Effects of thermal radiation on MHD bioconvection flow of non-Newtonian fluids using linear regression based machine learning and artificial neural networks DOI
Sadiq M. Sait, R. Ellahi, Norlin Khalid

et al.

International Journal of Numerical Methods for Heat &amp Fluid Flow, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Purpose This paper aims to investigate the effects of thermal radiation on magnetohydrodynamics (MHD) bioconvection nonlinear complex structure flow non-Newtonian fluids such as Casson, Williamson and Sisko fluids. Design/methodology/approach The coupled fundamental equations governing steady, incompressible combined with Casson–Williamson–Sisko over an exponential sheet are reduced ordinary differential using appropriate transformations. Open-source platforms Google Colab Python used. Results, performance, accuracy correlation examined neural networking, Levenberg-Marquardt, machine learning, artificial intelligence (AI) algorithms linear regression. Findings Numerical graphical results presented observe impact physical parameters. prospect AI tools, particularly increases developed fluid dynamics models. Besides, further scope learning in hybrid nature is also presented. It concluded that Levenberg-Marquardt algorithm most suitable for simulation boundary layer high accuracy, smooth regression curves minimum rate error. observed range 10 −8 mean squared error shows good fit model. noted by increasing Casson fluids’ parameters, velocity profile decreases. Both concentration motile density decrease values Schmidt Peclet numbers. Originality/value existing literature lacks a comparative analysis networks predicting AI-based approaches, MHD literature. effort devoted fill said gap.

Language: Английский

Gyrotactic microorganisms in the pharmacokinetics of electrically conducted nanofluid over a stretching sheet: complex algorithmic models DOI
Adnan Amjad,

Rahma Sellami,

Muhammad Imran Khan

et al.

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(6)

Published: April 28, 2025

Language: Английский

Citations

0
Effects of thermal radiation on MHD bioconvection flow of non-Newtonian fluids using linear regression based machine learning and artificial neural networks DOI
Sadiq M. Sait, R. Ellahi, Norlin Khalid

et al.

International Journal of Numerical Methods for Heat &amp Fluid Flow, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Purpose This paper aims to investigate the effects of thermal radiation on magnetohydrodynamics (MHD) bioconvection nonlinear complex structure flow non-Newtonian fluids such as Casson, Williamson and Sisko fluids. Design/methodology/approach The coupled fundamental equations governing steady, incompressible combined with Casson–Williamson–Sisko over an exponential sheet are reduced ordinary differential using appropriate transformations. Open-source platforms Google Colab Python used. Results, performance, accuracy correlation examined neural networking, Levenberg-Marquardt, machine learning, artificial intelligence (AI) algorithms linear regression. Findings Numerical graphical results presented observe impact physical parameters. prospect AI tools, particularly increases developed fluid dynamics models. Besides, further scope learning in hybrid nature is also presented. It concluded that Levenberg-Marquardt algorithm most suitable for simulation boundary layer high accuracy, smooth regression curves minimum rate error. observed range 10 −8 mean squared error shows good fit model. noted by increasing Casson fluids’ parameters, velocity profile decreases. Both concentration motile density decrease values Schmidt Peclet numbers. Originality/value existing literature lacks a comparative analysis networks predicting AI-based approaches, MHD literature. effort devoted fill said gap.

Language: Английский

Citations

0