Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review

Summary: Aerogel materials with super-insulating, visual-penetrable, and sound-proof properties are promising in buildings, whereas the coupling effect of various parameters in complex porous aerogels proposes challenges for thermal/visual performance prediction. Traditional physics-based models fac...

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Autor principal:	Yuekuan Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Artificial intelligence applications Energy application Surface treatment Polymers Science Q
Acceso en línea:	https://doaj.org/article/893b2c528d844c1e9a0b01ae670ec3e1
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id	oai:doaj.org-article:893b2c528d844c1e9a0b01ae670ec3e1
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spelling	oai:doaj.org-article:893b2c528d844c1e9a0b01ae670ec3e12021-11-22T04:28:46ZArtificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review2589-004210.1016/j.isci.2021.103420https://doaj.org/article/893b2c528d844c1e9a0b01ae670ec3e12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589004221013912https://doaj.org/toc/2589-0042Summary: Aerogel materials with super-insulating, visual-penetrable, and sound-proof properties are promising in buildings, whereas the coupling effect of various parameters in complex porous aerogels proposes challenges for thermal/visual performance prediction. Traditional physics-based models face challenges such as modeling complexity, heavy computational load, and inadaptability for long-term validation (owing to boundary condition change, degradation of thermophysical properties, and so on). In this study, a holistic review is conducted on aerogel production, components prefabrication, modeling development, single-, and multi-objective optimizations. Methodologies to quantify parameter uncertainties are reviewed, including interface energy balance, Rosseland approximation and Monte Carlo method. Novel aerogel integrated glazing systems with synergistic functions are demonstrated. Originalities include an innovative modeling approach, enhanced computational efficiency, and user-friendly interface for non-professionals or multidisciplinary research. In addition, human knowledge-based machine learning can reduce abundant data requirement, increase performance prediction reliability, and improve model interpretability, so as to promote advanced aerogel materials in smart and energy-efficient buildings.Yuekuan ZhouElsevierarticleArtificial intelligence applicationsEnergy applicationSurface treatmentPolymersScienceQENiScience, Vol 24, Iss 12, Pp 103420- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence applications Energy application Surface treatment Polymers Science Q
spellingShingle	Artificial intelligence applications Energy application Surface treatment Polymers Science Q Yuekuan Zhou Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review
description	Summary: Aerogel materials with super-insulating, visual-penetrable, and sound-proof properties are promising in buildings, whereas the coupling effect of various parameters in complex porous aerogels proposes challenges for thermal/visual performance prediction. Traditional physics-based models face challenges such as modeling complexity, heavy computational load, and inadaptability for long-term validation (owing to boundary condition change, degradation of thermophysical properties, and so on). In this study, a holistic review is conducted on aerogel production, components prefabrication, modeling development, single-, and multi-objective optimizations. Methodologies to quantify parameter uncertainties are reviewed, including interface energy balance, Rosseland approximation and Monte Carlo method. Novel aerogel integrated glazing systems with synergistic functions are demonstrated. Originalities include an innovative modeling approach, enhanced computational efficiency, and user-friendly interface for non-professionals or multidisciplinary research. In addition, human knowledge-based machine learning can reduce abundant data requirement, increase performance prediction reliability, and improve model interpretability, so as to promote advanced aerogel materials in smart and energy-efficient buildings.
format	article
author	Yuekuan Zhou
author_facet	Yuekuan Zhou
author_sort	Yuekuan Zhou
title	Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review
title_short	Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review
title_full	Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review
title_fullStr	Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review
title_full_unstemmed	Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review
title_sort	artificial neural network-based smart aerogel glazing in low-energy buildings: a state-of-the-art review
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/893b2c528d844c1e9a0b01ae670ec3e1
work_keys_str_mv	AT yuekuanzhou artificialneuralnetworkbasedsmartaerogelglazinginlowenergybuildingsastateoftheartreview
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Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review

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