Integrating High-Fidelity Urea–Water Solution—Computational Fluid Dynamics Simulations for Fast Three-Dimensional Selective Catalytic Reactor Modeling Using Artificial Neural Networks

<div>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.</div> <div>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.</div> <div>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.</div>