Relationship of flow pattern, vibration, and noise in automobile refrigerant system and flow pattern identification based on convolutional neural network

In recent years, the flow-induced vibration and noise in automotive refrigerant system gradually become the main factor affecting driving comfort. However, the relationship of flow pattern, vibration, and noise is not clear, and pattern identification is not easy but necessary. In this paper, a series of experiments are conducted to investigate the relationship of flow pattern, flow-induce vibration, and noise near the thermal expansion valve in an automobile refrigerant system. The flow pattern, vibration, and noise are closely related to startup processes. Mist flow, which contains more mist two-phase mixture than transition flow and wispy-annular flow, leads to the largest amplitude of vibration and strong broadband hiss noise. Moreover, the increase in compressor speed promotes the pattern transition and the vibration amplitude in time domain but has no effect on the distribution of vibration peaks in frequency spectrum. Finally, a short-time Fourier transform and convolutional neural network combined method for flow pattern identification is developed based on the relationship of flow pattern and flow-induced noise. After using transfer learning and data augmentation, four trained network architectures show relatively high accuracy above 94% for test set. Among them, ResNet34 not only has the highest accuracy of 98.8% but also can recognize each type of flow pattern. The generalization of this method can help engineers to recognize flow patterns in air conditioning without flow visualization but only need to measure sound signals.