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Instantaneous And Objective Flow Regime Identification Method For The Vertical Upward And Downward Co-current Two-phase Flow

机译:垂直向上和向下并流两相流的瞬时和客观流态识别方法

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An instantaneous and objective flow regime identification method for the two-phase flow is represented in the paper. The previous methods have been evolved to be an objective by replacing the heuristic determination using the sensor signals in terms of the statistical indexes. However, the flow pattern in the rapid transient or the inherently unstable flow such as the flow in the microgravity cannot be identified because of the observation time for the statistical meaning. The design of the neural network fed by the preprocessed impedance signals of the cross-sectional void fraction is proposed here to satisfy the requirement of both objective and an instantaneous identification. For the preprocessing, the both feed forward neural network and the self-organized neural network as an objective reasoning engine were tested using the experimental data for both upward and downward two-phase flow in the pipes with the inner diameter of 25.4 mm and 50.8 mm. It was found that the proposed flow regime identifier could successfully identify the flow regime using the short term observation data within 1 s. Furthermore, the obtained flow regimes were in a good agreement with the M ishima-Tshii criteria for the upward two-phase flow. However, for the downward flow, it was found that the current flow regimes are in reasonable agreement with the Usui criteria for the slug flow region, only. Other flow regimes have strong dependency on the pipe diameter and some phenomena related to the kinematic wave propagation which was not considered reasonably in the previous criteria. Therefore, theoretical studies to build up the transition criteria for the co-current downward two-phase flow are recommended.
机译:提出了一种瞬时,客观的两相流流态识别方法。通过根据统计指标代替使用传感器信号的启发式确定,先前的方法已经发展成为目标。然而,由于统计意义上的观察时间,所以无法识别快速瞬态流动或固有不稳定的流动(例如微重力流动)中的流动模式。在此提出了由横截面空隙率的预处理阻抗信号馈送的神经网络的设计,以满足客观和瞬时识别的要求。对于预处理,使用内径分别为25.4 mm和50.8 mm的管道中的上下两相流的实验数据,测试了前馈神经网络和自组织神经网络作为客观推理引擎。 。发现所提出的流动状态识别符可以在1 s内使用短期观测数据成功识别流动状态。此外,获得的流态与向上两相流的Mshima-Tshii标准非常吻合。但是,对于向下的流动,发现当前的流态仅与Us流区域的Usui标准合理地一致。其他流态对管道直径和与运动波传播有关的某些现象有很强的依赖性,而在先前的标准中并未合理考虑。因此,建议进行理论研究以建立并流向下两相流的过渡标准。

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