Deep Learning Strategies for Transport Properties Prediction in Flow Condensation via Acoustic Signatures

Flow condensation is critical to the efficient operation of power generation, refrigeration, water purification, and other important applications. Flow condensation in tubes has several advantages over surface condensation, especially in compact and high-power-density applications, where real-time, nondestructive monitoring and prediction of the regime transitions is desirable. Compared to image-based techniques, wideband acoustic sensing (e.g., acoustic emission and accelerometer) allows for higher sampling rates to capture high-frequency interface oscillations that are critical to the flow regime transitions and works well even for condensation in opaque tubes. In this paper, a machine learning framework is introduced to detect the annular to slug flow regime transition and interfacial instabilities, as well as characterizing the instantaneous vapor quality, heat flux and pressure drop of flow condensation, based on imaging, acoustics/vibration, temperature, and pressure data. Multimodal data fusion is implemented to integrate different signals at various operation conditions, sampling rates and test section dimensions. The utilization of multimodal data provides greater prediction accuracy and better feature extraction over a single data source. The model is used for the real-time, non-destructive detection of regime transitions and accurate prediction of thermofluidic performance of flow condensation, thereby improving overall system performance and reliability.