Effect of Cone Angle on the Hydraulic Characteristics of Globe Control Valve

Globe control valve is widely used in chemical, petroleum and hydraulic industries, and its throttling feature is achieved by the adopting of valve plug. However, very limited information is available in literature regarding the influence of valve plug on the internal and external features in globe control valves. Thus the effect of valve plug is studied by CFD and experiment in this paper. It is obtained from external features that the pressure drop between upstream and downstream pressure-sampling position increases exponentially with flow rate. And for small valve opening, the increment of pressure drop decreases with the increase of cone angle（β）. However, with the increase of valve opening, the effect of cone angle diminishes significantly. It is also found that the cone angle has little effect on flow coefficient（Cv） when the valve opening is larger than 70%. But for the cases less than 70%, Cv curve varies from an arc to a straight line. The variation of valve performance is caused by the change of internal flow. The results of internal flow show that cone angle has negligible effect on flow properties for the cases of valve opening larger than 70%. However, when valve opening is smaller than 70%, the pressure drop of orifice decreases with the increase of β, making the reduction in value and scope of the high speed zone around the conical surface of valve plug, and then results in a decreasing intensity of adjacent downstream vortex. Meanwhile, it is concluded from the results that the increase of cone angle will be beneficial for the anti-cavitation and anti-erosion of globe control valve. This paper focuses on the internal and external features of globe control valve that caused by the variation of cone angle, arriving at some results beneficial for the design and usage of globe control valve.

A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit

Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.