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It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise.

Compared to the gasoline engine, the diesel engine has the advantage of being more efficient and hence achieving a reduction of CO₂ levels. Unfortunately, particulate matter (PM) and nitrogen oxides (NOx) emissions from diesel engines are high. To overcome these drawbacks, several new combustion concepts have been developed, including the PCCI (Premixed Charge Compression Ignition) combustion mode. This strategy allows a simultaneous reduction of NOx and soot emissions through the reduction of local combustion temperatures and the enhancement of the fuel/air mixing. In spite of PCCI benefits, the concept is characterized by its high combustion noise levels. Currently, a promising way to improve the PCCI disadvantages is being investigated. It is related with the use of low cetane fuels such as gasoline and diesel-gasoline blends.