Commercial Buildings Air Leakage Testing and Comparison of Results
Bibliographic record
Abstract
From the perspective of energy-efficient buildings, the airtightness of the building envelope plays a significant role. Presently, the requirements set out in the Canadian National Energy Code for Buildings (NECB) for estimating energy use through simulation consider the effect of airtightness of buildings to be modeled as a fixed value. Determining the air leakage during and after the building construction stage through air leakage tests is a standard energy performance method that could be used to increase the accuracy of the model. A simplified airtightness modeling methodology is desired because it would enable the industry to account for this phenomenon numerically during the design stage before construction. This paper describes a multiyear project being undertaken at the National Research Council Canada (NRC) to develop and propose such a methodology for modeling the airtightness of buildings. The basis for the methodology lies in completing air leakage tests of buildings. We tested four commercial buildings for airtightness. We focused on stand-alone commercial retail buildings to complement existing data sets. The measured air leakage characteristics of these retail buildings in terms of normalized flow rates ranged from 0.8 to 1.7 L/ (s·m2) at a pressure difference of 75 Pa. For modeling purposes, the Specific Leakage Area (SLA) ranged from 0.57 to1.2 cm2/m2. We calculated SLA utilizing the Effective Leakage Area (ELA) with a pressure difference of 4 Pa. We then normalized the ELA value using the whole envelope area (walls and roof), including the on-grade floor area.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".