A Phenomenological Carbon Monoxide Model for Diesel Engines

2021-01-0375

04/06/2021

Features
Event
SAE WCX Digital Summit
Authors Abstract
Content
Intensified emission regulations as well as consumption demands lead to an increasing significance of carbon monoxide (CO) emissions for diesel engines. On the one hand, the quantity of CO raw emissions is important for emission predictions as well as for the exhaust gas after treatment. On the other hand, CO emissions are also important for predicting combustion efficiency and thus fuel consumption, since a part of unreleased chemical energy of the fuel is still bound in the CO molecules. Due to these reasons, a simulation model for predicting CO raw emissions was developed for diesel engines based on a phenomenological two-zone model. The CO model takes three main sources of CO emissions of diesel engines into account: Firstly, it contains a sub model that describes CO from local understoichiometric areas. Secondly, CO emissions from overmixed regions are considered. In these regions, the air-fuel mixture is too lean and consequently the temperatures are too low for CO to fully oxidize. Thirdly, CO emissions from cold peripheral zones near cylinder walls are determined in another sub model. To simplify the calibration process, the peripheral zone sub model is coupled to an existing nitrogen oxide (NO) peripheral zone model. For the CO model a one-step reaction mechanism for the CO oxidation was developed. This mechanism is applied to model the kinetically controlled oxidation of CO, which is initially produced by imperfect or incomplete combustion. The simulation results of the CO model have been validated against experimental data from two direct injection diesel engines. The simulation results show a high degree of accuracy for both engines.
Meta TagsDetails
DOI
https://doi.org/10.4271/2021-01-0375
Pages
12
Citation
Schnapp, C., Yang, Q., Grill, M., Bargende, M. et al., "A Phenomenological Carbon Monoxide Model for Diesel Engines," SAE Technical Paper 2021-01-0375, 2021, https://doi.org/10.4271/2021-01-0375.
Additional Details
Publisher
Published
Apr 6, 2021
Product Code
2021-01-0375
Content Type
Technical Paper
Language
English