Evaluating the Air Pressure Response of Multizonal Buildings
Bibliographic record
Abstract
Air flow in buildings is a complex flow and pressure distribution problem that makes quantification difficult. However, certain parameters have recently become easy to quantify – specifically the air pressure relationships within buildings. The measured building air pressure field can be used with network analysis to solve the building flow and leakage regime creating an analytical macro model of the building flow and leakage regime. The response of the analytical model can be further tuned by perturbing both the building air pressure field and the analytical model. Building analysis typically focuses on flows and requires that all flow paths into and out of a control volume be defined. The flow path resistances need to be characterized. Determining all air flow paths and determining the flow path resistances directly is difficult. As such, estimates of these flow path resistances are commonly used. These estimates are based on limited field data and laboratory measurements. The literature provides some component values that vary by orders of magnitude and their application is often unable to predict building flow fields (ASHRAE, 1997). Standard building analysis develops the building pressure field from the flow field. This paper argues that developing the flow field from the building pressure field is more powerful. Determining the characteristics of the building pressure field directly is considerably easier than determining flow path resistances. It allows closing of the gap between the mathematical sophistication of available multi-cell air flow models and the necessary input information defining the building boundary conditions. This approach allows the pressure response of the building to be used to ‘‘tune’’ the models extending the range of their applicability and accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".