Experimental study on heat transfer behavior and prediction of heat transfer deterioration of supercritical nitrogen in vertical tubes

Heat transfer characteristics of supercritical cryogenic fluids play important role in thermal equipment for liquid air energy storage, liquid hydrogen, etc. In this study, a low-temperature and high-pressure experimental device was established to investigate the heat transfer behavior of supercritical nitrogen. The combined effects of forced convection and drastic changes in physical properties should be considered to investigate the heat transfer behavior, especially the abnormal heat transfer phenomenon in the medium q^”/G range where the heat transfer coefficient (HTC) rises with q^”/G. A new HTC correlation considering pseudo-boiling number was proposed, and a mean absolute relative deviation (MARD) of 12.7% was achieved. The results showed that the bulk fluid at the inlet had a high heat absorption capacity because it was close to T_pc, which inhibited the onset of heat transfer deterioration (HTD) together with the inlet effect. Machine learning has high accuracy in the HTD prediction and avoids the prediction of over-HTD in existing models. A support vector machine model considering the inlet state and heating conditions was trained and tested in this work. Several parameters were used to identify HTD cases from multiple latitude features. The machine learning model has a high prediction accuracy of 96%.