Multi-fidelity simulation framework with large-eddy simulations for predicting natural ventilation
Abstract/Contents
- Abstract
- Natural ventilation can play an important role in preventing the spread of airborne diseases in indoor environments. For example, a previous study in Dhaka, Bangladesh identified an association between the ventilation status of slum homes and the occurrence of pneumonia in children under five, which is the leading cause of death in this age group. However, quantifying natural ventilation flow rates is a challenging task due to significant variability in the boundary conditions that drive the flow. Thus, the objective of this research is to establish a modeling framework to precisely estimate ventilation flow patterns and ventilation rates in terms of air change per hour (ACH). The test case considered in this study is a typical low-income house in Dhaka, but the modeling strategy can be readily applied to any naturally ventilated building. The framework encompasses two computational models with different levels of fidelity: a computationally efficient building thermal model (BTM) and a high-fidelity computational fluid dynamics (CFD) model. The BTM is combined with methods for uncertainty quantification to predict mean and 95% confidence intervals for two primary quantities of interest, i.e., the average indoor air temperature and the ACH, at very low computational cost. For the CFD model, computationally extensive large-eddy simulations (LES) are performed to provide a detailed solution for the flow pattern an accurate ACH prediction at a much higher computational cost. LES is leveraged to propose a similarity relation that efficiently represents the effect of variable wind and temperature conditions on the ACH with a single non-dimensional number, the ventilation Richardson number. This similarity relationship is incorporated in the BTM to improve the accuracy of the ACH predictions. The model results are validated against field measurements and interpreted to identify efficient and robust ventilation strategies that will work under a variety of housing and weather conditions.
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 | Hwang, Yunjae |
|---|---|
| Degree supervisor | Gorle, Catherine |
| Thesis advisor | Gorle, Catherine |
| Thesis advisor | Fringer, Oliver B. (Oliver Bartlett) |
| Thesis advisor | Luby, Stephen |
| Degree committee member | Fringer, Oliver B. (Oliver Bartlett) |
| Degree committee member | Luby, Stephen |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yunjae Hwang. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/tz361ns8340 |
Access conditions
- Copyright
- © 2022 by Yunjae Hwang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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