A Statistical Approach to Improve the Accuracy of the DPF Simulation Model under Transient Conditions
Cars with diesel engines are commonly equipped with a Diesel Particulate Filter (DPF) to reduce their emissions of particulate matter (PM). Because the pressure drop within the DPF reduces engine performance, it must be predicted with accuracy. The purpose of this study was to improve the accuracy of a DPF simulation model under transient conditions by parameter optimization. The DPF model under consideration consists of an inlet channel, a cake layer, wall layer, and an outlet channel. The pressure drop is influenced by the location, mass, and density of the deposited soot. Therefore, the model includes the following sub-models: Sub-model 1: Calculates the soot density deposited in the wall layer Sub-model 2: Computes the filtration efficiency and mass of the wall and cake layer Sub-model 3: Calculates the soot density deposited in the cake layer Because the sub-models include some empirical formulae, the first step in refining the model was to optimize their fitting parameters.