The Identification of Gas-liquid Co-current Two Phase Flow Pattern in a Horizontal Pipe Using the Power Spectral Density and the Artificial Neural Network ( ANN )

This paper presents a new method of the flow pattern identification on the basis of the analysis of Power Spectral Density (PSD) from the pressure difference data of horizontal flow. Seven parameters of PSD curve such as mean (K1), variance (K2), mean at 1-3 Hz (K3), mean at 3-8 Hz (K4), mean at 8-13 Hz (K5), mean at 13-25 Hz (K6) and mean at 25-30 Hz (K7) were used as training vector input of Artificial Neural Networks (ANN) in order to identify the flow patterns. From the obtained experimental of 123 operating conditions consisting of stratified flow, plug and slug, ANN was trained by using 100 data operation and 23 tested data. The results showed that the new method has a capability to identify the flow patterns of gas-liquid two phase flow with a high accuracy.


Introduction
The knowledge of two phase flow is of important in engineering process, such as oil industry, chemical process, power generation, and phase change heat exchanger apparatus.The common characteristics of parameter related to the flow pattern are the pressure gradient and the void fraction.The main issue in two phase flow researches is the relationship between the pressure fluctuation and flow pattern.In general, the pressure fluctuations resulted from the liquid-gas flow and their statistical characteristics are very interest for the objective characterization of the flow patterns because the required sensors are robust, inexpensive and relatively well established, and therefore more likely to be implemented in the industrial systems (Drahos et al., 1991).Drahos et al. (1996) has already conducted the study of the wall pressure fluctuations in a horizontal gas-liquid flow by using the methodology of chaotic time series analysis in order to obtain a new insight of the dynamics of the intermittent flow patterns.Next, Franca et al. (1991) presented the fractal techniques for flow pattern identification and classification.They observed that PSD and PDF could not easily be used for the flow pattern identification and the objective discrimination between separated and intermittent regimes.Ding et al. (2007) reported the application of the Hilbert-Huang Transform (HHT) to the dynamic characterization of transportation of the gas-liquid two-phase flow in a horizontal pipe.Matsui et al. (2007) studied the sensing method of gas-liquid two phase flow in horizontal pipe on the basis of statistical processing of differential pressure fluctuation.The flow pattern, the void fraction and the velocity of gas phase were measured by PDF and cross correlation.From the view point of engineering, the above method's are subjective in nature, therefore a newly scientific based method is needed.Artificial Neural Network (ANN) provides an alternative method for either modeling phenomena which are too difficult to model from fundamental principles, or reduce the computational time for predicting expected behavior.Artificial neural network is based on the important rules for classifying the flow pattern.Neural network stimulate human mind and demonstrate high intelligence and it can be trained to study the correct output and classify training exercises.Here, neural network needs knowledge input for training.After the training, the neural network can classify the similar flow pattern with a high accuracy.Cai et al. (1994) applied the Kohonen self-organizing neural network in order to identify the flow pattern in a horizontal air-water flow.In their work, the neural network was trained with stochastic features derived from the turbulent absolute pressure signals obtained across a range of the flow regimes.The feature map succeeded in classifying samples into distinctive flow regime classes consistent with the visual flow regime observation.Next, Wu et al. (2001) recorded the pressure difference signal in pipe flow and used the fractal analysis to analyze them for identification of flow pattern.By using the ANN, the good result was obtained but it is only considered stratified, intermittent and annular flows.For this reason, Jia et al. (2005) proposed a new flow pattern identification method based on PDF and neural network at the horizontal flow in pipe.Xie et al. (2004) examined the feasibility of the implementation of the artificial neural network (ANN) technique for the classification of flow regimes in three phase gas/liquid/pulp fiber systems by using the pressure signals as input.For this purpose, the flow behavior by using the power spectral density function is needed to implement the parameterization of the information contained in the spectral patterns.1.An input layer 2. A hidden layer sigmoid bipolar and

An output layer linear transfer/ramp
Training process: Step 1. Design the structure of neural network and input parameters of the network Step 2. Get initial weights W and initial θ values from randomizing.
Step 3. Input training data matrix X and output matrix T.
Step 4. Compute the output vector of each neural unit.
(a) Compute the output vector H of the hidden layer Step 8. Repeat step 3 to step7 until convergence.
Testing process: Step 1. Input the parameters of the network.
Step 2. Input the W and θ Step 3. Input an unknown data matrix X Step 4. Compute the output vector Figure 1 sh compresso individuall is released of 24 mm Figure 2 sh and (c) cor Figure 2. Figu Compute the modification of W and θ (η is the learning rate) (a) Compute the modification of W and θ of the output layer Renew W and θ of the hidden layer