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

The present investigation examines the circulation of TiO2−H2O${\mathrm{TiO}}_{2} - {{\mathrm{H}}}_{2}{\mathrm{O}}$ and Ti6Al2V−H2O${\mathrm{Ti}}_{6}{\mathrm{Al}}_{2}{\mathrm{V}} - {{\mathrm{H}}}_{2}{\mathrm{O}}$ based 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, the governing equations are converted into ordinary differential equations using similarity transformation and then numerically solved. The findings are represented graphically, and the model's correctness is assessed using Gaussian Process Regression, Categorical Boost, Extreme Gradient Boosting, and Random Forest, with linear regression acting as a meta‐model. The closely related testing and training data show the model's consistency and stability. Magnetic field and inclination angle will decline the velocity, space, and temperature‐dependent internal heat generation factors will enhance the temperature. Raising the pollutant external source parameter raises concentration. In all the cases, Ti6Al2V−H2O${\mathrm{Ti}}_{6}{\mathrm{Al}}_{2}{\mathrm{V}} - {{\mathrm{H}}}_{2}{\mathrm{O}}$ shows better performance than TiO2−H2O${\mathrm{TiO}}_{2} - {{\mathrm{H}}}_{2}{\mathrm{O}}$ based nanofluid. The work's application ranges from fluid dynamics to waste management. By offering precise forecasts of nanofluid concentration, the proposed prediction model may aid in designing and optimizing waste discharge systems.