Implementation of stacking regressor model on the flow induced by TiO2-H2O and Ti6Al4V-H2O nanofluid with waste discharge concentration

ThepresentinvestigationexaminesthecirculationofTiO(2)-H(2)OandTi(6)Al(2)V-H(2)Obased nanofluids while considering the concentration of waste discharge.An innovative stacking regressor model is used to increase prediction accuracy.Using Shooting and Runge Kutta Fehlberg's fourth and fifth-order schemes, thegoverning equations are converted into ordinary differential equations usingsimilarity transformation and then numerically solved. The findings are rep-resented graphically, and the model's correctness is assessed using GaussianProcess Regression, Categorical Boost, Extreme Gradient Boosting, and RandomForest, with linear regression acting as a meta-model. The closely related testingand training data show the model's consistency and stability. Magnetic field andinclination angle will decline the velocity, space, and temperature-dependentinternal heat generation factors will enhance the temperature. Raising the pollu-tant external source parameter raises concentration. In all the cases,Ti6Al2V-H(2)Oshows better performance thanTiO(2)-H(2)Obased nanofluid. The work'sapplication ranges from fluid dynamics to waste management. By offering precise forecasts of nanofluid concentration, the proposed prediction model mayaid in designing and optimizing waste discharge systems