Prediction of heat transfer on a flat plate subjected to a transversely pulsating jet using artificial neural networks

Akdag, Unal; Komur, M. Aydin; Akçay, Selma

doi:10.1016/j.applthermaleng.2016.01.147

Cited by 22 publications

(13 citation statements)

References 25 publications

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“…Nevertheless, number of neurons in hidden layer (layers) hasn't got any specific values which is related to the problem and needs trials for determination. It should be pointed out that the neurons in one layer are connected to the neurons of next layer, but there is no interconnection between neurons at the same layer (Akdag et al, 2016). In this paper, ANN is composed of one input layer, one hidden layer (the number neurons (Z) of hidden layer will be determined) and one output layer illustrated in Figure 2.…”

Section: Network Structurementioning

confidence: 99%

“…It is worth mentioning that outputs are created by summation and activation functions. In these layers, all neuron's inputs are multiplied by weights and are then aggregated by biases to produce summation functions as follows (Akdag et al, 2016)…”

Section: Network Structurementioning

confidence: 99%

“…The ANN's input data are classified into three groups called training, validation and testing data. According to the literature, different ratios have been considered for the data set so far (Akdag et al, 2016). A total of 128 numerical data points were selected for ANN considering Eckert number (Ec), squeezing parameter (S) and solid volume fraction (Φ) as input variables and Nusselt number (Nu) as output variable.…”

Section: Training Validation and Testing Datamentioning

confidence: 99%

See 2 more Smart Citations

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

Gorgani

Maghsoudi²,

Sadeghi

2019

European Journal of Sustainable Development Research

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This study reveals the thermal behavior of an unsteady nanofluid streaming between two parallel plates by using artificial neural network (ANN). Initially, a similarity solution is employed to simplify the partial differential equations (PDSs) and convert them into a system of coupled nonlinear ordinary differential equations (ODEs). Subsequently, a numerical analysis is undertaken to verify the predicted results applying forth order Runge Kutta method. ANN is utilized to provide a nonlinear map between the considered input parameters such as solid volume fraction (Φ), Eckert number (Ec) and a moving parameter which represents the movement of the parallel plates (S), and output parameters like Nusselt number (Nu). Considering the accuracy of the current results, it is concluded that ANN method can be a potential reliable approach for function approximation. Results indicate that an optimal network with 16 neurons exists in hidden layer for which the value of RMSE for testing data is found to be 0.001364.

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Section: Network Structurementioning

confidence: 99%

Section: Network Structurementioning

confidence: 99%

Section: Training Validation and Testing Datamentioning

confidence: 99%

See 1 more Smart Citation

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

Gorgani

Maghsoudi²,

Sadeghi

2019

European Journal of Sustainable Development Research

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“…Subsequently, the experimental results have been used to develop an analytical model that can predict unsteady natural convection at vertical flat plate [12]. Akdaq et al [13] implement artificial neural network to predict the heat transfer of flat plate under pulsating jet, the more advanced numerical method produces more accurate results. Kröger [14] deduced the correlation of convection heat transfer coefficients by taking into account the radiative effect, which is well aligned to the experimental measurement.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the effect of different materials on convective heat transfer

Ibrahim¹,

Chong²,

Rajasekharan³

et al. 2020

JMES

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Conventionally, the study of convection heat transfer merely focuses on the behavior of air flow without considering the conductive effect of the horizontal flat plate. However, it is expected that the conductive effect of the horizontal plate somewhat affects the air flow temperature across the flat plate. Therefore, it is motivated to study the variation of air flow temperature across different materials of flat plate in various time frame. The materials used in this study are aluminium, stainless steel and cast iron. Infrared camera and FloEFD simulation software are used to measure the upper surface temperature of the flat plate. For forced convection, the study is carried out within the range of 103 £ Re £ 104 and within the range of 1 × 107 £ Ra < 2.2 × 107 for natural convection. Flow velocity of 2.3 m/s, 4.1 m/s and 5.2 m/s are used for the forced convection. The results showed that aluminium plate cools down faster than the other two metal plates used in all scenarios. Stainless steel’s temperature goes down faster compared to cast iron. These results were supported by the fact that aluminium has higher heat transfer rate of other metals. For forced convection, the discrepancies of temperatures between experimental and simulation studies are below 10%, which demonstrates that the results are reasonably acceptable. For natural convection, even though the discrepancies between simulation and experimental results on temperature variations are relatively large, the temperatures varied in similar pattern. This indicates that the results are reliable.

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“…Nanoakışkanların pulsatif akışta ısı transferine etkilerinin incelendiği çalışmada; nanoakışkanların pulsatif akışı durumunda pulsatif parametrelerin artması ile sürtünme faktöründe artış ve ısı transferinde iyileşme olduğu belirlenmiştir [17][18][19].…”

Section: Introductionunclassified

Yüksek Isi Akili Hareketli̇ Bi̇r Plakadan Olan Isi Transferi̇ne Nanoakişkanlarin Ve Çarpan Jetleri̇n Müşterek Etki̇si̇ni̇n Sayisal İncelenmesi̇

Kılıç¹,

Gökçek²

2021

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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Gelişen teknoloji endüstriyel cihazlar üzerindeki ısıl yükler gün geçtikçe arttırmaktadır. Bu ısıl yüklerin cihazlara vereceği tahribatı engellemek için ısı transferinin artırılması çözülmesi gereken önemli bir problem haline gelmiştir. Bu çalışmada; üzerinde yüksek ısı akısı olan hareketli bir plakadan olan ısı transferinin, nanoakışkanlar ve çarpan jet kullanılarak iyileştirilmesinin sayısal incelemesini hedeflemiştir. Farklı nanoakışkan giriş hızları için hesaplanan

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Prediction of heat transfer on a flat plate subjected to a transversely pulsating jet using artificial neural networks

Cited by 22 publications

References 25 publications

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

Investigation of the effect of different materials on convective heat transfer

Yüksek Isi Akili Hareketli̇ Bi̇r Plakadan Olan Isi Transferi̇ne Nanoakişkanlarin Ve Çarpan Jetleri̇n Müşterek Etki̇si̇ni̇n Sayisal İncelenmesi̇

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