Scholarlink

<p>Cardiovascular disorders require therapeutic interventions beyond the limitations imposed by traditional drug delivery. Motivated by the need to understand heat and momentum transport in diseased arterial environments, the present study investigates the flow and thermal characteristics of a Casson-Maxwell ternary nanofluid over a Local Wall-Bounded Stretching Surface (LWBSS) representing a stenotic arterial segment. The analysis incorporates magnetic field effects, thermal radiation, and heat generation/absorption, while the governing equations are solved numerically using the MATLAB bvp4c solver. The study reveals that the Casson-Maxwell model produces a lower velocity field than the Casson fluid model, indicating enhanced flow resistance due to viscoelastic relaxation effects. An increase in the Maxwell parameter significantly suppresses fluid motion, whereas thermal radiation enhance the temperature distribution within the thermal boundary layer. Among the nanoparticles considered, copper and aluminium oxide nanoparticles increase the heat transfer rate, whereas silver nanoparticles reduce it. The skin-friction coefficient decreases by approximately 213.8% with a unit increase in the Maxwell parameter and increases by 28.25% with a unit increase in the Casson parameter, highlighting the dominant influence of fluid elasticity on wall drag. Furthermore, a Levenberg-Marquardt backpropagation artificial neural network is developed to predict the heat transfer rate, achieving a near unit accuracy. Response Surface Methodology-based sensitivity analysis identifies the Maxwell parameter as the most influential factor governing skin-friction behavior. The findings provide useful insights into the thermal and rheological behavior of complex bio-nanofluids and demonstrate the effectiveness of data-driven approaches for transport prediction.</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

Neural network-based heat transfer forecasting and skin friction sensitivity analysis of Casson-Maxwell ternary nanofluid model in a stenotic arterial segment

  • 期刊论文

  • 发表日期：

    2026年8月5日

查看全文
  • S. P. Shivakumar,
  • Gunisetty Ramasekhar,
  • P. Nimmy,
  • Sujesh Areekara,
  • L. Thanuja,
  • T. V. Smitha,
  • S. Devanathan,
  • Ganesh R. Naik,
  • K. V. Nagaraja

摘要

Cardiovascular disorders require therapeutic interventions beyond the limitations imposed by traditional drug delivery. Motivated by the need to understand heat and momentum transport in diseased arterial environments, the present study investigates the flow and thermal characteristics of a Casson-Maxwell ternary nanofluid over a Local Wall-Bounded Stretching Surface (LWBSS) representing a stenotic arterial segment. The analysis incorporates magnetic field effects, thermal radiation, and heat generation/absorption, while the governing equations are solved numerically using the MATLAB bvp4c solver. The study reveals that the Casson-Maxwell model produces a lower velocity field than the Casson fluid model, indicating enhanced flow resistance due to viscoelastic relaxation effects. An increase in the Maxwell parameter significantly suppresses fluid motion, whereas thermal radiation enhance the temperature distribution within the thermal boundary layer. Among the nanoparticles considered, copper and aluminium oxide nanoparticles increase the heat transfer rate, whereas silver nanoparticles reduce it. The skin-friction coefficient decreases by approximately 213.8% with a unit increase in the Maxwell parameter and increases by 28.25% with a unit increase in the Casson parameter, highlighting the dominant influence of fluid elasticity on wall drag. Furthermore, a Levenberg-Marquardt backpropagation artificial neural network is developed to predict the heat transfer rate, achieving a near unit accuracy. Response Surface Methodology-based sensitivity analysis identifies the Maxwell parameter as the most influential factor governing skin-friction behavior. The findings provide useful insights into the thermal and rheological behavior of complex bio-nanofluids and demonstrate the effectiveness of data-driven approaches for transport prediction.