Predicting geometry of rectangular and hyperbolic fin profiles with temperature-dependent thermal properties using decomposition and evolutionary methods

Bhowmik, Arka; Singla, Rohit; Roy, Pranab; Prasad, Dilip K.; Das, Ranjan; Repaka, Ramjee

doi:10.1016/j.enconman.2013.07.025

Cited by 40 publications

(29 citation statements)

References 26 publications

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“…Ghasemi et al [9] applied Differential Transform Method (DTM) for solving the nonlinear temperature distribution equation in a longitudinal fin with temperature dependent internal heat generation and thermal conductivity. Bhowmik et al [10] applied both decomposition and differential evolution method for predicting dimensions of rectangular and hyperbolic fins with variable thermal properties. Hatami et al [11] presented the temperature distribution for a fully wet semi-spherical porous fin using Least Square Method (LSM) and fourth-order Runge-Kutta Method.…”

Section: Introductionmentioning

confidence: 99%

A decomposition method for convective–radiative fin with heat generation

Roy

Mondal²,

Mallick

2015

Ain Shams Engineering Journal

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This work is aimed at studying the effect of environmental temperature such as radiation sink temperature, convection sink temperature and heat generation number on the temperature distribution and efficiency of a convective-radiative stationary fin. The Adomian Decomposition Method (ADM) being one of the efficient numerical methods for highly non linear equations, the local temperature field and efficiencies are obtained using ADM in which Newton-Rapson method is used to estimate the fin temperature for insulated boundary conditions. It is found that the present ADM results are good agreement with the results available in literature using Galerkin Method (GM) and Boundary Value problem Method (BVP). Ó 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

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Section: Introductionmentioning

confidence: 99%

A decomposition method for convective–radiative fin with heat generation

Roy

Mondal²,

Mallick

2015

Ain Shams Engineering Journal

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“…Many engineering problems contain objective functions that are highly non-linear, non-differentiable, non-continuous and also there are multiÀdimensional problems having many local minima, constraints or stochasticity. Such problems which are otherwise difficult to optimize/solve can be optimized/solved using evolutionary algorithms such as DE [20]. Initialization, mutation, recombination (crossover) and selection are the four basic operations associated with such algorithm.…”

Section: Differential Evolution (De) Based Inverse Methodsmentioning

confidence: 99%

“…. ; V D i;G g corresponding to each target vector X i,G is generated using the following equation [20,41,44,45].…”

Section: Mutationmentioning

confidence: 99%

“…Inverse analysis is however computationally expensive and mathematically more complicated compared to its forward counterpart due to ill posed and iterative nature of the problem [14][15][16] and many a times multiple combinations of parameters are obtained that satisfy the same given reference output. This is one of the unique features of inverse analysis due to which it has become very popular and successfully applied in many engineering problems in the last decade [17][18][19][20][21][22][23][24][25][26][27][28]. Various methods such as conjugate gradient method, steepest descent method, linear least-squares error method, golden section technique, genetic algorithm and DE are used in the inverse analysis.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of operating parameters of a reheat regenerative power cycle using simplex search and differential evolution based inverse methods

Gogoi

Pandey

Das

2015

Energy Conversion and Management

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“…Several analytical and semi-analytical methods have been proposed to solve the heat conduction problem through inhomogeneous fins including power series (Díez et al, 2009), the Adomian decomposition (Bhowmik et al, 2013); the homotopy (Moitsheki et al, 2015), the variation iterative and the differential transform methods (Joneidi et al, 2009). However, the differential transformation method (DTM) is well-known as an approximate analytical solution which provides more accurate results compared to other techniques such as the Adomian decomposition method and the variation iterative method (Salehi et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Thermo-Geometric Parameter Effects on Convectively Cooled Inhomogeneous Rectangular Fin

Kamdem¹,

Lemoubou²,

Bogning³

2017

Frontiers in Heat and Mass Transfer

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Numerical experiments involving heat transfer were performed to analyze the influence of both fin thermo-geometric parameter and cooling boundary conditions on the temperature distribution and the efficiency of convective cooled inhomogeneous rectangular fin. The inhomogeneity of the fin is due to both temperature dependent thermal conductivity and convection heat coefficients. The analysis was facilitated by the use of the differential transformation method, which can solve nonlinear differential equation. A specific application is first made for temperature/efficiency homogeneous fin predictions and the results are in excellent agreement with standard exact results. Predictions of inhomogeneous fin temperature and efficiency for three different convection conditions are then performed. The thermo-geometric parameter range for numerical experiments was from 0.0 to 1.0. The results reveal that the thermo-geometric parameter plays a great role in the amount of fin temperature distribution and efficiency. The temperature distribution within the fin is low for high thermo-geometric parameter value and increases rapidly for large values of this parameter. For geometric parameter greater than 0.3, the use of rectangular fin will be optimal for heated or cooled process having heat transfer coefficient increased with increased temperature as generally accounted in laminar or turbulent natural convection. On the other hand, the efficiency of the inhomogeneous fin does not depend on cooling conditions when the thermo-geometric parameter is less than 0.3.

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Predicting geometry of rectangular and hyperbolic fin profiles with temperature-dependent thermal properties using decomposition and evolutionary methods

Cited by 40 publications

References 26 publications

A decomposition method for convective–radiative fin with heat generation

A decomposition method for convective–radiative fin with heat generation

Estimation of operating parameters of a reheat regenerative power cycle using simplex search and differential evolution based inverse methods

Thermo-Geometric Parameter Effects on Convectively Cooled Inhomogeneous Rectangular Fin

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