Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks

Salgado, César Marques; Pereira, Cláudio M.N.A.; Schirru, Roberto; Brandão, Luís Eduardo Barreira

doi:10.1016/j.pnucene.2010.02.001

Cited by 151 publications

(47 citation statements)

References 14 publications

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“…(6) New individuals are created among the individuals who are binary matched, by the crossing over operators. (7) A mutation is applied to the newly formed individual. (8) Fitness value of the mutated individual is calculated and added to population.…”

Section: Calculation Procedures For the Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Salgado et al [7] presented a new approach, based on gamma-ray pulse height distributions pattern recognition by means of ANNs, for the identification of flow regimes and prediction of volume fraction in water-gas-oil multiphase systems. Their ideal and static theoretical models are developed for annular, stratified and homogeneous regimes using MCNP-X mathematical code.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of empirical correlations on the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube using artificial intelligence algorithms

2011

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The heat transfer characteristics of R134a during downward condensation are investigated experimentally and numerically. While the convective heat transfer coefficient, two-phase multiplier and frictional pressure drop are considered to be the significant variables as output for the analysis, inputs of the computational numerical techniques include the important two-phase flow parameters such as equivalent Reynolds number, Prandtl number, Bond number, Froude number, Lockhart and Martinelli number. Genetic algorithm technique (GA), unconstrained nonlinear minimization algorithm-Nelder-Mead method (NM) and non-linear least squares error method (NLS) are applied for the optimization of these significant variables in this study. Regression analysis gave convincing correlations on the prediction of condensation heat transfer characteristics using ±30% deviation band for practical applications. The most suitable coefficients of the proposed correlations are depicted to be compatible with the large number of experimental data by means of the computational numerical methods. Validation process of the proposed correlations is accomplished by means of the comparison between the various correlations reported in the literature.

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Section: Calculation Procedures For the Genetic Algorithm (Ga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of empirical correlations on the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube using artificial intelligence algorithms

2011

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“…Recent works show use of machine learning methods as e.g. artificial neural networks (ANN) [9][10][11][12][13][14][15][16][17][18] for this purpose. The flow structures classification with ANN is comprised of three steps: data acquisition and pre-processing, feature extraction, and structures classification.…”

Section: Introductionmentioning

confidence: 99%

Application of ANN and PCA to two-phase flow evaluation using radioisotopes

et al. 2017

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Abstract. In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug -bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN) to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA) was used. It was found that the reduction of signals features allows for building a network with better performance.

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“…A quantitative description of flow regimes can be obtained by the analysis of the signals registered in radioisotope measurements. According to [22][23][24][25][26][27][28][29][30][31] …”

Section: Introductionmentioning

confidence: 99%

Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 1: time domain

Hanus

Zych

Petryka

et al. 2016

EPJ Web of Conferences

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Abstract. The paper presents an application of the gamma-absorption method to study a gas-liquid two-phase flow in a horizontal pipeline. In the tests on laboratory installation two 241 Am radioactive sources and scintillation probes with NaI(Tl) crystals have been used. The experimental set-up allows recording of stochastic signals, which describe instantaneous content of the stream in the particular cross-section of the flow mixture. The analyses of these signals by statistical methods allow to determine the mean velocity of the gas phase. Meanwhile, the selected features of signals provided by the absorption set, can be applied to recognition of the structure of the flow. In this work such three structures of air-water flow as: plug, bubble, and transitional plug -bubble one were considered. The recorded raw signals were analyzed in time domain and several features were extracted. It was found that following features of signals as the mean, standard deviation, root mean square (RMS), variance and 4 th moment are most useful to recognize the structure of the flow.

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Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks

Cited by 151 publications

References 14 publications

Investigation of empirical correlations on the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube using artificial intelligence algorithms

Investigation of empirical correlations on the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube using artificial intelligence algorithms

Application of ANN and PCA to two-phase flow evaluation using radioisotopes

Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 1: time domain

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