Synchronized coupling of thermal mass and buoyancy ventilation: wood versus concrete
Bibliographic record
Abstract
Abstract This study describes an experiment that validates scaling rules for the design of thermal mass, coupled with buoyancy ventilation, suggesting that wood can perform as well as concrete if these rules are respected. The scaling rules potentially offer a shortcut for early design, showing how to tune the interior temperature and rate of buoyancy ventilation by adjusting the thickness and surface area of an internal thermal mass. A pair of test chambers (H~1m), comparing wood and concrete internal thermal masses, were located in Alabama, USA and Montreal, Canada, and left outside in sun- and-wind-sheltered environments for consecutive months. The thermal mass thicknesses were optimized so the chambers would maintain similar interior temperatures and airflow rates. The scaling rules predicted the behavior of the chambers with reasonable accuracy and both the concrete and wood thermal masses performed equivalently. For instance, the test chambers in Alabama were both designed to damp the maximum exterior temperature by a factor 1-1/Ai ≈ 0.7 and produce a maximum ventilation flow rate of Q ≈ 0.37 l/s. The measured damping was 1-1/Ai = 0.81±0.1 and 1-1/Ai = 0.81±0.13 for the concrete and wood chambers, respectively, while the maximum flow rates were 0.374±0.03 and 0.36±0.04 l/s, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".