![]() also 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). analyzed the wave signals measured in the vertical pipeline by using the adaptive optimal kernel time-frequency algorithm (AOK TFR), and distinguished different flow patterns and their complex dynamic behaviors. carried out a multi-scale marginal spectral entropy analysis on the differential pressure signal, which can distinguish the four flow regimes in the beam channel, macroscopically. identified flow patterns through the pressure characteristics of each branch of the riser and the probability density function (PDF) of the differential pressure. Unfortunately, the indirect method is mainly applied for identification of the flow patterns in pipes and channels, while few studies have been reported concerning centrifugal pumps. Pressure, differential pressure, gas volume fraction and void fraction are several parameters that are frequently employed. The second type is the indirect measurement method, which works by measuring the fluctuating signals reflecting the flow characteristics and then processing them for analysis. obtained distribution images of high gas content in the impeller by the gamma-ray scanning technique, and determined the effect of inlet flow conditions on the performance of centrifugal pumps. In addition, they obtained a flow pattern versus pump performance graph under different operating conditions. 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. 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. investigated the flow pattern in the impeller of a centrifugal pump using high-speed camera technology and obtained four different flow patterns. Visual inspection, high speed photography and tomographic imaging are several typical methods used. The first one is the direct measurement method, which determines the flow pattern from the flow image. 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.Ĭurrently, two methods are available for the identification of two-phase flow patterns. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. 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. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. ![]() 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. 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. 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.
0 Comments
Leave a Reply. |