AI-powered prediction of hybrid nanofluid dynamics over a cylinder via LM optimized neural network approach
Muhammad Imran Asjad, Wantao Jia, Syed Tauseef Saeed, Jihad Younis, Mubashir Qayyum, Abdulrahman A. Almehizia
Abstract
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 (TiO 2 ) nanoparticles in water (H 2 O) 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.