Abstract Summary (Max 250 words)
This work proposes a new methodology for predicting flow behavior in chemical reactors, applied to the specific case of Confined Imping Jets (CIJs), aiming to significantly reduce computational time and memory requirements. The proposed approach consists of two main steps: (i) dimensionality reduction of CFD data using Proper Orthogonal Decomposition (POD) to extract the dominant flow structures, and (ii) the development of Deep Neural Networks (DNNs) to predict POD time-independent functions and associated time-varying coefficients for new operating conditions. CIJs consist of a confined cylindrical chamber where two opposed streams are injected. For inlet Reynolds numbers above 150, a self-sustainable chaotic flow regime is established. In this work, the Reynolds number was selected as the governing operating parameter and varied between 300 and 600. 2D CFD simulations were performed, and POD was applied to the resulting velocity fields. The POD analysis showed that the first mode represents a steady state flow, while the subsequent modes correspond to large-scale vortical structures. Two independent DNNs were trained: one to predict the spatial modes (time-independent functions) and another to predict the temporal coefficients. The networks were trained, and their performance was evaluated for an unseen condition (Re = 450). The DNN predictions showed very good agreement with the POD data obtained from CFD, enabling an accurate reconstruction of the velocity field. This study represents the first application of DNNs to predict POD data for new operating conditions in CIJs and demonstrates strong potential to accelerate flow-field predictions in chemical reactors.
