Wind Tunnel Testing on Low Rise Bunkie Structures and Its Application as Affordable Storm Shelters
Bibliographic record
Abstract
In Canada, the number of wind-related damage to structures and losses in lives keeps increasing due to climate change. Residents of rural areas are more exposed to those fatalities due to the lack of storm shelters. Storm shelters can provide reliable protection to people during high-intensity wind events and can also be used during the re-construction phase. Currently, people in rural areas use Bunkie structures for guest accommodation and equipment storage only. However, it would be beneficial if those structures could also be used as storm shelters during extreme wind events. This dissertation focuses on wind tunnel testing of common Bunkie structures used in Ontario to assess their aerodynamic performance under boundary layer and downburst flows, to be used as storm shelters. First, a turbulence generating system was developed at Ryerson University wind tunnel in order to model boundary layer flows encountered in sparse and built environments (i.e., open, suburban, and urban). This was designed by employing Computational Fluid Dynamics Large-Eddy simulations, followed by validation tests at Ryerson University wind tunnel. This system was also used to develop a downburst generating system consisting of multiple rotating louvers to model downburst outflows. Subsequently, wind tunnel testing for the three commonly used Bunkie shapes in Ontario was conducted under both boundary layer and downburst wind fields. This led to an aerodynamic database that consists of force and moment coefficients for Bunkiestructures forboth wind fields. The databaseforboundarylayer flowswas usedto compare between experimental results and North American design codes (i.e., NBCC 2015, and ASCE 716). Moreover, the developed database was coupled with the climate analysis in Southern Ontario to quantify the actual forces and moments on Bunkie structures seen during boundary layer and downburst events. Then, Finite Element models were developed to study the behavior of the Bunkie structures and evaluate their potential usage as storm shelters. Finally, potential retrofitting solutions were suggested to strengthen Bunkie structures to resist high-intensity wind events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".