3D thermal model for predicting the thermal resistance of spray polyurethane foam wall assemblies
Bibliographic record
Abstract
A Wall Energy Rating (WER) system has been proposed to account for simultaneous thermalconduction and air leakage heat losses through a full-scale wall system. Determining the overall WERrequires that two standard tests be performed on a full-scale wall specimen: a thermal resistance testand an air leakage test. A 3D model representation of the wall specimen is developed to combine theresults of these tests to obtain an accurate prediction of the wall thermal resistance (apparent R-value)under the influence of air leakage. Two types of wall configurations were simulated. The first one wasa standard 2' by 6' wood stud frame construction, made of spruce, spaced at 16" (406 mm) o/c in 2.4m x 2.4 m full-scale wall specimens. The second type of wall configuration was similar to the first oneexcept that it included through-wall penetrations (a window opening, a pipe, an electric box and a duct),according to the Canadian Construction Material Centre (CCMC) Air Barrier Guide 07272 (1996).The cavities of the two types of wall configurations were filled with different types of insulations (i.e.Open Cell Spray Polyurethane foams). Results showed that the present model predicted the R-values ofall walls to within +5%. After gaining confidence in the model predictions, it was used to determine theapparent R-values for these walls at different air leakage rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".