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Predicting the efficiency of natural ventilation

Temperature contours showing the difference between the temperature evolution in the full conjugate model and the model with a volume-averaged representation of the thermal mass
In a follow up to their 2010 PEEC seed grant "Energy simulation tools for buildings," professors Iaccarino and Fischer used numerical simulations to predict the temperature dynamics in a modern building, Y2E2, on Stanford's campus. The building's nighttime ventilation flush was underperforming expectations.
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In moderate climates, passive cooling by nighttime ventilation can result in significant energy savings, but modeling natural ventilation systems for design and performance predictions remains a challenging task.

In this project, researchers developed an integral methods (box model), which predicts the floor-averaged temperature over time, and a detailed, computational fluid dynamics-based model, which describes the configuration and the various openings in the building. By considering realistic outdoor temperature conditions the work illustrated how the simplified box model can qualitatively predict the temperature dynamics, but requires calibration either from sensor or the CFD model to be quantitatively correct. The results illustrate that a combination of the two modeling strategies (in a multi-fidelity setting) can be a useful tool for design and analysis of the thermal conditions in large building for the purpose of design and control.

Conference paper: Modeling night-time ventilation in Stanford’s Y2E2 building (PDF) 14th International Conference on Wind Engineering Gorle, C., Chigurupati, A., Iaccarino, G. (2015)

Conference presentations: Uncertainty quantification of box model and CFD predictions for night-time ventilation in Stanford's Y2E2 building 68th Annual Meeting of the APS Division of Fluid Dynamics Gorle, C., Iaccarino, G. (2015)

Comparison of CFD simulation of night purge ventilation to full-scale building measurements 67th Annual Meeting of the APS Division of Fluid Dynamics Chigurupati, A., Gorle, C., Iaccarino, G. (2014)