Evaluating convective heat transfer coefficients using neural networks

Jambunathan, K.; Hartle, S.L.; Ashforth-Frost, S.; Fontama, Valentine

doi:10.1016/0017-9310(95)00332-0

Cited by 134 publications

(45 citation statements)

References 4 publications

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“…ANN has also been successfully used in the analysis of heat transfer data as well as heat transfer coefficient [4]- [6]. Sablani [7] in his research, developed a non-iterative procedure of an ANN to determine the fluid to particle heat transfer coefficient, Islamoglu [8] applied a new approach of an ANN model for predicting the rate of heat transfer of a heat exchanger which is the wire-on-tube type.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

Ang¹,

Woo²,

Ehsan³

2017

JOCET

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Abstract-This paper focus on a new methodology approach to evaluate more accurately the energy generated from Thermoelectric Generator (TEG) under the influence of its operating environmental parameters. An artificial neural network (ANN) model for predicting the energy generated by a TEG in its operating environment has been developed. The dataset generated through a validated finite volume method is trained in a supervised way and tested by a multi-layer perceptron (MLP) to predict the energy generated. However, the degree of ambiguity may vary widely across the whole range of input values therefore in this paper, a new methodological approach will be incorporated to not only predict the average value but as well as evaluating the reliability of the output value with the use of a scheme which is made up of two coupled neural network. Apart from predicting the output values, this model can perform reverse ANN to predict the input value when provided with an output value.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

Ang¹,

Woo²,

Ehsan³

2017

JOCET

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“…Artiÿcial neural network methodology has been applied by Jambunathan et al in Reference [1] to deduce convective heat transfer coe cients from experimental data using liquid crystal thermography. Accuracies of up to 2.7% have been obtained using the 3-6-3-1 network.…”

Section: Introductionmentioning

confidence: 99%

Evaluating convective heat transfer coefficients from a given set of data by using Mathematica

Mikhailov

2003

Commun. Numer. Meth. Engng.

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“…transfer and fluid mechanics with emphasis on visualization processing techniques such as particle image velocimetry [see also Jambunathan et al (1996)]. The authors reported several references that used ANNs to recognize different geometric patterns in multiphase flows.…”

Section: Ashforth-frost Et Al (1995) Described a Multitude Of Uses Omentioning

confidence: 99%

Heat Transfer and Friction in Helically-Finned Tubes Using Artificial Neural Networks

Chamra¹,

Mago²,

Zdaniuk³

2009

Advances in Multiphase Flow and Heat Transfer (Volume 2)

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Evaluating convective heat transfer coefficients using neural networks

Cited by 134 publications

References 4 publications

Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

Evaluating convective heat transfer coefficients from a given set of data by using Mathematica

Heat Transfer and Friction in Helically-Finned Tubes Using Artificial Neural Networks

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