Williamson Nanofluid Flow and Transport in an Asymmetric Porous Tapered Channel under Multiple Slip Conditions Using Perturbation and Supervised Machine Learning Models

H. Kamlesh¹, E. P. Siva^1,*, P. Bathmanaban², O. D. Makinde³, Dharmendra Tripathi⁴
1 Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu Dt., Tamil Nadu, India
2 Department of Science & Humanities (Mathematics), Jeppiaar Institute of Technology Kunnam, Sunguvarchatram, Sriperumbudur, Chennai, Tamil Nadu, India
3 Faculty of Military Science, Stellenbosch University, Stellenbosch, South Africa
4 Department of Mathematics, National Institute of Technology Uttarakhand, Srinagar, India
* Corresponding Author: E. P. Siva. Email: email
(This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.081147

Received 06 March 2026; Accepted 27 April 2026; Published online 14 May 2026

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Abstract

The current study comprehensively investigates Williamson nanofluid flow and transport in an asymmetric porous tapered channel under varying slip conditions, using both analytical and supervised machine learning approaches. This mathematical model integrates thermophoresis, Brownian motion, the Soret and Dufour effects, thermal radiation, and a transverse magnetic field to accurately describe thermosoluble transport phenomena relevant to biomedical contexts. The non-Newtonian Williamson formulation is used to explain how fluids, such as blood, dilute when sheared. Darcy resistance is used to describe porous structures in tissue scaffolds, capillary networks, and dialysis membranes. A perturbation method is used to find analytical solutions that show how key dimensionless parameters affect the profiles of velocity, temperature, concentration, Nusselt number, Sherwood number, skin friction, and pressure gradient. Supervised machine learning models, including artificial neural networks, are also used to predict heat and mass transfer properties and confirm analytical trends with a high degree of accuracy. The results show that increasing the Hartmann number reduces fluid motion due to Lorentz force resistance by approximately 14%, while the Williamson parameter increases shear-thinning and increases velocity by approximately 9%. Thermal radiation significantly broadens the temperature distribution, increasing heat transfer by 12%. The combination of perturbation analysis and supervised machine learning models demonstrates strong predictive power and makes the results more reliable. The integrated analytical-machine learning framework provides essential insights for enhancing nanoparticle-mediated drug delivery and advancing hyperthermia cancer treatment through regulated thermosolute transport in porous biological tissues.