Modeling flow boiling heat transfer of pure fluids through artificial neural networks

Scalabrin, G.; Condosta, M.; Marchi, P.

doi:10.1016/j.ijthermalsci.2005.09.009

Cited by 41 publications

(38 citation statements)

References 37 publications

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“…The recent development of powerful learning algorithm for the ANN (Artificial Neural Network) has led to their utilization in many engineering applications [10][11][12]. Among the various types of ANNs, RBF (Radial Basis Function) neural network has become more and more popular in engineering applications.…”

Section: The Rbf Neural Network Modelmentioning

confidence: 99%

Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

Liu¹,

Long²,

Yang³

et al. 2013

JILSA

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This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.

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Section: The Rbf Neural Network Modelmentioning

confidence: 99%

Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

Liu¹,

Long²,

Yang³

et al. 2013

JILSA

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“…A lot of work has been carried out to solve the problems of ground source heat pump and solar assisted heat pump by using the artificial neural networking [30][31][32][33][34][35]. ANN has also widely been used in several fields of heat transfer and fluid flow [36][37][38][39][40][41][42][43][44][45] like natural and forced convection, boiling and condensation, pipe flow etc.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of mixed convection in a lid-driven triangular cavity with a circular cylinder using ANN modeling

Nahak¹,

Triveni²,

Panua³

2017

IJHT

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The study of laminar mixed convection is conducted in an air filled the lid-driven triangular cavity with a static circular cylinder. The circular cylinder is fixed at the center of the triangular cavity whose diameter is varied with respect to the height of the cavity and the variation is shown in terms of aspect ratio (AR = D/H). Sides of the cavity and the cylinder are maintained at uniform temperature whereas the cylinder temperature is considered higher than the walls of the cavity. Finite volume method is adopted to solve the governing equations. The present problem is investigated for different constraint parameters such as moving walls, different geometries of the hot wall, different aspect ratio, Richardson number (Ri) and Grashof number (Gr). The outcomes of the investigation reported that the average Nusselt number is found significant at AR = 1/2, Ri = 0.1 and Gr = 105. An Artificial Neural Network (ANN) tool is employed in the study to validate the obtained numerical results of stream functions and Nusselt number.

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“…Recently a few researches have been performed by artificial neural networks for prediction of pure substances and petroleum fraction's properties 19 ; activity coefficients of isobaric binary systems 20 ; thermodynamic properties of refrigerants [21][22][23][24] ; activity coefficient ratio of electrolytes in amino acid's solutions 25 ; the phase stability problem 26 ; and dew point pressure for retrograde gases. 27 Other ANN applications include density predication of ionic liquids 28 ; modeling flow boiling heat transfer of pure fluids 29 ; predicting slag viscosity over a broad range of temperatures and slag compositions 30 ; -T-P prediction for ionic liquids 31 ; prediction of simple physical properties of mixed solvent systems, 32 etc. Those work demonstrated that neural networks can dramatically reduce the numerical errors and eliminate systematic deviation between predicted values and experimental values.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Miao¹,

Rooney²,

Gan³

2012

IJCIS

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Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C n -mim][NTf 2 ] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex.

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Modeling flow boiling heat transfer of pure fluids through artificial neural networks

Cited by 41 publications

References 37 publications

Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

Numerical investigation of mixed convection in a lid-driven triangular cavity with a circular cylinder using ANN modeling

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

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