Article ID Journal Published Year Pages File Type
5451241 Solar Energy 2017 7 Pages PDF
Abstract
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 × 108 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/m2, 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.
Related Topics
Physical Sciences and Engineering Energy Renewable Energy, Sustainability and the Environment
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