Unified Hermite polynomial collocation and artificial neural network methods to analyse viscoelastic-bioconvective fluid behavior in a circular porous slider system

Abstract The present study investigates the transport properties of a viscoelastic Maxwell fluid over a circular porous slider under the combined impacts of quadratic thermal radiation, activation energy, chemical reaction and bioconvection. Similarity transformations are employed to reduce the governing equations for momentum, energy, concentration and micro-organism movement into a dimensionless system which is then solved using the Hermite Polynomial Collocation Method (HPCM). Furthermore, a trained artificial neural network (ANN) with Levenberg–Marquardt algorithm is applied for prediction of transport profiles. The results reveal that an increase in Reynolds number causes an increase in transverse velocity and a decrease in temperature due to the stronger convective transport. Quadratic radiation amplifies thermal energy, and its effect on concentration depends on the Schmidt number and reaction rate. Good agreement between numerical and ANN predictions confirms model reliability. The results can be used as guidelines for polymer processing, reactive transport, thermal systems, and bioconvective applications.