盾形冷凝模块用于蒸发冷却设备消雾的消光值预测  

Extinction value prediction of defogging effect for evaporative cooling equipments with shield condensation module

作　　者：宋慧娟 章立新 刘婧楠[1,2] 席鹏飞 高明 陈永保[1,2] Song Huijuan;Zhang Lixin;Liu Jingnan;Xi Pengfei;Gao Ming;Chen Yongbao(University of Shanghai for Science and Technology,Shanghai;Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering,Shanghai)

机构地区：[1]上海理工大学,上海 [2]上海市动力工程多相流动与传热重点实验室,上海 [3]上海理工大学能源与动力工程学院工程热物理研究所,上海200093

出　　处：《暖通空调》2022年第12期120-125,113,共7页Heating Ventilating & Air Conditioning

基　　金：国家自然科学基金项目(编号:51976127)。

摘　　要：针对蒸发冷却设备冬季存在的羽雾问题,搭建了盾形冷凝模块+混风腔消雾实验装置。采用Logistic分布函数对消光值与混风比关系的实验结果进行了拟合,平均拟合度为0.9854。通过建立BP神经网络模型对出口羽雾的消光值进行了预测研究,并提出以消光值作为消雾效果的评价指标。预测结果表明,BP神经网络对消光值的预测精度较高,在95%的置信度下,30组消光预测值的相关系数、平均相对误差和均方根误差分别为0.99899±0.00008、(0.7633±0.0204)%和(6.441±0.222)×10^(-3)。According to the plume fog problem of evaporative cooling equipment in winter,builds a defogging experimental device with shield condensation module and mixed air chamber.The Logistic distribution function is used to fit the experimental results of the relationship between extinction value and mixed wind ratio,and the average fit degree is 0.9854.The extinction value of the outlet plume fog is predicted by establishing a BP neural network model,and the extinction value is proposed as an evaluation index for the defogging effect.The prediction results show that the BP neural network has high prediction accuracy for extinction value,and the correlation coefficient,mean relative error and root mean square error of the 30 groups of extinction prediction values are 0.99899±0.00008,(0.7633±0.0204)%and(6.441±0.222)×10^(-3),respectively,under 95%confidence.

关 键 词：盾形冷凝模块 蒸发冷却设备 消雾 BP神经网络 消光值 预测 

分 类 号：TU991.42[建筑科学—市政工程]