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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.

Experimental and numerical studies were conducted on diesel particulate filters (DPFs) under different soot loading conditions and DPF configurations. Pressure drops across DPFs with various mean pore diameters loaded with soots having different mean particle diameters were measured by introducing exhaust gases from a 2.2 liter inline four-cylinder, TCI diesel engine designed for use in passenger cars. A mechanistic hypothesis was then proposed to explain the observed trends, accounting for the effects of the soot loading regime in the wall and the soot cake layer on the pressure drop. This hypothesis was used to guide the development and validation of a numerical model for predicting the pressure drop in the DPF. The relationship between the permeability and the porosity of the wall and soot cake layer was modeled under various soot loading conditions.