1 An Advanced Optimization Methodology for Understanding the Effects of Piston Bowl Design in Late Injection Low-Temperature Diesel Combustion C. Genzale 1 , D. Wickman 2 and R.D. Reitz 1 1 Engine Research Center, University of Wisconsin–Madison, 1500 Engineering Dr., Madison, WI 53706, USA. E-mail: genzale@wisc.edu 2 Wisconsin Engine Research Consultants, LLC, 3983 Plymouth Cir., Madison, WI 53705, USA. Abstract. An integrated optimization methodology is presented that combines the use of a multi-objective genetic algorithm optimization tool and a non-parametric regression analysis tool in order to maximize understanding of piston bowl design for use in low-temperature diesel combustion. This methodology is specifically applied to a late injection, Modulated Kinetics (MK) type combustion in order to gain insight about the effect of bowl design under this type of operating condition. A multi-dimensional Computational Fluid Dynamics (CFD) code was employed with a newly developed automated grid generator and a multi-objective genetic algorithm to optimize eight piston bowl geometry parameters, start-of-injection timing and swirl ratio. The results indicate that bowl geometry and swirl ratio play an important supporting role in obtaining optimal emissions and fuel economy. Introduction In recent years, several engine modeling research groups have begun applying genetic algorithms to optimize complex engine design problems [1, 2, 3, 4]. This approach has shown a powerful ability to simultaneously optimize a large number of engine operating parameters at a relatively low computational cost. This technique has been especially successful in its ability to offer new insights and ideas that are assisting engine researchers in developing future strategies for emissions reductions and fuel conservation. By coupling genetic algorithms to CFD codes, researchers have found that a wide range of design options can be explored entirely theoretically without any concern of damaging engine components or implementing expensive experimental equipment. While these optimizations have yielded many interesting ideas, interpreting the meaning of the results is often difficult due to the large number of parameters being changed simultaneously. There have been many different approaches taken by researchers to better explain the results of these optimization problems. Liu et al. [1] undertook this task in an optimization of a multiple injection strategy for a high speed direct injection (HSDI) diesel engine by performing a parametric study around the resulting optimal design. In that study each design parameter was varied in order to gain a better understanding of the contribution each parameter gave to the optimal design. This approach successfully highlighted the important features in their optimal design, but could not fully illustrate the relative importance of each design parameter or the sensitivity of the design to changes in these parameters. De Risi et al. [2] were able to identify important but generalized piston bowl geometry requirements for optimal emissions in a HSDI diesel engine by using a multi-objective genetic algorithm. Because a multi-objective optimization method was used, a set of optimal solutions was found that simultaneously and individually optimized each of their design objectives. This set of solutions was used to identify important trends in the optimization of each objective. Using these trends, general piston bowl requirements for the optimization of each objective were able to be deduced, but the effect of individual design parameters was not obvious. Recently, Liu et al. [5] introduced a statistical regression method to fit a portion of the optimization data in their previous multiple injection strategy work. With this more rigorous technique, the contribution of each design parameter to the optimal design was quantified and the sensitivity of the optimal design to changes in design parameters was also illustrated. In this work, a multi-objective genetic algorithm similar to that used by De Risi et al. [2] is integrated with the statistical regression method used by Liu et al. [5] to maximize the interpretability of a piston bowl optimization using a new and highly flexible automated computational grid generator. This optimization is performed for a heavy duty direct injection diesel engine utilizing a late injection low-temperature combustion strategy. With a more meaningful methodology to interpret the optimization results, the role of bowl geometry and the effects of individual geometry features under these types of combustion regimes are illustrated.