Intelligent RKF–BLMS-ANN prediction of unsteady bioconvective nanofluid flow containing swimming gyrotactic microorganisms under HOC reaction and SOVS effects
Year: 2026
Type: article
Abstract: This study presents an intelligent numerical framework for predicting the unsteady squeezing flow of a bioconvective nanofluid containing gyrotactic microorganisms between two parallel plates. The effects of second-order velocity slip, higher-order chemical reaction, Brownian motion, thermophoresis, and bioconvective transport are incorporated into the mathematical formulation. The governing nonlinear partial differential equations are transformed into a coupled system of ordinary differential equations using suitable similarity transformations and are then solved numerically through the Runge–Kutta–Fehlberg shooting method. The obtained numerical solutions are used to construct high-quality datasets for training, validating, and testing a Back-Propagated Levenberg–Marquardt Scheme Artificial Neural Network (BLMS-ANN). The physical results reveal that the squeezing parameter significantly redistributes the velocity field and enhances core-region momentum transport, while the slip parameters strongly modify the near-wall velocity gradient and shear stress. Increasing the chemical reaction parameter suppresses nanoparticle concentration, whereas thermophoresis enhances the temperature field but reduces concentration distribution. Moreover, the Peclet number plays a dominant role in controlling microorganism transport by strengthening convective effects relative to diffusion. The developed BLMS-ANN model demonstrates excellent predictive capability, with very low validation mean squared errors, correlation coefficients of R = 1, and residual errors tightly clustered around the zero-error line. The developed BLMS-ANN model reduced the computational time from 4.1 s required by the RKF45 solver to 1.1 s for prediction, corresponding to a speed-up factor of approximately 3.73 and a computational time reduction of 73.17%. These findings confirm that the proposed intelligent framework provides a highly accurate, stable, and computationally efficient surrogate model for predicting complex nonlinear transport phenomena in bioconvective nanofluid squeezing flows.
Institutions: Babol Noshirvani University of Technology, Biruni University
Language: English
Subfield: Fluid Flow and Transfer Processes
Field: Chemical Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,190
OpenAlex ID: W7222975235