Inverse problem in determining convection heat transfer coefficient of an annular fin

Chen, Wen-Lih; Yu, Yang; Lee, Haw Long

doi:10.1016/j.enconman.2006.10.016

Cited by 79 publications

(20 citation statements)

References 15 publications

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“…(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11), together with the boundary conditions shown in Fig. 2 are solved using FLUENT 6.3, including the radiation model.…”

Section: Numerical Schemementioning

confidence: 99%

“…Sablani et al [8] adopted non-iterative methods to solve inverse problems mainly associated with conduction. Chen et al [9] used an inverse algorithm to estimate the unknown space and dependent convection heat transfer coefficient of an annular fin. Tahavvor and Mahmoud [10] used artificial neural networks (ANN) to determine natural convection heat transfer and fluid flow around a cooled horizontal circular cylinder having constant surface temperature.…”

Section: Introductionmentioning

confidence: 99%

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Inverse conjugate mixed convection in a vertical substrate with protruding heat sources: a combined experimental and numerical study

Ahamad

Balaji

2015

Heat Mass Transfer

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heat inputs, CFD simulations were again run to compare the temperature distribution at the back of the PCB board with measurements. List of symbolsa Absorption coefficient (m −1 ) A Area of heat source (m 2 ) B Distance from the edge of the plate to the nearest chip edge (m) C p Specific heat (J/kg k) D Spacing between the heat sources (m) d Height of the heat source (m) Gr Grashof number, Gr = gβ∆TL 3 υ 2 g Acceleration due to gravity (9.81 m/s 2 ) H Height of the duct/channel (m) h Convective heat transfer coefficient (W/m 2 K) I Total radiation intensity (W/m 3 ) k s Thermal conductivity of solid (W/m K) L Characteristic length (height of the heat source in this case) (m) N Length of substrate in Z direction (m) n Refractive index p Pressure (N/m 2 ) S Sum of the residues, as the case may be (Eq. 12) s Direction vector Q Power input (W) Q ANN Heat input value measured by ANN (W) Q Expt Heat input value measured by experiments (W) Re Reynolds number, U α L υ Ri Richardson number, Ri = Gr Re 2 S Spacing between the two walls of the channel (m) t Heat source thickness (m) T Temperature (K) U ∝ Inlet air velocity (m/s) V Velocity vector (m/s) Abstract This paper reports the results of a combined numerical and experimental study to estimate the heat inputs of three protruding heat sources of the same size placed on a vertically placed PCB board of height 150 mm, depth 250 mm, and thickness 5 mm. First, limited measurements of temperatures were recorded at eight locations along the height of the back of the PCB board for different (and known) values of heat inputs of the protruding heat sources and different velocities. These were followed by three-dimensional calculations of fluid flow and conjugate heat transfer for various heat transfer coefficients on the backside of the PCB board. The difference between the CFD predicted and experimentally measured temperature distributions on the back of the PCB board was minimized using least squares and the best value of heat transfer coefficient was obtained. Using this 'data assimilated' CFD model, detailed CFD simulations were done for various values of heat input values and Reynolds numbers (each of these can be different from one another) of the flow. The temperatures at the same eight locations at the back of the PCB board were noted. An artificial neural network was then developed with ten inputs (eight temperatures together with the input velocity and the ambient temperature) to estimate the three outputs (three heat inputs) after carrying out extensive studies on the architecture of the network. This inverse solution was then tested with experiments for validating the ANN approach to solve the inverse conjugate heat transfer problem. Finally, with the ANN estimated

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“…(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11), together with the boundary conditions shown in Fig. 2 are solved using FLUENT 6.3, including the radiation model.…”

Section: Numerical Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse conjugate mixed convection in a vertical substrate with protruding heat sources: a combined experimental and numerical study

Ahamad

Balaji

2015

Heat Mass Transfer

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show abstract

“…Huang and Hsiao [14] have used the conjugate gradient method (CGM) to estimate the shape of a fin. Chen et al [15] have estimated the heat transfer coefficient using temperature measurements. They have also used the CGM-based optimization.…”

Section: Introductionmentioning

confidence: 99%

Predicting multiple combination of parameters for designing a porous fin subjected to a given temperature requirement

Das

Ooi

2013

Energy Conversion and Management

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“…The results showed that the various parameters have significant effect on the temperature distribution. Chen et al [9] utilized the conjugate gradient method based on an inverse algorithm to estimate the convection heat transfer coefficient of an annular fin. Results showed that the technique proposed herein can accurately estimate the convection heat transfer coefficient, temperature distributions and thermal stress distribution for all the cases considered in this study.…”

Section: Introductionmentioning

confidence: 99%

Hybrid differential transformation and finite difference method to annular fin with temperature-dependent thermal conductivity

Peng

Chen

2011

International Journal of Heat and Mass Transfer

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Inverse problem in determining convection heat transfer coefficient of an annular fin

Cited by 79 publications

References 15 publications

Inverse conjugate mixed convection in a vertical substrate with protruding heat sources: a combined experimental and numerical study

Inverse conjugate mixed convection in a vertical substrate with protruding heat sources: a combined experimental and numerical study

Predicting multiple combination of parameters for designing a porous fin subjected to a given temperature requirement

Hybrid differential transformation and finite difference method to annular fin with temperature-dependent thermal conductivity

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