AI-powered prediction of hybrid nanofluid dynamics over a cylinder via LM optimized neural network approach

This study examines the effect of chemical reaction and heat source/sink on steady two-dimensional mixed convective boundary layer flow of a hybrid nanofluid (HNF) over an inclined permeable plate/cylinder. The HNF is constructed by dispersing copper oxide (CuO) and titanium dioxide (TiO₂) nanoparticles in water (H₂O) as the base fluid. The model considers convective boundary conditions in both temperature and nanoparticle concentration. The resulting governing partial differential equations (PDEs) are reduced to a scheme of nonlinear ordinary differential equations (ODEs) via similarity transformations and numerically resolved by means of MATLAB's bvp4c solver. Originality of this paper deceptions in integrating a numerical solver with an optimized feed-forward artificial neural network (FF-ANN) based on the Levenberg-Marquardt algorithm (LMA) to model HNF flow along with heterogeneous and homogeneous chemical reactions, heat source/sink, and inclination effects, a combination rarely explored in previous studies. The results indicate that porosity and inclination parameters reduce the velocity profiles, while increased concentration of nanoparticles and heat source/sink effect enhance thermal distribution. The LMA-ANN model possesses good predictive ability with the mean squared error (MSE) varying between 10^-08 and 10^-10. There is excessive consistency among the numerical solutions, as presented. The outcomes showcase the huge potential of HNFs and ANN-enhanced modeling to boost heat and mass transfer in complex engineering and industrial operations.