Journal ArticleDOI
Integrating artificial neural networks and empirical correlations for the prediction of water-subcooled critical heat flux
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TLDR
The prediction of CHF is approached by a hybrid system which couples a heuristic correlation with a neural network, and it partially overcomes the ‘black-box’ character typical of the straight application of ANNs because the neural network role is limited to the correlation tuning.About:
This article is published in Revue Générale de Thermique.The article was published on 1997-12-01. It has received 35 citations till now. The article focuses on the topics: Critical heat flux & Heat transfer.read more
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Journal ArticleDOI
Applications of artificial neural networks for thermal analysis of heat exchangers – A review
TL;DR: In this paper, the authors reviewed the applications of ANN for thermal analysis of heat exchangers and highlighted the limitations of ANN in this field and its further research needs in the field.
Journal ArticleDOI
Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data
TL;DR: In this article, the authors consider the problem of accuracy in heat rate estimations from artificial neural network (ANN) models of heat exchangers used for refrigeration applications and present a methodology based on the cross-validation technique to find regions where not enough data are available to construct a reliable neural network.
Journal ArticleDOI
Applications of ANNs in flow and heat transfer problems in nuclear engineering: A review work
TL;DR: Recent work on the applications of ANNs for predicting the flow regime, pressure drop, void fraction, critical heat flux, onset of nucleate boiling, heat transfer coefficient and boiling curve has been reviewed.
Journal ArticleDOI
Artificial Neural Networks (ANNs) : A New Paradigm for Thermal Science and Engineering
TL;DR: The purpose of the present review is to point out the recent advances in ANN and its successes in dealing with a variety of important thermal problems and its future prospects are indicated.
Journal ArticleDOI
Artificial neural network modelling of the thermal performance of a compact heat exchanger
TL;DR: In this article, the authors used artificial neural network models to simulate the thermal performance of a compact fin-tube heat exchanger with air and water/ethylene glycol anti-freeze mixtures as the working fluids.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.