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A Fast Modeling Approach for the Numerical Prediction of Urea Deposit Formation
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Citation: Budziankou, U., Quissek, M., and Lauer, T., "A Fast Modeling Approach for the Numerical Prediction of Urea Deposit Formation," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(3):1337-1355, 2020, https://doi.org/10.4271/2020-01-0358.
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 increase backpressure and impair 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 methodology for the modeling of deposit formation risk. A new determination of the initial footprint of the spray, where the deposit formation is inhibited, is proposed. The threshold values for the evaluation of the film transport were validated based on experimental results. To achieve a more realistic simulation in terms of wall wetting and cooling, the impingement heat transfer as well as the impingement model 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 substitution 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 and an exhaust aftertreatment system. The comparison of measured and simulated results shows the capability of the presented modeling approach to predict the position and the severity of solid deposits.