Use of artificial neural network for optimization of irreversibility analysis in radiative Cross nanofluid flow past an inclined surface with convective boundary conditions - Ali Farhan; Zaib Aurang; Zafar Syed Sohaib; Khan Umair; Ahmed Muhammad Faizan; Elattar Samia; Khashi’ie Najiyah Safwa (High Temperature Materials and Processes)
This study uses an artificial intelligence neural network to estimate the solution for a Cross-nanofluid containing gyrotactic microorganisms on an inclined surface. Furthermore, the concept of minimizing entropy has been taken into account. The energy and concentration equations take into account the importance of thermal radiation, convective flow, heat source/sink, and chemical processes. By applying an appropriate similarity transformation, the modeled equations are converted into ordinary differential equations. The equations are solved numerically using an artificial neural network (ANN) that employs the Levenberg–Marquardt approach. To evaluate the correctness of the proposed model, the results of training, testing, and validation are analyzed using the performance charts, error histograms, transition state analysis, comparisons between bvp4c and ANN, and regression plots. In addition, a bigger estimation of the radiative variable and the Biot number results in a rise in the temperature of the fluid. Conversely, greater values of the Prandtl number lead to a diminution in the temperature. Growing the Weissenberg parameter enhances the velocity and decreases both the Bejan number and entropy generation. To showcase the accuracy of the numerical approach that has been applied, a comparison table is provided, which displays a remarkable level of agreement when compared to previously published data.