This article presents a novel approach to enhance the accuracy and efficiency of
three-dimensional (3D) selective catalytic reduction (SCR) simulations in
monolith reactors by leveraging high-fidelity urea–water solution computational
fluid dynamics (UWS-CFD) data. The focus is on estimating the nonuniformity of
NH₃ at the SCR inlet, crucial for achieving optimal performance in
aftertreatment systems. Due to its high computational cost, a CFD-only approach
is not feasible for transient drive cycle simulations aiming to accurately
predict SCR NOx conversion and NH₃ slip by accounting for the nonuniform NH₃
distribution at the SCR inlet. Therefore, the development of reduced order or
fast models is of prime importance. By employing artificial neural networks
(ANNs), we establish a framework that eliminates the need for computationally
expensive CFD calculations, allowing for swift and precise 3D SCR simulations
under various injection, mixing region, and exhaust conditions.
The methodology involves conducting extensive CFD simulations across a range of
operating parameters to create a comprehensive dataset. This dataset is then
utilized to train an ANN, enabling the accurate prediction of inlet NH₃
distribution for 3D SCR simulations within the GT-SUITE environment. Validation
assessments demonstrate the trained ANN’s capability to predict inlet
distributions even for conditions not explicitly included in the training
dataset, attesting to its robust generalization.
The trained ANN model serves as a powerful tool for optimizing aftertreatment
system parameters, paving the way for enhanced design efficiency. Through
systematic parameter optimization, the proposed methodology aims to minimize
urea deposition and maximize NOx conversion efficiency while simultaneously
minimizing NH₃ slip. The integration of UWS-CFD and ANN modeling not only
expedites simulation processes but also contributes to the advancement of
aftertreatment system design by providing a reliable and accurate predictive
tool for engineers and researchers in the field.
