Two-dimensional boundary-layer flow and heat transfer over a wedge: numerical and asymptotic solutions

Kudenatti, Ramesh B.; Jyothi, B.

doi:10.1016/j.tsep.2019.03.006

Cited by 16 publications

(2 citation statements)

References 25 publications

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“…The iterative process will conclude once the final boundary conditions

f^{\prime} (\infty )=1

and

\theta (\infty )=0

are met. This particular method has been comprehensively documented in the work carried out by Kudenatti et al 32–34 The parameters of physical interest, such as

f^{\prime\prime} (0)

and

{\theta }^{^{\prime} }(0)

(the skin friction coefficient and heat transfer rate), are determined using this approach and subsequently compared with values obtained through numerical simulations of FSE‐HT. To assess the efficacy of the current method, graphical representations of velocity and temperature profiles are presented, accompanied by tabulated data for the skin friction coefficient and the heat transfer rate.…”

Section: Exact and Analytical Solutionsmentioning

confidence: 99%

Exact and analytical solutions for self‐similar thermal boundary layer flows over a moving wedge

Kirsur,

Joshi

2024

Heat Trans

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This article aims to achieve exact and analytical solutions for the classical Falkner–Skan equation with heat transfer (FSE‐HT). Specifically, when the pressure gradient parameter , there already exists a closed‐form solution in the literature for the Falkner–Skan flow equation. The main purpose here is to extend this case to obtain a closed‐form solution to the heat transport equation with the solubility condition . An algorithm is presented and is found to be new to the literature that enriches the physical properties of FSE‐HT. It is shown that for the moving wedge parameter , the momentum and temperature equations show multiple solutions analytically. The skin friction coefficient and the heat transfer rate are also obtained in analytical form. The thus‐obtained solution is then adapted to derive an analytical solution applicable to a wide range of pressure gradient parameters and Prandtl numbers . Furthermore, an asymptotic analysis is conducted, focusing on scenarios where the moving wedge parameter becomes significantly large (). Nevertheless, in all the above‐mentioned cases, the skin friction coefficient () and the heat transfer rate () are compared with the direct numerical solutions of the boundary layer equations, and it is found that the results are in good agreement. These solutions provide a benchmark and shed light on further studies on the families of FSE‐HT.

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“…The iterative process will conclude once the final boundary conditions

f^{\prime} (\infty )=1

and

\theta (\infty )=0

are met. This particular method has been comprehensively documented in the work carried out by Kudenatti et al 32–34 The parameters of physical interest, such as

f^{\prime\prime} (0)

and

{\theta }^{^{\prime} }(0)

Section: Exact and Analytical Solutionsmentioning

confidence: 99%

Exact and analytical solutions for self‐similar thermal boundary layer flows over a moving wedge

Kirsur,

Joshi

2024

Heat Trans

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“…Citation: Tsou et al [18]. The most intriguing nanofluidic models can be found in [19][20][21], while some recent computational fluid dynamics applications addressing boundary layer flow can be seen in [22][23][24]. The role of the heat source and sink is critical in the boundary layer flow problem because cooling is required.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Accurate Approach to Electrical Boundary Layer Nanofluid Flow Simulation: A Use of Artificial Intelligence

Baazeem,

Arif,

Abodayeh

2023

Processes

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Engineering and technological research groups are becoming interested in neural network techniques to improve productivity, business strategies, and societal development. In this paper, an explicit numerical scheme is given for both linear and nonlinear differential equations. The scheme is correct to second order. Additionally, the scheme’s consistency and stability are guaranteed. Backpropagation of Levenberg–Marquardt, the effect of including an induced magnetic field in a mathematical model for electrical boundary layer nanofluid flow on a flat plate, is quantitatively investigated using artificial neural networks. Later, the model is reduced into a set of boundary value problems, which are then resolved using the suggested scheme and a shooting strategy. The outcomes are also contrasted with earlier studies and the MATLAB solver bvp4c for validation purposes. In addition, neural networking is also employed for mapping input to outputs for velocity, temperature, and concentration profiles. These results prove that artificial neural networks can make precise forecasts and optimizations. Using a neural network to optimize the fluid flow in an electrical boundary layer while subjected to an induced magnetic field is a promising application of the suggested computational scheme. Fluid dynamics benefits greatly from combining numerical methods and artificial neural networks, which could lead to new developments in various fields. Results from this study may aid in optimizing fluid systems, leading to greater productivity and effectiveness in numerous technical fields.

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MHD heat transfer flow over a moving wedge with convective boundary conditions with the influence of viscous dissipation and internal heat generation/absorption

2022

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In this investigation, the problem of the study is the effect of the magnetic field and viscous dissipation on heat transfer flow through a moving wedge in the existence of the internal heat generation/absorption and also suction/injection. The governing equations are changed to some coupled nonlinear differential equations with aid of similarity variables. The numerical calculations of the equations are solved by the MATLAB package solver bvp5c. The changes of the pertinent constraints on the momentum and temperature have been discussed through graphs and numerical values of skin friction and heat transfer factor are listed in the tabular pattern. Although maintaining a constant value for the convection parameter, the Nusselt number is increased for Q > 0 and decreased for Q < 0. The temperature rises in conjunction with an increase in Ec and Nc variables.

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Two-dimensional boundary-layer flow and heat transfer over a wedge: numerical and asymptotic solutions

Cited by 16 publications

References 25 publications

Exact and analytical solutions for self‐similar thermal boundary layer flows over a moving wedge

Exact and analytical solutions for self‐similar thermal boundary layer flows over a moving wedge

An Efficient and Accurate Approach to Electrical Boundary Layer Nanofluid Flow Simulation: A Use of Artificial Intelligence

MHD heat transfer flow over a moving wedge with convective boundary conditions with the influence of viscous dissipation and internal heat generation/absorption

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