International Journal of Refrigeration ( IF 3.9 ) Pub Date : 2021-09-23 , DOI: 10.1016/j.ijrefrig.2021.09.022 M.A. Moradkhani 1 , S.H. Hosseini 1 , P. Morshedi 2 , M. Rahimi 3 , Song Mengjie 4
This study presents a general explicit model for estimating the saturated flow boiling frictional pressure drop (FPD) in conventional (macro) and mini/micro channels heat exchangers. An extensive database including 6021 experimental data samples has been gathered from 42 published sources, covering a broad range of fluids, channel diameters and operating parameters. The new model is based on the separated model suggested by Lockhart and Martinelli (1949) for two-phase flow. Thus, the two-phase multiplier, has been estimated using the intelligent approach of genetic programming (GP). The presented model predicts the mentioned database with a reasonable value of average absolute relative deviation (AARD) of 21.34%. Moreover, 74.85% of predicted data have an error of lower than 30% of the experimental values. The entire database is compared with ten well-known two-phase pressure drop correlations for the evaluation of previous models. But all of them showed a total AARD of more than 27%. The GP model shows good accuracy for both conventional and mini/micro channels and different flow regimes, including low and high Reynolds numbers. In addition, it is applicable for estimating the boiling FPD in different operating conditions. Based on 752 additional data from 4 independent sources, the new model provides the best predictions for estimating the FPD in conventional and mini/micro channels.
中文翻译:
常规和微型/微型通道内的饱和流沸腾:使用遗传编程的摩擦压降的新通用模型
这项研究提出了一个通用的显式模型,用于估计传统(宏)和微型/微通道换热器中的饱和流动沸腾摩擦压降 (FPD)。从 42 个已发布的来源收集了包括 6021 个实验数据样本的广泛数据库,涵盖了广泛的流体、通道直径和操作参数。新模型基于 Lockhart 和 Martinelli(1949)建议的两相流分离模型。因此,两相乘法器,已经使用遗传编程(GP)的智能方法进行了估计。所提出的模型以 21.34% 的平均绝对相对偏差 (AARD) 的合理值预测上述数据库。此外,74.85% 的预测数据的误差低于实验值的 30%。整个数据库与十个著名的两相压降相关性进行比较,以评估以前的模型。但所有这些都显示了超过 27% 的总 AARD。GP 模型对传统和微型/微通道以及不同的流态(包括低雷诺数和高雷诺数)均显示出良好的准确性。此外,它适用于估计不同操作条件下的沸腾 FPD。基于来自 4 个独立来源的 752 个额外数据,