Parameters describing soot particle processes are generally derived from a limited number of experimental studies. These parameters then have to be carefully calibrated for different operating conditions in internal combustion engine applications. This paper presents an innovative calibration procedure for soot simulation in Diesel engines. A Diesel engine is simulated using the Stochastic Reactor Model engine code, which is implemented with the Moment Projection Method for handling the soot particle dynamics. The main advantage of the engine-soot model is its low computational cost. The model is then coupled with an advanced statistical toolkit, Model Development Suite, where the Hooke-Jeeves algorithm is adopted to calibrate seven soot model parameters automatically based on the measurement data. The ability of the integrated code for soot model calibration is evaluated by simulating the soot formation and oxidation processes in a heavy-duty Diesel engine which is operated under 18 different conditions. Results suggest that the integrated code is able to calibrate the soot model parameters effectively. A significant improvement in the match between the simulation results and experimental soot emission is obtained after calibration.

描述烟灰颗粒过程的参数通常来自有限的实验研究。然后必须针对内燃机应用中的不同运行条件仔细校准这些参数。本文提出了一种创新的校准程序，用于柴油机中烟灰的模拟。使用随机反应堆模型引擎代码模拟柴油引擎，该引擎代码通过矩投影法实现，用于处理烟尘颗粒动力学。发动机烟灰模型的主要优点是计算成本低。然后，将模型与高级统计工具集“模型开发套件”结合使用，其中使用Hooke-Jeeves算法根据测量数据自动校准七个烟灰模型参数。通过模拟在18种不同条件下运行的重型柴油机中的烟灰形成和氧化过程，可以评估烟灰模型校准的集成代码的能力。结果表明，该集成代码能够有效地校准烟灰模型参数。校准后，仿真结果与实验烟尘排放量之间的匹配度有了显着改善。