Modeling nanofluid viscosity: comparing models and optimizing feature selection—a novel approach

Abstract

Abstract Background The accurate prediction of viscosity in nanofluids is essential for comprehending their flow behavior and enhancing their effectiveness in different industries. This research delves into modeling the viscosity of nanofluids and assessing various models through cross-validation techniques. The models are compared based on the root mean square error of the cross-validation sets, which served as the selection criteria. The main body of the abstract Four feature selection algorithms namely the minimum redundancy maximum relevance, F-test, RReliefF were evaluated to identify the most influential features for viscosity prediction. The feature selection based on physical meaning was the algorithm that yielded the best results, as outlined in this study. This methodology takes into account the physical relevance of most aspects of the nanofluid's viscosity. To assess the predictive performance of the models, a cross-validation process was conducted, which provided a robust evaluation. The root mean squared error of the validation sets was used to compare the models. This rigorous evaluation identified the most accurate and reliable model for predicting nanofluid viscosity. Results The results showed that the novel feature selection algorithm outclassed the established approaches in predicting the viscosity of single material nanofluid. The proposed feature selection algorithm had a root mean squared error of 0.022 and an r squared value of 0.9941 for the validation set, while for the test set, the root mean squared error was 0.0146, the mean squared error was 0.0157, the r squared value was 0.9924. Conclusions This research provides valuable insights into nanofluid viscosity and offers guidance on choosing the most suitable features for viscosity modeling. The study also highlights the importance of using physical meaning to select features and cross-validation to assess model performance. The models developed in this study can be helpful in predicting nanofluid viscosity and optimizing their use in different industrial processes.