Browse Publications Technical Papers 2020-01-0358
2020-04-14

A Fast Modeling Approach for Prediction of SCR Deposits – Implementation and Validation with Advanced Optical Techniques 2020-01-0358

The permanently tightening emission regulations for NOx pollutants force further development of automotive exhaust aftertreatment systems with selective catalytic reduction (SCR). Of particular interest is the long-term reliability of SCR-systems with regard to unfavorable operating conditions, such as high injection rates of urea water solution (UWS) or a low exhaust gas temperature. Both of them may lead to formation of solid deposits which decrease system efficiency by increasing backpressure and impairing ammonia uniformity. A fast modeling approach for numerical prediction of deposit formation in urea SCR systems is desired for optimization of system design. This paper presents a modified Smith´s methodology for the modeling of deposit formation risk. A new criterion for determination of the initial foot print of the spray, where the deposit formation is inhibited, is proposed. The threshold values for the evaluation of the liquid film dynamic were validated based on experimental results. Furthermore, for a better prediction of the liquid film pathways, a new approach for realistic modeling of the film viscosity was developed. To achieve a more realistic simulation in terms of wall wetting and cooling, the heat transfer model as well as the impingement diagram were modified based on optical investigations. In order to accomplish the modeling of deposit formation with typical time ranges of several minutes, a recently developed injection source approach was applied. The substituting of the Lagrange-particles with source terms of mass, momentum and energy allowed to reduce simulation time by a factor of 30. The presented modeling approach was validated against both, the experimental data from an optical box with heat resistant glass and a real exhaust aftertreatment system. The comparison of measured and simulated results shows the capability of the presented modelling approach to predict the position and the severity of solid deposits.

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