Numerical modelling and non-dimensional analysis of a diesel oxidation catalyst with focus on NO2 reduction

INTERNATIONAL JOURNAL OF ENGINE RESEARCH(2024)

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Abstract
A diesel oxidation catalyst (DOC) is widely used to oxidize partial combustion by-products, such as unburned hydrocarbons and carbon monoxide (CO), and nitric oxide (NO) from compression ignition (CI) engines. Numerical modelling of the DOC, reported in the literature, often does not predict the performance of the DOC accurately over a wide range of engine operating conditions because only a few chemical reactions are considered. The objective of this work is to develop a robust 1D transient numerical model, capable of accurately predicting the conversion efficiency of the engine-out total hydrocarbon (THC), CO and NO in a conventional diesel combustion mode. Based on experimental observations of the low temperature oxidation of CO and THC with nitrogen dioxide (NO2), the developed numerical model not only include oxidation reactions with oxygen but also the NO2 reduction and selective catalytic reduction (SCR) reactions to improve the robustness of the model. From the non-dimensional analysis, the kinetics and mass transfer limitation of exhaust gas species oxidation and their dependence on exhaust gas properties and DOC geometric parameters are identified. Relative magnitudes of resistances to chemical reaction and mass transfer reveal that CO oxidation in the DOC transitions from kinetically controlled to a mass transfer-controlled regime at the CO oxidation light-off temperature (218 degrees C DOC inlet temperature), whereas, THC oxidation is in the kinetic controlled regime even at 377 degrees C exhaust gas temperature. NO2 reduction in the DOC is always in the kinetic controlled regime; however, NO oxidation reaction transitions from kinetic to a mass transfer-controlled regime at 215 degrees C.
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Key words
Diesel oxidation catalyst,NO2 reduction,transient simulation,non-dimensional analysis,kinetic controlled regime,mass transfer-controlled regime
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