Statistical Analysis of Flow Parameters for the Graphical Simulation Outputs

Abstract

System dynamics simulation software, in general, depicts graphical interpretations. The values of the parameters, on the other hand, are required for prediction. The goal of this research is to develop a novel multivariate model that can predict flow parameters while simulating flow under various scenarios. The project involves looking for variations in the streamline and constructing a new multivariate model for each elliptic cylinder system's velocity magnitude. Furthermore, the flow zones were split into three groups based on streamline behavior. As a result, utilizing simulation outputs, new models for flow zones are developed using linear and semiparametric regression. The best fitted model for each flow region was determined using mean square error (MSE), root of mean square error (RMSE), and mean absolute percentage error (MAPE). Based on the fitted smoothing curve of the velocity magnitude, a summary statistic and variability may be assessed. The presented models can be used to predict magnitude in any point of fluid flow using these models.