Coupling of Scaling Laws and Computational Optimization to Develop Guidelines for Diesel Engine Down-sizing 2011-01-0836

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. An automated grid generator was used for efficient mesh generation with 12 variable piston bowl geometry parameters, including the bore size. Injection timing, spray angle, number of nozzle holes, and swirl ratio were also considered as design parameters. To fully utilize the benefits of the down-scaled engine, its design was used to replace one of the randomly generated initial sets of design parameters. The effects of bore size on down-scaled engine's performance were analyzed using a parametric study and regression analysis of the optimization results. Potential ways to reduce hardware costs are discussed, including maintaining the same bore as the 450cc engine.