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Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds

This Data Article presents simulation data and methodology on fire in the cavity of naturally ventilated Double Skin Façade (DSF) with Venetian blinds. The simulation data includes glazing surface temperature data and the Input and Output Source Code files. The data for the validation of the model i...

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Detalles Bibliográficos
Autores principales: Huang, Youxian, Yeboah, Siegfried, Shao, Jingjing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823162/
https://www.ncbi.nlm.nih.gov/pubmed/36624771
http://dx.doi.org/10.1016/j.dib.2022.108859
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author Huang, Youxian
Yeboah, Siegfried
Shao, Jingjing
author_facet Huang, Youxian
Yeboah, Siegfried
Shao, Jingjing
author_sort Huang, Youxian
collection PubMed
description This Data Article presents simulation data and methodology on fire in the cavity of naturally ventilated Double Skin Façade (DSF) with Venetian blinds. The simulation data includes glazing surface temperature data and the Input and Output Source Code files. The data for the validation of the model is also presented along with its methodology, input source code file and output temperature results. The comprehensive methodology used to obtain this data from the National Institute of Standards and Technology's (NIST) Fire Dynamics Simulator (FDS) and PyroSim are presented. The data presented can provide theoretical benchmarks for architects, engineers, researchers, and designers when incorporating Venetian blinds in DSFs. It can also help fire fighters and engineers to theoretically assess the spread of fire in buildings with DSFs incorporating Venetian blinds.
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spelling pubmed-98231622023-01-08 Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds Huang, Youxian Yeboah, Siegfried Shao, Jingjing Data Brief Data Article This Data Article presents simulation data and methodology on fire in the cavity of naturally ventilated Double Skin Façade (DSF) with Venetian blinds. The simulation data includes glazing surface temperature data and the Input and Output Source Code files. The data for the validation of the model is also presented along with its methodology, input source code file and output temperature results. The comprehensive methodology used to obtain this data from the National Institute of Standards and Technology's (NIST) Fire Dynamics Simulator (FDS) and PyroSim are presented. The data presented can provide theoretical benchmarks for architects, engineers, researchers, and designers when incorporating Venetian blinds in DSFs. It can also help fire fighters and engineers to theoretically assess the spread of fire in buildings with DSFs incorporating Venetian blinds. Elsevier 2022-12-28 /pmc/articles/PMC9823162/ /pubmed/36624771 http://dx.doi.org/10.1016/j.dib.2022.108859 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Huang, Youxian
Yeboah, Siegfried
Shao, Jingjing
Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds
title Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds
title_full Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds
title_fullStr Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds
title_full_unstemmed Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds
title_short Numerical data on fire in the cavity of naturally ventilated double skin façade with Venetian blinds
title_sort numerical data on fire in the cavity of naturally ventilated double skin façade with venetian blinds
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823162/
https://www.ncbi.nlm.nih.gov/pubmed/36624771
http://dx.doi.org/10.1016/j.dib.2022.108859
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