Linear and quadratic multiple regressions analysis on magneto-thermal and chemical reactions on the Casson-Williamson nanofluids boundary layer flow under Soret-Dufour mechanism

Falodun, B. O.; Ige, Ebenezer Olubunmi

doi:10.1080/25765299.2022.2115688

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…The concept of the statistical analysis utilized in this section is described as follows (see Falodun and Ige 51 ).…”

Section: Methodsmentioning

confidence: 99%

“…The quadratic equation is defined as (see Falodun & Ige 51 ):

Y=C+{Bx}+A{x}^{2},

where A , B , and C are estimated by

B=\frac{{S}_{xx}{S}_{{x}^{2}{x}^{2}}-{S}_{{x}^{2}y}{S}_{x{x}^{2}}}{{S}_{xx}{S}_{{x}^{2}{x}^{2}}-{({S}_{x{x}^{2}})}^{2}},&#8199;A=\frac{{S}_{{x}^{2}y}{S}_{xx}-{S}_{{xy}}{S}_{{x}^{2}x}}{{S}_{xx}{S}_{{x}^{2}{x}^{2}}-{({S}_{x{x}^{2}})}^{2}},&#8199;\mathrm{and}&#8199;C=\bar{y}-B\bar{x}-C{\bar{x}}^{2}.

…”

Section: Methodsmentioning

confidence: 99%

“…The recent study of Falodun et al 50 gives a detailed explanation of double‐diffusive MHD viscous fluid flow in a porous medium in the presence of Cattaneo–Christov theories. In another development, Falodun and Ige 51 studied Linear and quadratic multiple regression analysis on magnetothermal and chemical reactions on the Casson–Williamson nanofluids boundary layer flow under the Soret–Dufour mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Numerical simulation of heat and mass transfers radiative–convective fluid flow on a stretching porous surface with temperature‐dependent thermophysical properties

2022

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This paper is focused on the analysis of heat and mass transfer radiative–convective fluid flow using quadratic multiple regression and numerical approach. The physical phenomenon is analyzed by utilizing partial differential equations (PDEs). Thermophysical properties, such as viscosity, thermal conductivity, and mass diffusivity, are varied and temperature‐dependent. This study is unique because of its applications in magnetohydrodynamic power accelerators, drilling operators, and bioengineering. The governing PDEs are transformed into coupled nonlinear ordinary differential equations (ODEs). The transformed ODEs are solved numerically using the spectral homotopy analysis method. Also, a quadratic multiple regression analysis is performed on quantities of engineering interest to show the significance of the flow parameters. It is observed that the heat and mass transfer process is affected by nonlinear buoyancy impact. The Lorentz force produced by the imposed magnetic field decline the thickness of the hydrodynamic boundary layer. Findings revealed that the nonlinear convective parameter and variable thermophysical properties are greatly affected by the rate of heat and mass transfer. Previously published work was used to validate the present one, which conformed with it. The slope of linear regression through data points is adopted to show the rate of change in skin friction, Nusselt, and Sherwood numbers during the flow phenomenon.

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“…The concept of the statistical analysis utilized in this section is described as follows (see Falodun and Ige 51 ).…”

Section: Methodsmentioning

confidence: 99%

“…The quadratic equation is defined as (see Falodun & Ige 51 ):

Y=C+{Bx}+A{x}^{2},

where A , B , and C are estimated by

B=\frac{{S}_{xx}{S}_{{x}^{2}{x}^{2}}-{S}_{{x}^{2}y}{S}_{x{x}^{2}}}{{S}_{xx}{S}_{{x}^{2}{x}^{2}}-{({S}_{x{x}^{2}})}^{2}},&#8199;A=\frac{{S}_{{x}^{2}y}{S}_{xx}-{S}_{{xy}}{S}_{{x}^{2}x}}{{S}_{xx}{S}_{{x}^{2}{x}^{2}}-{({S}_{x{x}^{2}})}^{2}},&#8199;\mathrm{and}&#8199;C=\bar{y}-B\bar{x}-C{\bar{x}}^{2}.

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Numerical simulation of heat and mass transfers radiative–convective fluid flow on a stretching porous surface with temperature‐dependent thermophysical properties

2022

Self Cite

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“…Regression has multiple applications in almost every field. Linear and quadratic multiple regression analysis to find the behavior of certain reactions in the chemistry field was carried out by Falodun et al [22]. Dorugade [23] introduced new ridge parameters for ridge regression.…”

Section: Logistic Regressionmentioning

confidence: 99%

Estimation of Hidden Logits Using Several Randomized Response Techniques

Halim,

Arshad,

Alomair

et al. 2023

Symmetry

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In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues.

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“…Non-Newtonian Casson fluids find implementation in food preservation. For the last few decades concept of Casson fluid motion over an extended plane has been active due to its implementations in manufacturing and the commercial field [2]. Ullah et al [3] worked on the unsteady MHD slip flow of a Casson fluid.…”

Section: Introductionmentioning

confidence: 99%

Intelligent computing technique to study heat and mass transport of Casson nanofluidic flow model on a nonlinear slanted extending sheet

Hussain,

Islam,

Raja

et al. 2023

Z Angew Math Mech

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This paper explains the importance and benefits of using AI in fluid mechanics, emphasizing its capability of finding the solution of fluid flow systems through iterative optimization techniques. It explores how AI can enhance the understanding of fluid dynamics to improve engineering processes. In the presented studies, the heat and mass transport of the Casson nanofluidic flow model on a nonlinear slanted extending sheet (HMT‐CNFM) via AI‐based Levenberg Marquard methodology with backpropagated trained neural networks (TNN‐BLMM) is studied. By applying an appropriate transformation, the governing PDEs representing HMT‐CNFM are converted into a system of nonlinear ODEs. The dataset for Levenberg Marquard methodology with backpropagated trained neural network (TNN‐BLMM) for all six scenarios of this proposed model via computational power of the Lobatto IIIA scheme using the “bvp4c” package in MATLAB and then graphically illustrate all these six scenarios through nftool to attain mean square error, regression, error histogram, performance, and fit curve. Training, testing, and validation processes of NN‐BLMM are organized for the investigation of the HMT‐CNFM model.

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Linear and quadratic multiple regressions analysis on magneto-thermal and chemical reactions on the Casson-Williamson nanofluids boundary layer flow under Soret-Dufour mechanism

Cited by 9 publications

References 34 publications

Numerical simulation of heat and mass transfers radiative–convective fluid flow on a stretching porous surface with temperature‐dependent thermophysical properties

Numerical simulation of heat and mass transfers radiative–convective fluid flow on a stretching porous surface with temperature‐dependent thermophysical properties

Estimation of Hidden Logits Using Several Randomized Response Techniques

Intelligent computing technique to study heat and mass transport of Casson nanofluidic flow model on a nonlinear slanted extending sheet

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