Scholarlink

<p>This study investigates the entropy generation and heat transfer characteristics of Williamson hybrid nanofluid flow over a vertical cone embedded in a Darcy porous medium with injection effects. The mathematical model incorporates non-Newtonian fluid behavior, hybrid nanoparticles, and bioconvection by gyrotactic microorganisms. Using similarity transformations, the governing partial differential equations were transformed into nonlinear ordinary differential equations and solved numerically using the MATLAB bvp4c solver. Response surface methodology (RSM) and artificial neural network (ANN) techniques are employed to analyze, predict, and optimize the Nusselt number. The results indicate that heat generation and variable viscosity enhance thermal transport, whereas the magnetic field intensity and permeability reduce the fluid velocity. An increase in the nanoparticle concentration improved the heat transfer and microorganism distribution. Sensitivity analysis was used to identify the most influential parameters affecting the Nusselt number. The present analysis provides useful insights into entropy generation and heat transfer optimization in Williamson hybrid nanofluid flow systems, with potential thermal engineering applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Entropy generation analysis with ANN and RSM based optimization of heat transfer in Williamson hybrid nanofluid flow over a cone

  • 期刊论文

  • 发表日期：

    2026年7月14日

查看全文
  • Kavin Rajendran,
  • P. Bala Anki Reddy

摘要

This study investigates the entropy generation and heat transfer characteristics of Williamson hybrid nanofluid flow over a vertical cone embedded in a Darcy porous medium with injection effects. The mathematical model incorporates non-Newtonian fluid behavior, hybrid nanoparticles, and bioconvection by gyrotactic microorganisms. Using similarity transformations, the governing partial differential equations were transformed into nonlinear ordinary differential equations and solved numerically using the MATLAB bvp4c solver. Response surface methodology (RSM) and artificial neural network (ANN) techniques are employed to analyze, predict, and optimize the Nusselt number. The results indicate that heat generation and variable viscosity enhance thermal transport, whereas the magnetic field intensity and permeability reduce the fluid velocity. An increase in the nanoparticle concentration improved the heat transfer and microorganism distribution. Sensitivity analysis was used to identify the most influential parameters affecting the Nusselt number. The present analysis provides useful insights into entropy generation and heat transfer optimization in Williamson hybrid nanofluid flow systems, with potential thermal engineering applications.