Scholarlink

<p>The hybrid nanofluids (HNFs) have superior thermal impact and stability than traditional nanofluids, making them more suitable for peak thermal results in thermal systems and solar energy. Furthermore, artificial neural network (ANN) improves the accuracy and thermal aspects of HNF with optimised results. This analysis presents optimised thermal results associated with the flow of HNF due to the permeable porous surface with mass suction effects. Hybrid nanofluid properties are accounted for by using the suspension of gold (Au) and silver (Ag) nanoparticles with blood as the base liquid. The nonlinear applications of radiated phenomenon have been considered. The modification in heat and concentration equations are done by following the Cattaneo–Christov model. The concentration of HNF is observed using chemical reaction. Artificial neural network (ANN) simulations are performed to validate and optimise thermal results. Convective heat constraints are implemented to observe the thermal simulations. Computations are numerically simulated with the help of shooting scheme. The results for mono-nanofluid (MNF) and HNF are compared. It is observed that the suspension of HNF is more suitable for enhancing heat transfer systems. Temperature profile increases using suction phenomenon and porous media.</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

Machine learning-based thermal performances of hybrid nanofluid (Au–Ag/blood) with nonlinear radiative features for stretched surface flow

  • 期刊论文

  • 发表日期：

    2025年12月9日

查看全文
  • Sami Ullah Khan,
  • Nermeen Abdullah,
  • Manzoor Ishaq,
  • Muhammad Amir,
  • Eashwar Dan Charan,
  • Kaouther Ghachem,
  • Lioua Kolsi

摘要

The hybrid nanofluids (HNFs) have superior thermal impact and stability than traditional nanofluids, making them more suitable for peak thermal results in thermal systems and solar energy. Furthermore, artificial neural network (ANN) improves the accuracy and thermal aspects of HNF with optimised results. This analysis presents optimised thermal results associated with the flow of HNF due to the permeable porous surface with mass suction effects. Hybrid nanofluid properties are accounted for by using the suspension of gold (Au) and silver (Ag) nanoparticles with blood as the base liquid. The nonlinear applications of radiated phenomenon have been considered. The modification in heat and concentration equations are done by following the Cattaneo–Christov model. The concentration of HNF is observed using chemical reaction. Artificial neural network (ANN) simulations are performed to validate and optimise thermal results. Convective heat constraints are implemented to observe the thermal simulations. Computations are numerically simulated with the help of shooting scheme. The results for mono-nanofluid (MNF) and HNF are compared. It is observed that the suspension of HNF is more suitable for enhancing heat transfer systems. Temperature profile increases using suction phenomenon and porous media.