DEVELOPMENT OF A CFD PREDICTIVE METHODOLOGY FOR UREA DEPOSIT RISK ASSESSMENT

This master's thesis analyses critical areas of diesel after-treatment systems and emissions, particularly nitrogen oxides (NOx), in light of current and upcoming EU emission standards. The aqueous urea solution (AdBlue) used in the SCR (Selective Catalytic Reducer) system for reducing nitrogen oxides undergoes complex chemical processes in the mixing part of the exhaust system, in which efficient urea decomposition is required. Incomplete processes will result in harmful solids build-up that will adversely affect system efficiency and may lead to component corrosion. This research highlights the importance of high-performance SCR systems and the role of efficient urea water solution mixers. It also stresses the growing utility of three-dimensional Computational Fluid Dynamics (3D-CFD) simulations, focusing on CONVERGE CFD software. This software allows for uniformity index analysis, surface chemistry reaction modelling, and the prediction of accurate flow fields. Moreover, it addresses urea deposit modelling and thermo-hydrolysis processes. This work shows the practical relevance of adopting advanced simulation tools in optimizing engine aftertreatment systems. Developing a urea deposit predictive tool can help meet the upcoming emission regulations cost-effectively, thus saving time and providing correct results on complex geometry designs following the industry pace.