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Technical Paper

Modeling and Controlling Active Regeneration of a Diesel Particulate Filter

2020-09-15
2020-01-2176
Heavy soot deposition in wall-flow type diesel particulate filters reduces engine output and fuel efficiency. This necessitates forced regeneration to oxidize soot via exothermic reactions in a diesel oxidation catalyst upstream of the Diesel Particulate Filter (DPF). Soot loading in the wall of the DPF during forced regeneration causes much greater pressure drops than cake deposition, which is undesirable because high pressure drops reduce engine performance. We show that the description of soot deposition using a DPF model is improved by using a shrinking sphere soot oxidation sub-model. We then use this revised model to analyze cake deposition during forced regeneration, and to study the effects of varying the forced regeneration temperature and duration on the local soot reaction rate and soot mass distribution in the radial and longitudinal directions of the DPF channels during forced regeneration.
Technical Paper

A Statistical Approach to Improve the Accuracy of the DPF Simulation Model under Transient Conditions

2019-01-15
2019-01-0027
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.
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