Scholarlink

<p>The current study aims to look at the Darcy-Forchheimer and bioconvective flow of Casson blood-based trihybrid nanofluid (THNF) through an exponentially expanding surface. The three different nanoparticles, namely, cobalt ferrite (CoFe<sub>2</sub>O<sub>4</sub>), molybdenum disulfide (MoS<sub>2</sub>), and zirconium dioxide (ZrO<sub>2</sub>) are used to make the THNF. The impacts of viscous dissipation, magnetic field, thermal radiation, Brownian motion, thermophoresis, heat consumption/generation, and thermophoretic particle deposition are also included in this investigation. Using appropriate variables, the set of partial differential equations representing the fluid models are transformed into a system of ordinary differential equations and these equations are numerically solved using the ND solver and the bvp4c approach. Our outcomes are validated through the earlier publication results. Physical traits such as fluid velocity, temperature, nanofluid (NF) concentration, and motile microorganisms are shown graphically. The results show that improving the porosity parameter diminishes the velocity profile. The temperature profile decays when enhancing the value of the Casson parameter. The NF concentration profile suppresses as the thermophoretic particle deposition parameter enhances. The profile of microorganisms declines when enhancing the bioconvective Lewis number. Accelerating the magnetic field parameter makes a reduction in skin friction coefficient. The raise in the radiation parameter improves the heat transmission rate. The larger thermophoresis parameter declines the rate of mass transfer, and the motile microorganisms density diminishes when enlarging the value of the Peclet number. In addition, the long-short term memory model is used to optimize the heat transfer gradient data by training, validating, and testing to determine the data accuracy. The training mean square error (MSE) is 0.001089, 0.000195, 0.000236, and 0.000499, the validation MSE is 0.001665, 0.000647, 0.000629, and 0.000694, and the test MSE is 0.001779, 0.000158, 0.000154, and 0.000269 for Cattaneo-Christov heat and mass flux model (CCHMFM) with suction, CCHMFM with injection, Fourier heat and mass flux model (FHMFM) with suction, and FHMFM with injection, respectively.</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

Scientific computing of thermally radiative Casson blood-based tri-hybrid nanofluid flow past an exponentially expanding surface with gyrotactic microorganisms: A machine learning approach

  • 期刊论文

  • 发表日期：

    2026年1月29日

查看全文
  • Divya Shanmugam,
  • Eswaramoorthi Sheniyappan,
  • Loganathan Karuppusamy,
  • Anand Rajendran,
  • Rifaqat Ali,
  • Koppula Srinivas Rao

摘要

The current study aims to look at the Darcy-Forchheimer and bioconvective flow of Casson blood-based trihybrid nanofluid (THNF) through an exponentially expanding surface. The three different nanoparticles, namely, cobalt ferrite (CoFe2O4), molybdenum disulfide (MoS2), and zirconium dioxide (ZrO2) are used to make the THNF. The impacts of viscous dissipation, magnetic field, thermal radiation, Brownian motion, thermophoresis, heat consumption/generation, and thermophoretic particle deposition are also included in this investigation. Using appropriate variables, the set of partial differential equations representing the fluid models are transformed into a system of ordinary differential equations and these equations are numerically solved using the ND solver and the bvp4c approach. Our outcomes are validated through the earlier publication results. Physical traits such as fluid velocity, temperature, nanofluid (NF) concentration, and motile microorganisms are shown graphically. The results show that improving the porosity parameter diminishes the velocity profile. The temperature profile decays when enhancing the value of the Casson parameter. The NF concentration profile suppresses as the thermophoretic particle deposition parameter enhances. The profile of microorganisms declines when enhancing the bioconvective Lewis number. Accelerating the magnetic field parameter makes a reduction in skin friction coefficient. The raise in the radiation parameter improves the heat transmission rate. The larger thermophoresis parameter declines the rate of mass transfer, and the motile microorganisms density diminishes when enlarging the value of the Peclet number. In addition, the long-short term memory model is used to optimize the heat transfer gradient data by training, validating, and testing to determine the data accuracy. The training mean square error (MSE) is 0.001089, 0.000195, 0.000236, and 0.000499, the validation MSE is 0.001665, 0.000647, 0.000629, and 0.000694, and the test MSE is 0.001779, 0.000158, 0.000154, and 0.000269 for Cattaneo-Christov heat and mass flux model (CCHMFM) with suction, CCHMFM with injection, Fourier heat and mass flux model (FHMFM) with suction, and FHMFM with injection, respectively.