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A set of scaling laws were previously developed to guide the transfer of combustion system designs between diesel engines of different sizes [ 1 , 2 , 3 , 4 ]. The intent of these scaling laws was to maintain geometric similarity of key parameters influencing diesel combustion such as in-cylinder spray penetration and flame lift-off length. The current study explores the impact of design constraints or limitations on the application of the scaling laws and the effect this has on the ability to replicate combustion and emissions. Multi dimensional computational fluid dynamics (CFD) calculations were used to evaluate the relative impact of engine design parameters on engine performance under full load operating conditions. The base engine was first scaled using the scaling laws. Design constraints were then applied to assess how such constraints deviate from the established scaling laws and how these alter the effectiveness of the scaling effort.

The present work proposes a methodology for diesel engine development using scaling laws and computational optimization with multi-dimensional CFD tools. A previously optimized 450cc HSDI diesel engine was down-scaled to 400cc size using recently developed scaling laws. The scaling laws were validated by comparing the performance of these two engines, including pressure, HRR, peak and averaged temperature, and pollutant emissions. A novel optimization methodology, which is able to simultaneously optimize multiple operating conditions, was proposed. The method is based on multi-objective genetic algorithms, and was coupled with the KIVA3V Release 2 code to further optimize the down-scaled diesel engine. An adaptive multi-grid chemistry model was used in the KIVA3V code to reduce the computational cost of the optimization. The computations were conducted using high-throughput computing with the CONDOR system.