As one of the cooling methods for the blanket in a fusion reactor, forced convection using helium gas has been considered as one of the promising candidates. In this study, in order to clarify the effect of tube length on heat transfer for forced flow of helium gas under various velocities and pressures, experiments on forced convection heat transfer for helium gas in a small diameter tube were conducted. A circular tube made of platinum with an inner diameter of 1.8 mm and a heated length of 50 mm was used in this experiment. The tube was heated with exponentially increasing heat inputs. As a result of the experiment, the heat transfer process can be considered in the quasi-steady state when the e-folding time is larger than about 1.5 s. In addition, the heat transfer coefficient increased with the increases in velocity and pressure. The heat transfer was also higher than that of conventional turbulent heat transfer correlation. By comparing with the experimental results of tube with different heated lengths, it was found that the heat transfer coefficients for the heated length of 50 mm were higher than those of the one with a length of 90 mm.

Non-uniform temperature distributions in air-conditioned areas can reduce the energy efficiency of air conditioners and cause uncomfortable thermal sensations for occupants. Furthermore, it is impractical to use physical sensors to measure the local temperature at every position. This study developed a soft-sensing model that integrates the fundamentals of thermodynamics and transport phenomena to predict the temperature at the target position in space. Water experiments were conducted to simulate indoor conditions in an air-conditioning cooling mode. The transient temperatures of various positions were measured for model training and validation. The velocity vectors of water flow were acquired using the particle image velocimetry method. Correlation analysis of various positions was conducted to select the input variable. The soft-sensing model was developed using the multiple linear regression method. The model for the top layer was modified by the correction of dead time. The experimental results showed the temperature inhomogeneity between different layers. The temperature at each target position under two initial temperatures and two flow rates was accurately predicted with a mean absolute error within 0.69 K. Moreover, the temperature under different flow rates can be predicted with one model. Therefore, this soft-sensing model has the potential to be integrated into air-conditioning systems.