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Validating Computational Predictions of Natural Ventilation in Stanford’s Y2E2 Building

TitleValidating Computational Predictions of Natural Ventilation in Stanford’s Y2E2 Building
Publication TypeConference Paper
Year of Publication2019
AuthorsChen, C, Gorle, C
Conference NameThe 7th International Symposium on Computational Wind Engineering 2018
Date Published06/2018
Other NumbersTR238
Keywordscomputational fluid dynamics, experiment, Natural ventilation, uncertainty quantification
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.

URLhttps://purl.stanford.edu/jq045vh4041