Browse Publications Technical Papers 03-14-02-0011

Optimization of a 3D Combustion Bowl Geometry Using Response Surface Modeling 03-14-02-0011

This also appears in SAE International Journal of Engines-V130-3EJ

Prediction of combustion system performance in the design stage via simulation tools can facilitate the reduction of iterations in the testing stage. Simulation tools can be used not only to predict the overall system performance for a certain set of hardware but can also be used to optimize the hardware. In this work, we intend to demonstrate the approach of Response Surface Modeling (RSM) to optimize the geometries of combustion systems from a performance and emission perspective. The Gaussian Process RSM algorithm, supplemented by Uniform Latin Hypercube (ULH) and Incremental Space Filler (ISF) Design of Experiment (DOE), has been used to arrive at an optimized piston bowl geometry for a Direct Injection (DI) diesel engine, having the potential to perform well both at the rated power and maximum torque operating points. Three principal piston bowl parameters have been identified for optimizing the geometry: (a) Bowl diameter, (b) Bowl depth, and (c) Bowl angle. A sensitivity analysis shows the bowl diameter to be the dominant geometry parameter in influencing the Indicated Mean Effective Pressure (IMEP) of the engine. The IMEP increases with a reduced bowl diameter, but at the expense of increased oxides of nitrogen (NOx). Within our parameter range of investigation, bowl depth was observed to be less influential than the bowl diameter, and the bowl angle was found to be the least influential of the three parameters in affecting the engine performance. Due to the strong nonlinearity of the combustion problem, the generated three-dimensional (3D) RSM surface manifested an intricate shape, highlighting the importance of an appropriate algorithm selection to minimize the prediction error. In the end, the competency of the parallel coordinate chart has been shown to prove it as a smart and elegant tool for a multi-objective optimization problem.


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