ArticleDiscover nano2026
Computational and machine learning analysis of bio-convective magnetized Ree-Eyring nanofluid flow on Riga plate with thermal radiation and activation energy.
Article in Discover nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This work examines bio-convective magnetized Ree-Eyring nanofluid flow on a convectively heated Riga surface. Thermal diffusion is controlled through use of Cattaneo-Christov thermal flux and Brownian motion effects along with thermal radiations. The flow is also affected by Darcy-Forchheimer model, activation energy and presence of microorganisms. The leading equations have valuated initially using bvp4c technique. The obtained set from this numerical technique is then used as a dataset to implement ANN technique in current problem. It has deduced in this work that, optimal Mean Squared Error (MSE) convergence have attained at epochs 199, 170, 275 and 216 for four scenarios. With growth in Weissenberg number, magnetic parameter and Forchheimer term there is decline in velocity profiles. Thermal profiles augment with growth in radiation parameter, Eckert number and Brownian motion parameter while decline with progression in thermal relaxation time factor. Growing the reaction parameter decreases concentration profiles by accelerating species consumption and thinning the concentration boundary layer. Increasing activation energy enhances concentration profiles by slowing the reaction rate, thereby preserving more reactant species and thickening the concentration boundary layer. The reliability of the present results was confirmed by comparing with the established results. Under the corresponding limiting conditions, the current results show close agreement with the benchmark data, thereby validating the accuracy and consistency of the present mathematical formulation and numerical procedure. The comparison is performed for variations in Prandtl number, with the absolute error remaining very small (0.0000078-0.0008990) and the percentage error limited to 0.00078% to 0.0800%. The proposed computational and machine-learning framework has potential applications in advanced thermal management, electromagnetic flow control, bio-convective transport systems, microfluidic devices, biomedical engineering, and energy-conversion technologies.
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