Flow regime and volume fraction identification using nuclear techniques, artificial neural networks and computational fluid dynamics

Affonso, Renato Raoni Werneck; Dam, Roos Sophia de Freitas; Salgado, William Luna; Silva, Ademir X. da; Salgado, César Marques

doi:10.1016/j.apradiso.2020.109103

Cited by 43 publications

(4 citation statements)

References 17 publications

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“…Also, a backpropagation fivelayer perceptron network was used for the scale prediction. Similarly, a study published by Affonso et al [2], measured the transmission of narrow gamma-ray radiation through the pipe. In parallel they developed data sets for training and testing artificial neural networks (ANNs) using models of circular flow regimes and Monte Carlo N Particle Code X-version (MCNPX) for classification.…”

Section: Introductionmentioning

confidence: 91%

“…In parallel they developed data sets for training and testing artificial neural networks (ANNs) using models of circular flow regimes and Monte Carlo N Particle Code X-version (MCNPX) for classification. They concluded that the existing ANN was fully capable of detecting the regime [2]. In [3] Alamoudi et al attempted to determine the thickness of the scale with a gamma attenuation method and a radial basis function neural network (RBFNN) in oil pipelines where there are two-phase flows with symmetric flow regimes.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

et al. 2022

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Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

et al. 2022

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“…Thanks to the successful application of data mining techniques, data-driven theory has been used in many areas of industrial knowledge, such as fluid dynamics and intelligent manufacturing. In the fluid dynamics and thermodynamics, many data-driven models have been used to identify of flow regimes (Salgado et al, 2010;Affonso et al, 2020;Aarabi Jeshvaghani et al, 2021) and predict boiling crises (Greenwood et al, 2017;Yan et al, 2021) using artificial neural networks (ANN). All the results show good performance in a wide experimental condition.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Data-Driven Model for Bubble Maximum Diameter in Subcooled Boiling Flow Using Artificial Neural Networks

Dong

Chen

et al. 2022

Front. Energy Res.

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In the subcooled boiling flow under low-pressure conditions, bubble characteristic diameter is of great influence on the surface heat transfer coefficient. However, large errors are still found in calculations using traditional mechanistic models or empirical correlations, especially for wide experimental condition. In this paper, we propose a widely applicable data-driven model using artificial neural networks (ANN) to predict the bubble maximum diameter and investigate the effect of experimental conditions. After a series of analyses on structural parameters and input parameters, the ANN model is established and validated based on six available experimental databases. The result shows that the relative error is around 14%. Uncertainty analysis is carried out for the four experimental conditions and two structural conditions. The results show the measuring accuracy of pressure is one of the most sensitive parameters on the prediction of bubble maximum diameter in the subcooled boiling flow under 1.0 MPa, especially for the bubble sizes larger than 0.5 mm. According to the results of uncertainty analysis, a new correlation is proposed for coefficients C and φ, which are used to express the effect of pressure and fluid dynamic. The new correlation works well for all the experimental databases, and the error for bubble datasets of large size is also modified. Furthermore, another independent validation with a low relative error to 14% is provided to prove the accuracy of the new correlation.

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“…In order to solve the above-mentioned technical problems, multi-phase flowmeters (MPFMs) using the combination of a Venturi tube and a gamma-ray densitometer have been proposed [5,6]. The attenuation rate of gamma-ray varies with the media density, which is employed by the densitometer to estimate the mixture density [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Sensor Fusion Algorithm Based on Uncertainty Analysis

Xue

Zhang

et al. 2021

Sensors

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During the research and development of multiphase flowmeters, errors are often used to evaluate the advantages and disadvantages of different devices and algorithms, whilst an in-depth uncertainty analysis is seldom carried out. However, limited information is sometimes revealed from the errors, especially when the test data are scant, and this makes an in-depth comparison of different algorithms impossible. In response to this problem, three combinations of sensing methods are implemented, which are the “capacitance and cross-correlation”, the “cross-correlation and differential pressure” and the “differential pressure and capacitance” respectively. The analytical expressions of the gas/liquid flowrate and the associated standard uncertainty have been derived, and Monte Carlo simulations are carried out to determine the desired probability density function. The results obtained through these two approaches are basically the same. Thereafter, the sources of uncertainty for each combination are traced and their respective variations with flowrates are analyzed. Further, the relationship between errors and uncertainty is studied, which demonstrates that the two uncertainty analysis approaches can be a powerful tool for error prediction. Finally, a novel multi-sensor fusion algorithm based on the uncertainty analysis is proposed. This algorithm can minimize the standard uncertainty over the whole flowrate range and thus reduces the measurement error.

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Flow regime and volume fraction identification using nuclear techniques, artificial neural networks and computational fluid dynamics

Cited by 43 publications

References 17 publications

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

An Evaluation of the Data-Driven Model for Bubble Maximum Diameter in Subcooled Boiling Flow Using Artificial Neural Networks

A Novel Multi-Sensor Fusion Algorithm Based on Uncertainty Analysis

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