ANN validation and RSM optimization of Cattaneo-Christov heat flux in MHD stenotic artery trihybrid nanofluid flow
- 1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632014 India
Abstract
This study investigates shape-dependent thermal transport in a blood-based Cu–Al₂O₃–Au trihybrid nanofluid using a local throat-level similarity model motivated by stenosed arterial geometry. The model incorporates magnetohydrodynamics, thermal radiation, viscous dissipation, porous-medium resistance, Darcy–Forchheimer drag, and Cattaneo–Christov non-Fourier heat-flux. The key novelty lies in combining mixed-shape nanoparticle modeling with response surface methodology (RSM) optimization and artificial neural network (ANN) surrogate prediction for arterial-flow configurations relevant to thermal therapy. Shape-dependent property correlations compare a fully spherical trihybrid suspension with a mixed-shape case (spherical Cu/Al₂O₃, cylindrical Au). Using similarity transformations, the governing equations are converted into coupled nonlinear ODEs and solved numerically with a collocation-based boundary-value solver. Parametric analysis covers magnetic parameter (0 ≤ M ≤ 3), Eckert number (0 ≤ Ec ≤ 1.5), radiation parameter (0 ≤ Rd ≤ 0.75), Darcy parameter (1 ≤ K ≤ 5), thermal relaxation parameter (0 ≤ αt ≤ 0.1), and nanoparticle volume fraction (0.03 ≤ ϕ ≤ 0.1). RSM with a face-centered central composite design applied over the bounded optimization space for prediction of Nusselt number (Nu), followed by feedforward ANN, identifies the high-M, high-ϕ, and low-Rd region as favorable for local wall heat-transfer enhancement. The solver-confirmed constrained optimum (M = 2.9935, ϕ = 0.0525, and Rd = 0.00012) yields Nu = 12.286, with ANN predicting Nu = 12.1721 (relative deviation: 0.93%). The mixed shape configuration increase mean velocity by 0.56–6.18% and mean temperature by 0.49–12.88% versus spherical particles. Overall, the combined numerical–RSM–ANN framework offers a reproducible route for screening design space and optimizing trihybrid nanofluid heat transfer in magnetically assisted arterial thermal-therapy modeling.
Clinical trial number: Not applicable. This study did not involve a clinical trial.
Related
- Thermal analysis of shape-dependent Fe3O4–CoFe2O4 hybrid nanofluid with Cattaneo–Christov heat flux and chemical reaction
- Novel concepts of Cattaneo-Christov fluxes for Darcy-Forchheimer flow featuring Buongiorno's nanofluid mathematical model through design of intelligent-based neuro-structures
- Thermal convective and flow dynamics of blood trihybrid nanofluid inside a stenosis artery for cardiovascular disease treatment using artificial intelligence approach.
- An analysis of homogeneous-heterogeneous chemical reactions, entropy generation, joule heating, variable thermal conductivity, Dufour effect, Cattaneo-Christov heat and mass flux, and MHD flow of the casson fluid in a rotating channel
- Optimization of Segmented Thermoelectric Generator with Wavy Structure and Nanofluid Cooling: Variable Structure
BibTeX
@article{kumari2026validation,
title = {{ANN validation and RSM optimization of Cattaneo-Christov heat flux in MHD stenotic artery trihybrid nanofluid flow}},
author = {Sakshi Kumari and Rajesh Bhatt},
journal = {Discover Nano},
year = {2026},
month = nov,
doi = {10.1186/s11671-026-05085-5},
url = {https://doi.org/10.1186/s11671-026-05085-5},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - ANN validation and RSM optimization of Cattaneo-Christov heat flux in MHD stenotic artery trihybrid nanofluid flow
AU - Sakshi Kumari
AU - Rajesh Bhatt
PY - 2026
DA - 2026/11/03
JO - Discover Nano
DO - 10.1186/s11671-026-05085-5
UR - https://doi.org/10.1186/s11671-026-05085-5
AB - This study investigates shape-dependent thermal transport in a blood-based Cu–Al₂O₃–Au trihybrid nanofluid using a local throat-level similarity model motivated by stenosed arterial geometry. The model incorporates magnetohydrodynamics, thermal radiation, viscous dissipation, porous-medium resistance, Darcy–Forchheimer drag, and Cattaneo–Christov non-Fourier heat-flux. The key novelty lies in combining mixed-shape nanoparticle modeling with response surface methodology (RSM) optimization and artificial neural network (ANN) surrogate prediction for arterial-flow configurations relevant to thermal therapy. Shape-dependent property correlations compare a fully spherical trihybrid suspension with a mixed-shape case (spherical Cu/Al₂O₃, cylindrical Au). Using similarity transformations, the governing equations are converted into coupled nonlinear ODEs and solved numerically with a collocation-based boundary-value solver. Parametric analysis covers magnetic parameter (0 ≤ M ≤ 3), Eckert number (0 ≤ Ec ≤ 1.5), radiation parameter (0 ≤ Rd ≤ 0.75), Darcy parameter (1 ≤ K ≤ 5), thermal relaxation parameter (0 ≤ αt ≤ 0.1), and nanoparticle volume fraction (0.03 ≤ ϕ ≤ 0.1). RSM with a face-centered central composite design applied over the bounded optimization space for prediction of Nusselt number (Nu), followed by feedforward ANN, identifies the high-M, high-ϕ, and low-Rd region as favorable for local wall heat-transfer enhancement. The solver-confirmed constrained optimum (M = 2.9935, ϕ = 0.0525, and Rd = 0.00012) yields Nu = 12.286, with ANN predicting Nu = 12.1721 (relative deviation: 0.93%). The mixed shape configuration increase mean velocity by 0.56–6.18% and mean temperature by 0.49–12.88% versus spherical particles. Overall, the combined numerical–RSM–ANN framework offers a reproducible route for screening design space and optimizing trihybrid nanofluid heat transfer in magnetically assisted arterial thermal-therapy modeling. Clinical trial number: Not applicable. This study did not involve a clinical trial.
SN - 2731-9229
ER -