TR238: Validating Computational Predictions of Natural Ventilation in Stanford’s Y2E2 Building
Abstract/Contents
- Abstract
- Natural ventilation can significantly reduce building energy consumption, but uncertainties in a future building’s operating conditions make robust design a challenging task. In a previous study, we used an integral model and a computational fluid dynamics (CFD) model with uncertainty quantification (UQ) to predict the air temperature during night-time ventilation in the Y2E2 building on Stanford’s campus. The predictions showed a slightly higher cooling rate for the volume- averaged temperature than building measurements, and the initial thermal mass temperature and window discharge coefficients had an important influence on the results. The objective of the present study is to further investigate the effect of these uncertain parameters, and to validate the spatial variability in the temperature field predicted by the CFD model. Additional measurements, using thermocouples and hotwires, were implemented to achieve this objective. The spatial variability in the temperature field was found to be an important reason for the discrepancies observed in the previous study. In addition, the measured initial thermal mass temperatures were on the lower end of the previously assumed range, and the measured velocities were found to be slightly higher than the CFD predictions. The data will be used to inform an updated UQ study and further experiments for validation of the CFD.
Description
Type of resource | text |
---|---|
Date created | 2019 |
Creators/Contributors
Author | Chen, Chen | |
---|---|---|
Author | Gorle, Catherine |
Subjects
Subject | CIFE |
---|---|
Subject | Natural ventilation |
Subject | computational fluid dynamics |
Subject | uncertainty quantification |
Subject | experiment |
Genre | Technical report |
Bibliographic information
Related Publication | The 7th International Symposium on Computational Wind Engineering 2018 |
---|---|
Location | https://purl.stanford.edu/jq045vh4041 |
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.
Preferred citation
- Preferred Citation
- Chen, Chen and Gorle, Catherine. (2019). TR238: Validating Computational Predictions of Natural Ventilation in Stanford’s Y2E2 Building. Stanford Digital Repository. Available at: https://purl.stanford.edu/jq045vh4041
Collection
CIFE Publications
View other items in this collection in SearchWorksContact information
- Contact
- cife-email@stanford.edu
Also listed in
Loading usage metrics...