Inverse boundary design of square enclosures with natural convection

Payan, S.; Sarvari, S. M. Hosseini; Ajam, Hossein

doi:10.1016/j.ijthermalsci.2008.05.019

Cited by 28 publications

(4 citation statements)

References 25 publications

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“…Investigators [5e8] have used adjoint and conjugate gradient methods for inverse convection analysis. Arbitrary heat flux from measured temperatures without a priori information for natural and forced convection in an enclosure was predicted by Payan et al [8]. In this study, the estimation of heater strength was carried out as an inverse analysis from knowledge of the temperature.…”

Section: Introductionmentioning

confidence: 97%

ANN based estimation of heat generation from multiple protruding heat sources on a vertical plate under conjugate mixed convection

Kumar

Balaji

2011

International Journal of Thermal Sciences

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Section: Introductionmentioning

confidence: 97%

ANN based estimation of heat generation from multiple protruding heat sources on a vertical plate under conjugate mixed convection

Kumar

Balaji

2011

International Journal of Thermal Sciences

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“…These kinds of problems are known as inverse problems. The inverse problems may be classified into two categories of "design" [1][2][3][4][5][6][7][8][9][10][11][12][13] and "identification" [14][15][16][17][18][19][20][21][22] problems. The inverse problems can be solved by the regularization (explicit) and optimization (implicit) methods.…”

Section: Introductionmentioning

confidence: 99%

“…The inverse problems can be solved by the regularization (explicit) and optimization (implicit) methods. 11 Optimization techniques can be classified as gradient-based methods 4,8,10,23 and heuristic or gradient-free methods. 19,20,[24][25][26][27] In the gradientbased methods, the local topography of the objective function is used to find a path toward the minimum point of the objective function, that is, by using the first and sometimes the second derivative of the objective function.…”

Section: Introductionmentioning

confidence: 99%

Nongray inverse boundary design problems in enclosures at radiative equilibrium

Amiri

Lari

2019

Heat Trans. Asian Res.

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In this paper, an inverse analysis is used to find an appropriate heat flux distribution over the heater surface of radiant enclosures, filled with nongray media at radiative equilibrium from the knowledge of desired (prespecified) temperature and heat flux distributions over the given design surface. Regular and irregular 2D enclosures filled with nongray combustive gas products are considered. Radiation is considered the dominant mode of heat transfer and the medium temperature is obtained from the energy equation. To evaluate the nongray behavior of the participating gases properly, the spectral‐line weighted‐sum‐of‐gray‐gases (SLW) model with updated correlations is used. The dependence of absorption coefficients and the weights of the SLW model on the temperature of the medium makes the inverse problem nonlinear and difficult to handle. Here, the inverse problem is formulated as an optimization problem and the Levenberg‐Marquardt method has been used to solve it. The finite volume method is exploited for the discretization of the energy equation and the spatial discretization of the radiative transfer equation (RTE). The discrete ordinates method (TN quadrature) is used for the angular discretization of RTE. Five test cases, including homogeneous and inhomogeneous media, are investigated to prove the ability of the present methodology for achieving the desired conditions.

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“…They found that at high Reynolds number the accuracy of the inverse method strongly depends on the relative position between the estimated and measured quantities. Payan et al [14] used an optimization technique in the inverse boundary design of square enclosures with natural convection. The conjugate gradient method was used to optimize the objective function.…”

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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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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Inverse boundary design of square enclosures with natural convection

Cited by 28 publications

References 25 publications

ANN based estimation of heat generation from multiple protruding heat sources on a vertical plate under conjugate mixed convection

ANN based estimation of heat generation from multiple protruding heat sources on a vertical plate under conjugate mixed convection

Nongray inverse boundary design problems in enclosures at radiative equilibrium

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

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