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Full-scale experimental data of natural ventilation in an atrium building

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Abstract/Contents

Abstract
Night-time passive cooling is an energy-efficient cooling strategy, but the design of passive cooling systems relies on imperfect computational models, which require validation. The spatial variability in the temperature field when performing model validation is significant. The dataset contains temperature measurements collected from full-scale experiments in a three-story atrium building during the natural ventilation process. The dataset is made publicly available on the Stanford digital repository as we believe experimental data can be valuable assets for future studies about natural ventilation in atrium buildings.

Description

Type of resource Dataset
Date created June 2, 2022
Date modified December 5, 2022
Publication date July 2, 2022; June 2, 2022

Creators/Contributors

Author Chen, Chen ORCiD icon https://orcid.org/0000-0001-5573-8780 (unverified)
Author Gorle, Catherine

Subjects

Subject Natural ventilation
Subject Atrium buildings
Subject Temperature
Subject Full scale experiment
Genre Data
Genre Database
Genre Data sets
Genre Dataset
Genre Databases

Bibliographic information

Related item
Preferred citation
Chen Chen, Catherine Gorlé, Optimal temperature sensor placement in buildings with buoyancy-driven natural ventilation using computational fluid dynamics and uncertainty quantification, Building and Environment, Volume 207, Part B, 2022, 108496, ISSN 0360-1323, https://doi.org/10.1016/j.buildenv.2021.108496.
Preferred citation
Chen Chen, Catherine Gorlé, Full-scale validation of CFD simulations of buoyancy-driven ventilation in a three-story office building, Building and Environment, 2022 (In print)
Preferred citation
Chen Chen, Lup Wai Chew, Catherine Gorlé, Characterizing spatial variability in the temperature field to support thermal model validation in a naturally ventilated building, Journal of Building Performance Simulation, 2022 (Under review)
Preferred citation
Lup Wai Chew, Chen Chen, Catherine Gorlé, Improving thermal model predictions for naturally ventilated buildings using large-eddy simulations, Building and Environment, 2022 (In print)
DOI https://doi.org/10.25740/xc945cg8168
Location https://purl.stanford.edu/xc945cg8168

Access conditions

Use and reproduction
User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
License
This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).

Preferred citation

Chen, C. and Gorle, C. (2022). Full-scale experimental data of natural ventilation in an atrium building. Stanford Digital Repository. Available at https://purl.stanford.edu/xc945cg8168

Collection

Stanford Research Data

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Contact information

chenc2@stanford.edu
gorle@stanford.edu

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