Classification of two-phase flow regimes via image analysis and a neuro-wavelet approach

Sunde, Carl; Avdic, Senada; Pázsit, Imre

doi:10.1016/j.pnucene.2005.03.015

Cited by 50 publications

(17 citation statements)

References 17 publications

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“…(8) Fitness value of the mutated individual is calculated and added to population. (9) The most well-defined number of individuals (elitism) are stored for the next generation. (10) To fix the increased number of population to the initially defined number, individuals with high fitness values (poor individuals) are removed from the population randomly.…”

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

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%

“…Sunde et al [9] investigated a non-intrusive method of twophase flow identification for the bubbly and slug flow regimes in their measurements. Consequently, they classified the flow regime types by an ANN algorithm in agreement with their experimental work.…”

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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“…One of the popular tools in optimizing, predicting, and classifying scientific problems, in which there are many determinant parameters affecting the results of the system, is artificial neural networks. In recent years, artificial neural networks have been widely used (or widely focused on) for predicting volumetric phase fraction and identifying flow regime (Ghanbarzadeh, Hanafizadeh, & Saidi, 2012;Rosa, Salgado, Ohishi, & Mastelari, 2010;Sunde, Avdic, & Pázsit, 2005). Jing, Xing, Liu, and Bai (2006) developed a dual modality densitometry model in which they used an artificial neural network to predict gas and water volume fractions in oil-water-gas flows.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of multilayer perceptron, radial basis function, fuzzy inference system, and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

Khayat

Afarideh

2019

Expert Systems

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Application of predictive models in industrial multiphase flow metering has attracted an increasing attention recently. Void fraction (VF), water–liquid ratio (WLR), and flow regime are key parameters, measured by oil/water/gas multiphase flow metres (MPFM) in petroleum industry. Artificial neural networks and fuzzy inference systems (FIS) are reliable and efficient computational models, which can be simply implemented on microprocessors of MPFMs, having the advantages of trainability, adaptability, and capability to model non‐linear functions. In this paper, a wavelet‐based adaptive neuro‐FIS (WANFIS) is introduced and validated by the prediction of multiphase flow measurement critical parameters including flow regime, VF, and WLR. The performance of the proposed WANFIS model is then compared with multilayer perceptron (MLP), radial basis function (RBF) network, and an FIS trained by fuzzy c‐means and a subtractive clustering method in the prediction of flow parameters in a customized designed structure of oil/water/gas MPFM. Structural parameters of all predictive models are first optimized to yield the most efficient structure for the available dataset. Comparison is then made between the optimized predictive models, in terms of mean squared error of parameter prediction, computation time, and repeatability of the prediction process. According to the obtained results, MLP model using Levenberg–Marquardt training algorithm and WANFIS model using gradient‐based back propagation dynamical iterative learning algorithm are the most efficient models, which give the best performance compared with other used models. All predictive models can predict the flow regime with 100% accuracy, whereas the highest inaccuracy is related to the prediction of WLR. The results of this study can be used to select and develop the most appropriate predictive model applicable in predicting and identifying flow measurement parameters in industrial MPFMs.

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“…Sunde et al conducted a precise work based on image processing of data obtained partly from dynamic neutron radiography recordings of real two-phase flow in a heated metal channel, and partly by visible light from a two component mixture of water and air. They classified the flow regime type by an artificial neural network (ANN) algorithm [10]. Hanafizadeh et al detected the two-phase flow regime in up riser pipe of an airlift pump by image processing technique.…”

Section: Introductionmentioning

confidence: 99%

Experimental determination of void fraction in surface aeration using image processing technique

et al. 2015

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In this paper, a new method for determination of void fraction in surface aeration process is presented and discussed. The proposed method is based on the image processing technique. The experimental setup has been designed to create various surface aeration conditions in the water. Void fraction has been calculated for the wide range of water height, impeller immersion depth and rotational speed. Experiments have been performed in an open cubic tank with side length of 60 cm, equipped with one Rushton disk turbine. Moreover, the void fraction has been measured with level gauge method. The results showed that the image processing technique provides more accurate results than the level gauge measurements for void fraction calculation in surface aeration especially in low void fraction aeration. In addition, the experimental data revealed that increase in impeller immersion depth and rotational speed increase void fraction and oxygen transfer rate in surface aeration process.

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Classification of two-phase flow regimes via image analysis and a neuro-wavelet approach

Cited by 50 publications

References 17 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

Performance evaluation of multilayer perceptron, radial basis function, fuzzy inference system, and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

Experimental determination of void fraction in surface aeration using image processing technique

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