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<p>This study investigates the thermophysical behavior and heat transfer characteristics of <i>TiO</i><sub><i>2</i></sub>/ethylene glycol nanofluid between two horizontal coaxial tubes under the influence of thermal radiation and a magnetic field, with a focus on nanoparticle aggregation effects. A mathematical model incorporating aggregation is utilized and solved numerically using MATLAB’s bvp4c function. The analysis compares scenarios with nanoparticles aggregation (<i>WA</i>) and without nanoparticles aggregation (<i>WOA</i>), examining the impact of key dimensionless parameters: Reynolds number (<i>Re</i>), Hartmann number (<i>Ha</i>), nanoparticles volume fraction (<i>ϕ</i>), radiation parameter (<i>Rd</i>), and Eckert number (<i>Ec</i>). Higher <i>Re</i> enhances velocity, while higher values of <i>Ha</i> suppress velocity. Higher values of nanoparticles volume fraction and Eckert number increase the temperature profile. The Nusselt number, reflecting heat transfer, is consistently higher in the flow model without nanoparticles aggregation. Artificial neural network (<i>ANN</i>) and fuzzy particle swarm optimization (<i>FPSO</i>) are employed to predict Nusselt number, and good precision is seen in the predicted values. It is observed that when Hartmann number (<i>Ha</i>) value is equal to <i>5</i>, and Reynolds number (<i>Re</i>) changes from 0.8 to 4.8, the Nusselt number values increase by <i>84.0046%</i> and <i>83.5241%</i> in the case of nanoparticles aggregation and without nanoparticles aggregation model.</p>

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Nanoparticle aggregation impact on nanofluid flow in a rotating horizontal annulus: application of particle swarm optimization and artificial neural network

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Moh Yaseen,
  • Nisha Joshi Bhadhani,
  • Sawan Kumar Rawat,
  • Sumit Pant,
  • Manish Pant

摘要

This study investigates the thermophysical behavior and heat transfer characteristics of TiO2/ethylene glycol nanofluid between two horizontal coaxial tubes under the influence of thermal radiation and a magnetic field, with a focus on nanoparticle aggregation effects. A mathematical model incorporating aggregation is utilized and solved numerically using MATLAB’s bvp4c function. The analysis compares scenarios with nanoparticles aggregation (WA) and without nanoparticles aggregation (WOA), examining the impact of key dimensionless parameters: Reynolds number (Re), Hartmann number (Ha), nanoparticles volume fraction (ϕ), radiation parameter (Rd), and Eckert number (Ec). Higher Re enhances velocity, while higher values of Ha suppress velocity. Higher values of nanoparticles volume fraction and Eckert number increase the temperature profile. The Nusselt number, reflecting heat transfer, is consistently higher in the flow model without nanoparticles aggregation. Artificial neural network (ANN) and fuzzy particle swarm optimization (FPSO) are employed to predict Nusselt number, and good precision is seen in the predicted values. It is observed that when Hartmann number (Ha) value is equal to 5, and Reynolds number (Re) changes from 0.8 to 4.8, the Nusselt number values increase by 84.0046% and 83.5241% in the case of nanoparticles aggregation and without nanoparticles aggregation model.