Bio-Convection Effects of MHD Williamson Fluid Flow over a Symmetrically Stretching Sheet: Machine Learning

Abstract

The primary goal of this research study is to examine the influence of Brownian motion and thermophoresis diffusion with the impact of thermal radiation and the bioconvection of microorganisms in a symmetrically stretching sheet of non-Newtonian typical Williamson fluid. Structures of the momentum, energy, concentration, and bio-convection equations are interconnected with the imperative partial differential equations (PDEs). Similarity transformations are implemented to translate pertinent complicated partial differential equations into ordinary differential equations (ODEs). The BVP4C approach from the MATLAB assemblage computational methods scheme is extensively impacted by the results of these ODEs. The impact of several physical parameters, including Williamson fluid We(0.2≤We≤1.2), the magnetic field parameter M(0.0≤M≤2.5), Brownian motion Nb(0.0≤Nb≤1.0), thermophoresis diffusion Nt(0.1≤Nt≤0.9). In addition, various physical quantities of the skin friction (RexCfx), Nusselt number (Nux), Sherwood number (Shx), and motile microorganisms (Nnx) are occupied and demonstrate the visualization of graphs and tabular values. These outcomes are validated with earlier obtained results, displaying excellent synchronicity in the physical parameters. Furthermore, the physical quantities concerning the non-dimensional parameters are anticipated by employing Multiple Linear Regression (MLR) in Machine Learning (ML) as successfully executed a novelty of this study. These innovative techniques can help to advance development and technologies for future researchers. The real-world implications of this research are that bio-remediation, microbial movements in mixed fluids, and cancer prevention therapy are crucial.

Machine learning impact of radiative blood flow over a wedge in a time-dependent MHD Williamson fluid

“…Many different methods exist for regression and classification. After examining the prevalence of ML models in that area, the multiple linear regression technique was used in this work to anticipate the physical properties of fluid flow ([42] - [45]). In this regard, the current paper examined three distinct data points obtained from the numerical results for 19 observations from skin friction, 13 data from the Nusselt number, and 11 from the Sherwood number.…”