Development and Validation of a One-Dimensional Computational Model of the Continuously Regenerating Diesel Particulate Filter (CR-DPF) System
Date Published: 2005-04-11
Paper Number:2005-01-0954
DOI: 10.4271/2005-01-0954
Citation:
York, A., Cox, J., Watling, T., Walker, A. et al., "Development and Validation of a One-Dimensional Computational Model of the Continuously Regenerating Diesel Particulate Filter (CR-DPF) System," SAE Technical Paper 2005-01-0954, 2005, doi:10.4271/2005-01-0954.
Diesel emissions legislation continues to tighten around the world, and Particulate Matter (PM) emissions are currently the focus of much attention. Diesel PM can be controlled using Diesel Particulate Filters (DPFs), which can effectively reduce the level of carbon (soot) emissions to ambient background levels.
In the Heavy Duty Diesel (HDD) area, the Continuously Regenerating Trap (CRT
®
) [
1
] has been widely applied in the retrofit market. This system will henceforth be referred to as the Continuously Regenerating DPF (CR-DPF). There are currently over 100,000 of these systems in use in retrofit applications worldwide. This system comprises a specially formulated Diesel Oxidation Catalyst (DOC) upstream of a DPF; the NO
2
generated by the DOC is used to combust the carbon collected in the DPF at low temperatures.
A model describing the performance of the CR-DPF has been developed. This model comprises two basic components: i) a 1-D DOC model based on laboratory microreactor data, and ii) a 1-D DPF model. The DOC model includes Langmuir-Hinshelwood expressions to describe the kinetics of the NO, CO and HC oxidation reactions. This model has been validated using engine data measured over both low and high temperature driving cycles. The DPF model has been validated using engine bench pressure drop data measured over the ESC (European Stationary Cycle).
These 2 models have been combined to create a full model of the CR-DPF system, which has been validated over a wide range of conditions. Very good agreement between the experimental data and the model has been achieved.
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