Accelerated Computational Fluid Dynamics (CFD)-Based Quantitative Prediction of Urea Deposition in Selective Catalytic Reduction (SCR) Systems

Mitigation of harmful emissions from oil-based engines is essential to avoid environmental pollution and comply with various NO_x regulations across the globe. This can be partially achieved by injecting urea to produce ammonia (NH₃), which reacts with NO_x in a catalyst to produce harmless nitrogen (N₂) and water vapor (H₂O). However, urea deposition in a selective catalytic reduction (SCR) system poses a significant threat to the NO_x removal process by not only reducing the urea conversion rate but also blocking the incoming flow and causing an additional pressure drop. Numerical modeling of this urea deposit formation involves multiphase flow physics coupled with accurate heat transfer calculations. Additionally, since urea decomposes into various by-products like biuret, cyanuric acid (CYA), and ammelide, detailed chemical kinetics modeling is equally important. Accurate and fast computational fluid dynamics (CFD) simulations can help accelerate SCR system design cycles, leading to a reduction in experimental cost. In this study, we employ CONVERGE CFD to model the whole process from urea–water solution (UWS) injection to droplet evaporation and decomposition (using 12-step detailed-chemistry), film formation, and final deposition as a solid. A new spray-wall interaction model is introduced based on published experimental observations. The efficacy of the numerical model is demonstrated using an S-bend tube, where the UWS is injected just at the end of the S-bend. The predicted deposit mass and patterns are compared with the experiments, and good agreement is observed for three different operating conditions. A novel boundary morphing feature is activated to model the deformation of the tube walls because of urea deposition. Finally, to accelerate the simulations, a spray database approach is introduced. Coupled with the fixed-flow feature, this results in around 58% reduction in computational time without compromising accuracy. The present work thus provides a numerical framework to accurately capture urea deposition with a fast turnaround time.