Estimation of heat transfer in oscillating annular flow using artifical neural networks

Akdag, Unal; Komur, M. Aydin; Özgüç, A.

doi:10.1016/j.advengsoft.2009.01.010

Cited by 34 publications

(12 citation statements)

References 23 publications

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“…3. There are several foregoing studies where ANN is utilized to predict the fluid flow and heat transfer characteristics and concluded that ANN is an effective tool to predict the characteristics (Akdag et al, 2009;Islamoglu and Kurt, 2004;Tahavvor and Yaghoubi, 2008;Taymaz and Islamoglu, 2009). …”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

“…A number of experiments were conducted to generate the input and output data that were used in the network. An investigation of heat transfer from a constant heat fluxed surface subjected to oscillating annular flow was studied and predicted by ANN (Akdag, Komur, and Ozguc, 2009). An experimentally assessed data set was organized to be processed with the use of neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Analysis and Prediction of Unsteady Forced Convection over a Sharp and Rounded Edged Square Cylinder

Dey¹,

Das²

2016

JAFM

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An unsteady two-dimensional forced convection over a square cylinder with sharp and rounded corner edge is numerically analyzed for the low Reynolds number laminar flow regime. In this study, the analysis is carried out for Reynolds number (Re) in the range of 80 to 180 with Prandtl number (Pr) variation from 0.01 to 1000 for various corner radius (r=0.50, 0.51, 0.54, 0.59, 0.64 and 0.71). The lateral sides of the computational domain are kept constant to maintain the blockage as 5%. Heat transfer due to unsteady forced convection has been predicted by Artificial Neural network (ANN). The present ANN is trained by the input and output data which has been acquired from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software FLUENT. The heat transfer characteristics over the sharp and rounded corner square cylinder are evaluated by analyzing the local Nusselt number (Nu local ), average Nusselt number (Nu avg ) at various Reynolds number, Prandtl numbers and for various corner radii. It is found that the heat transfer rate of a circular cylinder can be enhanced by 12% when Re is varying and 14% when Prandtl number is varying by introducing a new cylinder geometry of corner radius r=0.51. It is found that the unsteady forced convection heat transfer over a cylinder can be predicted appropriately by ANN. It is also observed that the back propagation ANN can predict the heat transfer characteristics of forced convection very quickly compared to a standard CFD method.

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Section: Artificial Neural Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical Analysis and Prediction of Unsteady Forced Convection over a Sharp and Rounded Edged Square Cylinder

Dey¹,

Das²

2016

JAFM

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“…The Re, Pr and corner radii (r) are the input parameters for the present network to predict the local Nusselt number (Nu local ) which is the output of the model. The ANN model has been efficiently implemented in various heat transfer analysis in conjunction with numerical and experimental data [30][31][32][33].…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

“…A number of experiments were conducted to generate the input and output data that were used in the network. An investigation of heat transfer from a constant heat fluxed surface subjected to oscillating annular flow was studied and predicted by ANN [32]. An experimentally assessed data set was organized to be processed with the use of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Development of GEP and ANN model to predict the unsteady forced convection over a cylinder

Dey

Sarkar

2015

Neural Comput & Applic

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In the present study, forced convection heat transfer flow past a square cylinder with a rounded corner edge in an unsteady two-dimensional low Reynolds number laminar regime is numerically performed and predicted by the artificial neural network (ANN) and gene expression programming (GEP). In this study, Reynolds number (Re) is varied from 80 to 180 with Prandtl number (Pr) variation from 0.01 to 1000 for various corner radii (r = 0.50, 0.51, 0.54, 0.59, 0.64 and 0.71). Finite volume-based commercial software FLUENT is used in the present numerical solution for the output results that are used to train the present ANN and GEP model. The local Nusselt number (Nu local ) and average Nusselt number (Nu avg ) at various Reynolds numbers, Prandtl numbers and various corner radii are utilized to forecast the heat transfer characteristics. It is found that the heat transfer rate of a circular cylinder can be enhanced by 12 % when Re is varying and 14 % when the Prandtl number is varying by introducing new cylinder geometry of corner radius r = 0.51. The forced convection characteristics can be envisioned properly and very promptly by using back-propagation ANN and GEP in contrast to a regular CFD approach. It is also found that GEP is more efficient in predicting than the ANN. The outcomes obtained by the present numerical study confirm a fine accord with the available results in the literature.

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“…Besides, heat transfer in the oscillating flow has been a principal investigation area for past several decades. Oscillation-induced heat transport processes maintaining an effective heat transfer enhancement have been demonstrated [7][8][9][10][11]. Recently, the fluid flow and heat transfer characteristics of oscillating flow through porous media are explored [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Flow Visualization and Heat Transfer Characteristics of Oscillating Fluid through Pin-Fin Array in a Rectangular Channel

Jeng

Tzeng

Hsu

et al. 2013

Advances in Mechanical Engineering

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This work experimentally investigated the fluid flow and heat transfer characteristics of the pin-fin heat sink with the oscillating air flow. The oscillating air flow would be unstable in the passages among the fins due to the periodical change of flow rate. It might enhance the overall heat-transfer performance. At the present study, the pin-fin heat sinks with various fin heights were installed in the rectangular channel, resulting in different bypass clearances between the pin fins and the shroud of the test channel. The smoke flow visualizations for the oscillating-flow system were completed. The heat-transfer tests under the asymmetrically heated condition were performed to obtain the average Nusselt numbers. The smoke lines with obvious waves in the transverse direction were found in the results of the flow visualizations. By comparing to the steady flow system, there was about 20∼34% increment in the overall heat-transfer performance at the operating state without bypass clearance. However, if the bypass clearance was too big, the heat-exchange capacity of the oscillating flow was less than that of the steady flow. It demonstrates that the oscillating flow promotes the cooling performance of pin-fin heat sink at the non-bypass and specified bypass conditions.

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Estimation of heat transfer in oscillating annular flow using artifical neural networks

Cited by 34 publications

References 23 publications

Numerical Analysis and Prediction of Unsteady Forced Convection over a Sharp and Rounded Edged Square Cylinder

Numerical Analysis and Prediction of Unsteady Forced Convection over a Sharp and Rounded Edged Square Cylinder

Development of GEP and ANN model to predict the unsteady forced convection over a cylinder

Flow Visualization and Heat Transfer Characteristics of Oscillating Fluid through Pin-Fin Array in a Rectangular Channel

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