Artificial neural network prediction on free convection in a triangular chamber with a v-shaped corrugated bottom wall filled with nanofluid

Abstract

Artificial neural network (ANN) prediction on magnetohydrodynamic (MHD) free convection for complex systems using nanofluid plays a significant role in understanding and optimizing the system’s behaviour. The current research investigates how various governing parameters affect heat transport within a v-shaped corrugated bottom triangular chamber consisting of Al2O3-H2O nanofluid. The vertical wall is kept at a uniform low temperature, the inclined wall is adiabatic, and the v-shaped corrugated bottom wall is exposed to constant high temperature. The Galerkin weighted residual approach, based on the method of finite elements, is used to solve the dimensionless PDE problems with boundary constraints. The isotherms, streamlines, and average Nusselt numbers are displayed for various Hartmann numbers (0 ≤ Ha ≤ 100), Rayleigh numbers (103 ≤ Ra ≤ 106), and nanoparticles volume fractions (0.01 ≤ ϕ ≤ 0.05). It demonstrates how the average Nu is affected by Ha, Ra, and ϕ. Furthermore, it has been discovered that the average Nu increases with Ra and ϕ but decreases with Ha. However, the Levenberg-Marquardt back propagation method is employed for the artificial neural network (ANN) approach on 120 datasets to estimate the average Nu, demonstrating an excellent correlation with simulated data and an average R2 of 0.99689. MATLAB software is used to calculate the ANN predictions.

Keywords

• Artificial neural network (ANN)
• magnetohydrodynamic (MHD)
• Nanofluid
• Free convection
• Corrugated wall
• Triangular enclosure