CFD Study of Sensitivity Parameters in SCR NOx Reduction Modeling

2014-01-2346

09/30/2014

Event
SAE 2014 Commercial Vehicle Engineering Congress
Authors Abstract
Content
The Diesel engine combustion process results in harmful exhaust emissions, mainly composed of Particulate Matter (PM), Hydro Carbon (HC), Carbon monoxide (CO) and Nitrogen Oxides (NOx). Several technologies have been developed in the past decades to control these diesel emissions. One of the promising and well matured technology of reducing NOx is to implement Selective Catalytic Reduction (SCR) using ammonia (NH3) as the reducing agent. For an effective SCR system, the aqueous urea solutions should be fully decomposed into ammonia and it should be well distributed across the SCR. In the catalyst, all the ammonia is utilized for NOx reduction process. In the design stage, it is more viable to implement Computational Fluid Dynamics (CFD) for design iterations to determine an optimized SCR system based on SCR flow distribution. And in later stage, experimental test is required to predict the after-treatment system performance based on NOx reduction.
The SCR model predicts the NH3 formation from urea decomposition and it is quantified at the SCR inlet, whereas experimental data involves the NOx reduction process. In order to validate the numerical predictions, the modeling needs to include surface reactions happening in the SCR catalyst bed. There are many factors that affect NOx conversion process, such as exhaust temperature, NO2/NOx ratio, catalyst coating, oxidation, etc. To model SCR Kinetics, various input parameters are required and also the reaction process needs to be well defined. The objective of this work is to conduct urea SCR spray modeling studies to focus on these input parameters and to observe its influence over NOx reduction process.
Meta TagsDetails
DOI
https://doi.org/10.4271/2014-01-2346
Pages
9
Citation
Sampath, M., and Lacin, F., "CFD Study of Sensitivity Parameters in SCR NOx Reduction Modeling," SAE Technical Paper 2014-01-2346, 2014, https://doi.org/10.4271/2014-01-2346.
Additional Details
Publisher
Published
Sep 30, 2014
Product Code
2014-01-2346
Content Type
Technical Paper
Language
English