A statistical model for dew point air cooler based on the multiple polynomial regression approach
A statistical model for dew point air cooler based on the multiple polynomial regression approach
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DOI：
10.1016/j.energy.2019.05.213
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发表时间：
2019-08-15
期刊：
影响因子：
9
通讯作者：
Li, Junming
中科院分区：
文献类型：
--
作者：
Akhlaghi, Yousef Golizadeh;Ma, Xiaoli;Li, Junming
蒸发冷却系统的快速评估在这项先进技术的实际工程应用中已成为必要的。本文绕过了性能过程的细节，并率先开发了基于多重多项式回归(MPR)的统计模型来预测露点冷却(DPC)系统的性能。研究了数以千计的数值和实验数据，并建立了统计模型。建立的统计模型将性能参数与关键操作参数关联起来，包括流动和几何特性。选择的运行参数是进气条件，包括温度、相对湿度、流量以及进气上方的工作空气分数，而制冷量、性能系数(COP)、压降、露点和湿球效率被选为性能参数。所考虑的几何特性包括通道高度、通道间距和换热器的层数。用R2、MRE和MSE度量对不同多项式次的模型进行了评估。选择了8次多项式模型。制冷量、性能系数、压降、露点和湿球效率的最大相对误差分别为6.1%、7.54%、0.07%、3.54%和2.53%。最后，作为例子，该模型被用来预测DPC系统在随机运行条件和干燥气候(如拉斯维加斯)下的性能。本研究开发的模型将使DPC系统的快速预测成为可能。(C)2019爱思唯尔有限公司。保留所有权利。
Swift assessment of evaporative cooling systems has become a necessity in practical engineering applications of this advanced technology. This paper bypasses details of the performance process and pioneers in developing a statistical model based on the multiple polynomial regression (MPR) to predict the performance of a dew point cooling (DPC) system. Thousands of numerical and experimental data are explored and the statistical model is produced. The developed statistical model correlates the performance parameters with the key operational parameters, including the flow and geometric characteristics. The selected operational parameters are, intake air conditions, including temperature, relative humidity and flow rate as well as the working air fraction over the intake air, while cooling capacity, coefficient of performance (COP), pressure drop, dew point and wet-bulb effectiveness are selected as performance parameters. The considered geometric characteristics are channel height, channel interval and number of layers in heat and mass exchanger. The model with different polynomial degrees is assessed by R2, MRE and MSE metrics. The 8th degree polynomial model is selected. The maximum relative error of the cooling capacity, coefficient of performance, pressure drop, dew point and wet-bulb effectiveness are 6.1%, 7.54%, 0.07%, 3.54% and 2.53% respectively. Finally, as examples, the model is used to predict the performance of the DPC system in random operating conditions and in a dry climate i.e. Las Vegas. Model developed in this study would enable the swift prediction of the DPC system. (C) 2019 Elsevier Ltd. All rights reserved.