AI‐driven analysis of thermal enhancement in solar field with magnetized ternary‐hybrid nanofluid flow past a stretching cylinder in Darcy–Forchheimer medium

Abstract In extrusion procedures for metal and plastic, stretching cylinders are essential. Carboxymethyl cellulose (CMC)/water‐based THNF () Darcy–Forchheimer flow via a stretching cylinder is the focus of the current study by applying artificial neural network (ANN). To the Crossflow, a THNF composite of , , and has been added, along with a CMC‐based fluid. A stochastic scheme artificial intelligence neural network (NN) in MATLAB is used to verify and cross‐check the results until they further converge to the Levenberg–Marquardt backpropagated model. Similarity variables contribute to the conversion of PDEs into the desired ODEs. The ND‐Solve technique is implemented as a numerical solver tool using the Wolfram Mathematica application, resulting in a statistical matrix data set and a linear graphical representation of the established model on several parameters. After an extensive conversion of the matrix‐formatted data into the necessary MATLAB notebook code language, the intact set of samples (100%) are split into training data (70%) along with testing and validation data (15%) before back propagation Levenberg–Marquardt scheme (BPLMS) is applied. Ten hidden neural units are used to continually train the data to get the mean squared error. The statistical tabular record contains regression expressions, fitness state functions, error histograms, state transition functions, and performance analysis. It is clearly visible from the images that the acquired numerical and graphical data matches the reference results, and the solution graphs against each parameter visually represent this as well. The minimal absolute error was also recorded, which contributed to the comprehension of the reliability of the acquired outcomes. Six significant parameters for seven scenarios are examined in the study: curvature parameter , Weissenberg parameter , porosity parameter , radiation parameter , Schmidt number , and chemical reaction parameter . The velocity profile is decreases with increasing values of curvature parameter and porosity parameter also, profile increases by increasing values of Weissenberg parameter . The temperature field increases as the radiation parameter rises and tends to decrease as the curvature parameter increases. The concentration profile is goes to retardations while increasing the Schmidt number . Moreover, profile increases with increasing values of chemical reaction parameter .