustic comfort of passengers of a vehicle. Owing to larger cabin volume of a bus, multiple air blowers have to be installed to ensure comfort of passengers. Such multiple blowers produce significant flow induced noise inside the cabin. For commercial success, it becomes essential to predict intensity of such a flow induced noise at very early stages in product development. Conventionally sliding mesh based CFD approach is deployed to predict flow and turbulence noise around each blower. However due to complexity, this method becomes computationally intensive resulting in cost and time inefficiency. Hence it is desirable to innovate around an alternative rapid, reliable prediction method, which ensures quick turnaround of prediction.