Hybrid Numerical–Artificial Neural Network Study of Chemically Reactive MHD Nanofluid Flow Incorporating Thompson–Troian Slip and Stefan Blowing

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    S. Harinath Reddy,

  • R. Chandra Sekhar Reddy,

  • Aruna Ganjikunta,

  • P. Ramakrishna Reddy,

  • P. Chandra Reddy,

  1. Assistant Professor, Department of Mathematics, Annamacharya University, Rajampet, Andhra Pradesh, India
  2. Assistant Professor, Department of Mathematics, Rajeev Gandhi Memorial College of Engineering and Technology, Nerawada, X’ Roads, Nandyala, Andhra Pradesh, India
  3. Associate Professor, Department of Mathematics, Malla Reddy (MR) Deemed to be University, Secunderabad, 500100, Hyderabad, Telangana, India
  4. Professor, Department of Mathematics, PBR Visvodaya Institute of Technology and Science, Kavali, Andhra Pradesh, India
  5. Assistant Professor, Department of Mathematics, Annamacharya University, Rajampet, Andhra Pradesh, India

Abstract

Boundary layer behaviour in chemically reactive nanoliquid is significantly affected by surface conditions, magnetic fields, heat and mas transfer mechanisms. However, the combined impact of Stefan blowing and nonlinear Thompson–Troian slip under inclined magnetic fields remains mostly unexplored, particularly in mixed convection flows. In this research work, the flow of a chemically reactive nanoliquid over a permeable surface is investigated by considering the Troian slip, inclined magnetic fields, Stefan blowing, and mixed convection. The Buongiorno nanofluid model is utilized to incorporate Brownian motion and thermophoretic diffusion. By similarity transformetions, the governed expressions are simplified into ordinary differentials and solved using a hybrid approach, such as shooting and an Artificial Neural Network (ANN) technique. The ANN guesses show sturdy pact with numerical outcomes, yielding a maximum error of 6.113×10−10 for skin friction, Nusselt, and Sherwood numbers. Findings indicate that Stefan blowing decreases fluid velocity while increasing the layer of temperature. Brownian motion and thermophoresis significantly enhance thermal and solutal profiles, whereas surface slip mitigates these effects. The inclined magnetic field reduces flow velocity and skin friction due to the Lorentz force but boosts heat and mass transfer rates. Mixed convection enhances momentum exchange at the wall, while the chemical reaction reduces nanoparticle concentration.

Keywords: Inclined magnetic field, mixed convection, chemically reactive nanofluid, Thompson-Torian slip, Stefan blowing, Numerical-ANN Approach.

How to cite this article:
S. Harinath Reddy, R. Chandra Sekhar Reddy, Aruna Ganjikunta, P. Ramakrishna Reddy, P. Chandra Reddy. Hybrid Numerical–Artificial Neural Network Study of Chemically Reactive MHD Nanofluid Flow Incorporating Thompson–Troian Slip and Stefan Blowing. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
S. Harinath Reddy, R. Chandra Sekhar Reddy, Aruna Ganjikunta, P. Ramakrishna Reddy, P. Chandra Reddy. Hybrid Numerical–Artificial Neural Network Study of Chemically Reactive MHD Nanofluid Flow Incorporating Thompson–Troian Slip and Stefan Blowing. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=243964


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Ahead of Print Subscription Original Research
Volume 14
03
Received 11/04/2026
Accepted 23/04/2026
Published 15/05/2026
Publication Time 34 Days
53

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