Journal ArticleDOI
Precise volume fraction prediction in oil-water-gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector
Gholam Hossein Roshani,Seyed Amir Hossein Feghhi,Ahmad Mahmoudi-Aznaveh,Ehsan Nazemi,A. Adineh-Vand +4 more
TLDR
A proposed ANN architecture is used to predict the oil, water and air percentage, precisely, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source.About:
This article is published in Measurement.The article was published on 2014-05-01. It has received 121 citations till now. The article focuses on the topics: Detector & Perceptron.read more
Citations
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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
Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation
TL;DR: In this article, a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows.
Journal ArticleDOI
Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation
Ehsan Nazemi,Seyed Amir Hossein Feghhi,Gholam Hossein Roshani,R. Gholipour Peyvandi,Saeed Setayeshi +4 more
TL;DR: In this paper, a multilayer perceptron neural network was used to predict void fraction in gas-eliquid two-phase flows with a mean relative error of < 1.4%.
Journal ArticleDOI
Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique
Ehsan Nazemi,Gholam Hossein Roshani,Seyed Amir Hossein Feghhi,Saeed Setayeshi,E. Eftekhari Zadeh,A. Fatehi +5 more
TL;DR: In this paper, a gamma-ray transmission technique is used to measure the void fraction and identify the flow regime of a two-phase flow using two detectors which were optimized in terms of detector orientation.
Journal ArticleDOI
Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using radial basis function
TL;DR: In this paper, the authors proposed a new methodology for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems using dual energy fan-beam gamma-ray attenuation technique and artificial neural networks.
References
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Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
MCNP-A General Monte Carlo N-Particle Transport Code
TL;DR: In this article, the authors present a practical guide for the use of general-purpose Monte Carlo code MCNP, including several examples and a discussion of the particular techniques and the Monte Carlo method itself.
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Original Contribution: On learning the derivatives of an unknown mapping with multilayer feedforward networks
A. Ronald Gallant,Halbert White +1 more
TL;DR: It is shown that a net can be trained so that the map and its derivatives are learned, and least squares and similar estimates are strongly consistent in Sobolev norm provided the number of hidden units and the size of the training set increase together.
Journal ArticleDOI
Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks
César Marques Salgado,Cláudio Márcio do Nascimento Abreu Pereira,Roberto Schirru,Luis Eduardo Barreira Brandão +3 more
TL;DR: In this paper, a new methodology for flow regimes identification and volume fraction prediction in water-gas-oil multiphase systems is presented based on gamma-ray pulse height distributions (PHDs) pattern recognition by means of artificial neural networks (ANNs).