The thermal effects of adding a window to a wood stud wall assembly
Bibliographic record
Abstract
Improving the thermal performance of buildings is an essential element when addressing issues related to the effects of climate change on the building envelope. Minimizing energy usage of and heat losses from buildings are important measures in achieving these associated goals. The thermal performance of a building envelope can highly impact the overall performance and energy efficiency of the building. It has been shown that the thermal resistance (R-value) of a building envelope can be affected by thermal bridging sources. Hence, it is important to accurately determine the R-value of building envelopes with thermal bridging components. In this study the thermal bridging effect of a window on wood stud wall assemblies was investigated both experimentally and numerically. A 2.4 m × 2.4 m wood-stud wall assembly, typical of North American wood-frame construction practice, was fabricated with an opening to accommodate a window. The opening in the wall assemblies was first filled with EPS and thereafter tested in the guarded hot box; following which, the EPS in the wall assembly, was replaced with a window. In this study Numerical simulation packages, THERM and WINDOW, were employed to calculate the thermal resistance of a window. The incremental effect of adding this window to a wood stud wall assembly was then investigated numerically and experimentally. COMSOL Multiphysics was employed to evaluate the effective thermal resistance of the wall and the results were benchmarked against the Guarded Hot Box (GHB) results collected in the NRC facility.
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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) | 0.000 | 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".