Spatial Conversion Profiles within an SCR in a Test Exhaust System with Injection of Ammonia Gas Modelled in CFD using the Porous Medium Approach

Paper #:
  • 2010-01-2089

Published:
  • 2010-10-25
Citation:
Sturgess, M., Benjamin, S., and Roberts, C., "Spatial Conversion Profiles within an SCR in a Test Exhaust System with Injection of Ammonia Gas Modelled in CFD using the Porous Medium Approach," SAE Technical Paper 2010-01-2089, 2010, https://doi.org/10.4271/2010-01-2089.
Pages:
10
Abstract:
Modeling of SCR in diesel exhaust systems with injection of urea spray is complex and challenging but many models use only the conversion observed at the brick exit as a test of the model. In this study, the case modeled is simplified by injecting ammonia gas in nitrogen in place of urea, but the spatial conversion profiles along the SCR brick length at steady state are investigated. This is a more rigorous way of assessing the ability of the model to simulate observations made on a test exhaust system. The data have been collected by repeated engine tests on eight different brick lengths, all which were shorter than a standard-sized SCR. The tests have been carried out for supplied NH₃ /NOx ratios of a 1.5, excess ammonia, a 1.0, balanced ammonia, and a 0.5, deficient ammonia. Levels of NO, NO₂ and NH₃ have been measured both upstream and downstream of the SCR using a gas analyzer fitted with ammonia scrubbers to give reliable NOx measurements. A CFD model based on the porous medium approach has incorporated a kinetic scheme available in the open literature. Comparison of CFD simulations with observed data is presented and the results are discussed. NOx conversion is significant in the first 30 mm of the brick for a 0.5 and in the first 90 mm for a 1.0 and 1.5. The ammonia level influences NOx conversion, which is generally under-predicted by the current model for a 0.5 and over-predicted for a 1.5. Measurements show that NO₂ conversion exceeds NO conversion in the first section of the monolith, which the model fails to predict.
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