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<p>The current investigation emphasis on the hemodynamics of blood doped with silver Ag nanofluids in stenosed arteries under the influence of uniform magnetic field has been analyzed. A semi experimental model has been developed in COMSOL Multiphysics to simulate blood flow having microorganism and silver nanoparticles under varying non-dimensional parameters like Reynolds number, Hartmann number, and bioconvection Lewis number. Key insights of the investigation exhibited augmented shear stress at the wall of the stenosis for the higher Reynolds number, which may increase the risk of vascular damage. In contrast, elevated impact of Hartmann number stabilizes the blood flow, reduces the shear stress which may reduce the deformation of the arteries. Furthermore, the bioconvection Lewis number enhances the microbial activity, leading to uneven distribution of microorganisms within the fluid domain. The height of the stenosis also plays a pivotal role; greater stenosis height may disrupt the fluid motion and the flow become turbulent due to which the temperature of the fluid may also enhances. The antimicrobial property and higher thermal conductivity of Ag nanoparticles provide potential biomedical benefits. The numerical framework exhibited excellent stability, with Finite Element Method (FEM) residuals consistently converging below 10<sup>−6</sup>, ensuring robust computational accuracy. Comparative validation with benchmark studies further confirmed the reliability of the simulations, strengthening confidence in the obtained hemodynamic predictions. To assess the reliability of the computational results, an Artificial Neural Network (ANN) surrogate model was compared with a finite-difference reference solution. The ANN predictions showed excellent agreement, with all variables reaching very low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values and R<sup>2</sup> consistently above 0.99—including near-perfect accuracy for the pressure field (R<sup>2</sup> = 1.0). These outcomes confirm that the surrogate model effectively reproduces the numerical solution. With these findings, the study provides useful insights for exploring nanofluid-assisted cardiovascular treatments, controlled drug transport, and magnetically influenced bioconvective phenomena.</p>

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Magneto-responsive hemodynamic behaviour of nanofluids in biomedical transport: a multiphysics design approach and artificial neural network predictions

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Sahin Ahmed,
  • S. R. Mishra

摘要

The current investigation emphasis on the hemodynamics of blood doped with silver Ag nanofluids in stenosed arteries under the influence of uniform magnetic field has been analyzed. A semi experimental model has been developed in COMSOL Multiphysics to simulate blood flow having microorganism and silver nanoparticles under varying non-dimensional parameters like Reynolds number, Hartmann number, and bioconvection Lewis number. Key insights of the investigation exhibited augmented shear stress at the wall of the stenosis for the higher Reynolds number, which may increase the risk of vascular damage. In contrast, elevated impact of Hartmann number stabilizes the blood flow, reduces the shear stress which may reduce the deformation of the arteries. Furthermore, the bioconvection Lewis number enhances the microbial activity, leading to uneven distribution of microorganisms within the fluid domain. The height of the stenosis also plays a pivotal role; greater stenosis height may disrupt the fluid motion and the flow become turbulent due to which the temperature of the fluid may also enhances. The antimicrobial property and higher thermal conductivity of Ag nanoparticles provide potential biomedical benefits. The numerical framework exhibited excellent stability, with Finite Element Method (FEM) residuals consistently converging below 10−6, ensuring robust computational accuracy. Comparative validation with benchmark studies further confirmed the reliability of the simulations, strengthening confidence in the obtained hemodynamic predictions. To assess the reliability of the computational results, an Artificial Neural Network (ANN) surrogate model was compared with a finite-difference reference solution. The ANN predictions showed excellent agreement, with all variables reaching very low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values and R2 consistently above 0.99—including near-perfect accuracy for the pressure field (R2 = 1.0). These outcomes confirm that the surrogate model effectively reproduces the numerical solution. With these findings, the study provides useful insights for exploring nanofluid-assisted cardiovascular treatments, controlled drug transport, and magnetically influenced bioconvective phenomena.