基于流型的R290水平管内流动沸腾压降实验研究

丙烷(R290)作为一种性能优异的自然制冷剂，其两相流动压降特性在换热器设计及制冷系统优化等方面起到重要作用，而且目前对于低质量流率以及低饱和压力条件下的压降分析相对较少，且仅有少数研究结合流型进行分析。因此，开展了R290在内径6 mm的水平光管内压降特性实验研究。在如下实验工况范围内，质量流率70~190 kg·m^-2·s^-1，热通量10.6~73.0 kW·m^-2，饱和压力0.215~0.415 MPa，干度0~1，获取了压降实验数据，并进一步基于实验工况以及流型分析了加速压降、两相摩擦压降的变化趋势。对比了现有的摩擦压降关联式并基于Friedel模型，使用Re_v/Re_l 和液相Froude数Fr表征气液相相互作用，获取了一个新的基于流型的两相摩擦压降关联式。新模型可以很好地预测R290实验数据，预测结果的平均相对偏差(ARD)为-0.2%，平均绝对相对偏差(AARD)为5.2%，λ_30%为97.9%。对比文献中的实验数据，10组数据预测结果的ARD为10.0%，AARD为19.3%，λ_30%为80.3%，由此可见新模型具有一定的预测精度和适用性。

Experimental study of flow boiling pressure drop of R290 in a horizontal tube based on flow pattern

Propane (R290) is a natural refrigerant with excellent performance, and its two-phase flow pressure drop characteristics play an important role in heat exchanger design and refrigeration system optimization.Its boiling pressure drop characteristics are important for heat exchanger design and performance analysis in refrigeration facilities. Despite that there are studies about the pressure drop of R290, there are few studies on analyzing the pressure drop combined with the flow pattern. There is also a lack of work on the pressure drop of R290 under low mass flux (G<200 kg·m^-2·s^-1) and low saturation pressure (p<0.5 MPa) conditions. An experimental study on the pressure drop characteristics of R290 in a horizontal tube was conducted with mass fluxes ranging from 70 kg·m^-2·s^-1 to 190 kg·m^-2·s^-1, heat fluxes ranging from 10.6 kW·m^-2 to 73.0 kW·m^-2, saturation pressures ranging from 0.215 MPa to 0.415 MPa, covering the entire vapor quality range. The influence of experimental conditions and the flow pattern on the frictional pressure drop and the acceleration pressure drop have been analyzed. By comparing the existing frictional pressure drop correlations and using Re_v/Re_l and Froude number Fr to represent the two-phase interaction based on the Friedel model, a new flow pattern-based frictional pressure drop correlation for two phases was obtained. The new correlation can predict the pressure drop well with an ARD (average relative deviation) of -0.2%, an AARD (average absolute relative deviation) of 5.2%, and 97.9% of data points within ±30% error bands. Compared with the experimental data in the literature, the average ARD of the 10 groups of data prediction results is 10.0%, the average AARD is 19.3%, and λ_30% is 80.3%, which shows that the new model has certain prediction accuracy and applicability.