Advanced Modeling of Diesel Particulate Filters to Predict Soot Accumulation and Pressure Drop

Paper #:
  • 2011-24-0187

Published:
  • 2011-09-11
Citation:
Cozzolini, A., Mulone, V., Abeyratne, P., Littera, D. et al., "Advanced Modeling of Diesel Particulate Filters to Predict Soot Accumulation and Pressure Drop," SAE Technical Paper 2011-24-0187, 2011, https://doi.org/10.4271/2011-24-0187.
Pages:
21
Abstract:
Diesel particulate filters (DPFs) are recognized as the most efficient technology for particulate matter (PM) reduction, with filtration efficiencies in excess of 90%. Design guidelines for DPFs typically are: high removal efficiency, low pressure drop, high durability and capacity to resist high temperature excursions during regeneration events. The collected mass inside the trap needs to be periodically oxidized to regenerate the DPF. Thus, an in-depth understanding of filtration and regeneration mechanisms, together with the ability of predicting actual DPF conditions, could play a key role in optimizing the duration and number of regeneration events in case of active DPFs.Thus, the correct estimation of soot loading during operation is imperative for effectively controlling the whole engine-DPF assembly and simultaneously avoidingany system failure due to a malfunctioning DPF. A viable way to solve this problem is to use DPF models. This paper presents a DPF model jointly developed by West Virginia University and University of Rome Tor Vergata. The fully analytical model is based on a single channel representation of the flow while the thermal and catalytic framework is based on a novel 2-layer approach Numerical results are compared with experimental data gathered at West Virginia University (WVU) engine laboratory using a Mack heavy-duty diesel engine coupled to a Johnson Matthey CCRT aftertreatment system. The engine test bench was equipped with a DPF weighing system to track soot loading over a customized engine operating procedure.The study shows that: a) Wall and washcoat layer present different regeneration and collection dynamics, whose behavior is important to capture back pressure and temporal evolution of the collected mass b) Advanced filtration and regeneration process treatment in the wall allow the use of constant wall and cake parameters; thus, the model can be used to track back pressure and mass history of DPFs under subsequent regeneration and loading processes, c) Filtration sub-model results are highly influenced by engine-out particle size distribution during deep bed filtration mechanism suggesting a possible implementation in conjunction with soot sensor devices, and d) Estimation of the mass trapped using the DPF sub-model affords the opportunity to define a possible control strategy for active regeneration and OBD diagnostics.
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