| Summary: | Void fraction is one of the key parameters for gas-liquid study and detection of nuclear power system state. Based on fully convolutional neural network (FCN) and high-speed photography, an indirect void fraction measure approach for flow boiling condition in narrow channels is developed in this paper. Deep learning technique is applied to extract image features and can better realize the identification of gas and liquid phase in channels of complicated flow pattern and high void fraction, and can obtain the instantaneous value of void fraction for analyzing and monitoring. This paper verified the FCN method with visual boiling experiment data. Compared with the time-averaged experimental results calculated by the energy conservation method and the empirical formula, the relative deviations are within 11%, which verifies the reliability of this method. Moreover, the recognition results show that the FCN method has promising improvement in the scope of application compared with the traditional morphological method, and meanwhile saves the design cost. In the future, it can be applied to void fraction measurement and flow state monitoring of narrow channels under complex working conditions.
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