Predicting flow status of a flexible rectifier using cognitive computing

Given the increasing demand for efficient fluid dynamics analysis, this study introduces a novel machine learning-based approach for predicting fluidic flow rate and reverse flow rate. Leveraging Multilayer Perceptron and Catboost models, the study aims to advance the analysis and prediction of flexible rectifier behavior. A dataset comprising multiple variables, including pressure, valve dimensions, and flow rate, was utilized to train and test the models. The study successfully demonstrated superior prediction accuracy compared to traditional methods, signifying a significant advancement in the field. These findings imply potential for optimization in industrial applications, thereby reducing waste and contributing to more sustainable practices. Finally, we packaged these machine learning models into GitHub, available at https://github.com/yhpeng6/FluidicRectifier .