Design of high-performance water-in-glass evacuated tube solar water heaters by a high-throughput screening based on machine learning: A combined modeling and experimental study
Design of high-performance water-in-glass evacuated tube solar water heaters by a high-throughput screening based on machine learning: A combined modeling and experimental study
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DOI：
10.1016/j.solener.2016.12.015
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发表时间：
2017-01-15
期刊：
影响因子：
6.7
通讯作者：
Cheng, Kewei
中科院分区：
文献类型：
--
作者：
Liu, Zhijian;Li, Hao;Cheng, Kewei
如何设计出高集热率的玻璃中水真空管太阳能热水器(WGET-SWH)一直是人们关注的问题。在这里，我们提出了一种基于机器学习的高通量筛选方法来设计和筛选3.538125×10(8)种可能的WGET-SWH外部特性组合，通过比较它们的预测集热率来发现有前途的WGET-SWHS。实验中安装了两个新设计的WGET-SWHS，它们的集热率(分别为11.32和11.44MJ/m(2))高于我们之前数据库中所有915个测量样本的集热率。这项研究表明，我们可以使用HTS方法来修改WGET-SWH的设计，而对太阳能热水器外部特性与集热率之间高度复杂的关联知之甚少。(C)2016爱思唯尔有限公司。保留所有权利。
How to design water-in-glass evacuated tube solar water heater (WGET-SWH) with high heat collection rates has long been a question. Here, we propose a high-throughput screening (HTS) method based on machine learning to design and screen 3.538125 x 10(8) possible combinations of extrinsic properties of WGET-SWH, to discover promising WGET-SWHs by comparing their predicted heat collection rates. Two new-designed WGET-SWHs were installed experimentally and showed higher heat collection rates (11.32 and 11.44 MJ/m(2), respectively) than all the 915 measured samples in our previous database. This study shows that we can use the HTS method to modify the design of WGET-SWH with just few knowledge about the highly complicated correlations between the extrinsic properties and heat collection rates of solar water heaters. (C) 2016 Elsevier Ltd. All rights reserved.