| 1 |
Stochastic uncertainty-based optimisation on an aerogel glazing building in China using supervised learning surrogate model and a heuristic optimisation algorithm
Zhou YK, Zheng SQ
Renewable Energy, 155, 810, 2020 |
| 2 |
Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization
Zhou YK, Zheng SQ
Renewable Energy, 153, 375, 2020 |
| 3 |
A model and method to determine solar extinction coefficient of aerogel granules layer through experiment under real climatic condition
Liu Y, Chen YM, Zheng SA, Li YP, Lu B, Lu ML, Zhang DR
Energy and Buildings, 190, 144, 2019 |
| 4 |
Exergy analysis of a hybrid PV/T system based on plasmonic nanofluids and fit silica aerogel glazing
Du M, Tang GH, Wang TM
Solar Energy, 183, 501, 2019 |
| 5 |
Dynamic heat transfer model and applicability evaluation of aerogel glazing system in various climates of China
Chen YM, Xiao YL, Zheng SQ, Liu Y, Li YP
Energy, 163, 1115, 2018 |
| 6 |
Quantitative research on the influence of particle size and filling thickness on aerogel glazing performance
Lv YJ, Wu HJ, Liu YC, Huang Y, Xu T, Zhou XQ, Huang RD
Energy and Buildings, 174, 190, 2018 |
| 7 |
Impact of convection on thermal performance of aerogel granulate glazing systems
Ihara T, Grynning S, Gao T, Gustavsen A, Jelle BP
Energy and Buildings, 88, 165, 2015 |
| 8 |
Insulating glazing units with silica aerogel granules: The impact of particle size
Gao T, Jelle BP, Ihara T, Gustavsen A
Applied Energy, 128, 27, 2014 |
