The study of two-phase flow instability is of interest in all fields encountering flow boiling. Compact systems relying on flow boiling due to its superior transport capabilities of heat and mass are prone to several modes of flow instability, which adversely affect the safety and performance of systems. As such, understanding of the flow instability is critical to the design and operation of compact flow boiling systems. In current paper, the instability caused by channel-to-channel interaction is investigated experimentally in a test section with 6 parallel small rectangular channels having hydraulic diameter of Dh = 526.2 μm by using deionized water as working fluid. Boiling leads to very asymmetrical flow distribution within the 6 channels, which results in the simultaneous existence of different flow regimes along the transverse direction of test section. Time traces of pressure drop for different channels and operation conditions are analyzed to explore the properties of channel-to-channel interaction by using spectral analysis. Bubble dynamics including nucleation and growth give rise to flow fluctuations in individual channel, and the time intervals between flow fluctuations are caused by a series of time-scale self-convolutions of bubble duration. Based on the aforementioned mechanism, the time intervals between flow fluctuations can be predicted statistically by the gamma distributions.

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Many types of rotating mechanical equipment, such as the primary pump, turbine, and fans, are key components of fourth-generation (Gen IV) advanced reactors. Given that these machines operate in challenging environments with high temperatures and liquid metal corrosion, accurate problem identification and health management are essential for keeping these machines in good working order. This study proposes a deep learning (DL)-based intelligent diagnosis model for the rotating machinery used in fast reactors. The diagnosis model is tested by identifying the faults of bearings and gears. Normalization, augmentation, and splitting of data are applied to prepare the datasets for classification of faults. Multiple diagnosis models containing the multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), and residual network (RESNET) are compared and investigated with the Case Western Reserve University datasets. An improved Transformer model is proposed, and an enhanced embeddings generator is designed to combine the strengths of the CNN and transformer. The effects of the size of the training samples and the domain of data preprocessing, such as the time domain, frequency domain, time-frequency domain, and wavelet domain, are investigated, and it is found that the time-frequency domain is most effective, and the improved Transformer model is appropriate for the fault diagnosis of rotating mechanical equipment. Because of the low probability of the occurrence of a fault, the imbalanced learning method should be improved in future studies.