Abstract
The accurate identification of the gas–liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas–liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow rate and the pump rotational speed on the flow characteristics of the centrifugal pump were investigated experimentally. Four typical flow patterns in the impeller of the centrifugal pump, i.e., the bubble flow, the agglomerated bubble flow, the gas pocket flow and the segregated flow, were obtained, and the corresponding flow pattern maps were drawn. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. By selecting the appropriate network super parameters, including the neuron numbers in the hidden layer, the learning rate and the activation function, the different flow patterns in the centrifugal pump impeller were identified. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. In addition, the Kappa coefficient, the Macro-F1 and the Micro-F1 were 0.93, 0.95 and 0.95, respectively, indicating that the model established in this paper can well identify the flow pattern in the impeller of a centrifugal pump.
Highlights
As important energy conversion and fluid transportation equipment, centrifugal pumps are widely used in the petrochemical, coal chemical, and oil and gas extraction fields [1]
We found that the bubble flow (BF) pattern only appeared under the condition of extremely low inlet gas volume fraction (IGVF) and no obvious bubble accumulated in the impeller (Figure 2a)
It can be seen that the training data of the BF that the agglomerated bubble flow (ABF) and the gas pocket flow (GPF) increased from 6, 30 and 66 to 72 after data enhancement by SMOTE, which was equal to the increase to the segregated flow (SF) pattern
Summary
As important energy conversion and fluid transportation equipment, centrifugal pumps are widely used in the petrochemical, coal chemical, and oil and gas extraction fields [1]. The effects of the inlet gas volume fraction (IGVF), liquid flow rate and rotational speed on the distribution of the gas–liquid phase in the impeller was analyzed, as were the pump pressure increment and efficiency. Verde et al [8] observed four typical flow patterns using the high-speed camera technique and determined that the centrifugal pump performance variation is related to the gas–liquid two-phase flow characteristics in the pump. Sun et al [19] identified gas–liquid two-phase flow patterns after analyzing the differential pressure signal in the horizontal Venturi based on the time-frequency signal processing method of the adaptive optimal kernel (AOK). Euh et al [20] carried out a wavelet analysis on the void fraction signal measured in the vertical channel and obtained the identification criteria of different flow patterns by calculating the effective local wavelet energy and scale in the time-frequency diagram. The identification results of four flow patterns were analyzed and evaluated
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