A predictive simulation framework for buoyancy-driven natural ventilation in office buildings
Abstract/Contents
- Abstract
- Natural ventilation and cooling can significantly reduce building energy consumption, but inherent variability in boundary and operating conditions makes optimal and robust design of natural ventilation a challenging task. The goal of this study is to enable robust design and operation of natural ventilation systems by developing an efficient simulation framework that can predict the building's thermal response under buoyancy-driven natural ventilation. A simplified building thermal model that solves for the time-evolution of the volume-averaged indoor air temperature with uncertain quantification (UQ) can provide an intuition of the likely effects of design choices under a variety of operating conditions in the early building design stage. Subsequently, computational fluid dynamics (CFD) simulations can be employed to fine-tune the building design at the design development stage, and the CFD results can be used to reduce the uncertainty in the building thermal model coefficients that determine the flow and heat transfer rates. The predictive capability of the resulting multi-fidelity framework was validated with a carefully designed full-scale experiment performed in an operational atrium building. A CFD-based experimental design ensured optimal temperature sensor placement under the full range of expected operating conditions to ensure an accurate characterization of the volume-averaged indoor air temperature, as well as of the spatial variability in the temperature field. The prediction of thermal performance matches well to the experiments with discrepancies lower than 0.38 °C for all the approaches, and the final widths of the 95% confidence interval are reduced to less than 0.18 °C, indicating promising predictive capabilities for buoyancy-driven natural ventilation at all stages of designs.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Chen, Chen |
|---|---|
| Degree supervisor | Gorle, Catherine |
| Thesis advisor | Gorle, Catherine |
| Thesis advisor | Fischer, Martin, 1960 July 11- |
| Thesis advisor | Kitanidis, P. K. (Peter K.) |
| Degree committee member | Fischer, Martin, 1960 July 11- |
| Degree committee member | Kitanidis, P. K. (Peter K.) |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Chen Chen. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/bq301fy1404 |
Access conditions
- Copyright
- © 2022 by Chen Chen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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