Towards harmonizing the NFRC and CEN window performance simulation methods
Bibliographic record
Abstract
Studies have found that the European Committee for Standardization (CEN) and National Fenestration Rating Council (NFRC) methods produce different U-values for the same window resulting in confusion when comparing products. A comparative evaluation of the NFRC and CEN U-value calculation methods was conducted for North American residential high-performance window products with focus on the most influential parameters in determining the whole window U-value for high-performance windows. Using two-dimensional conduction simulation software, four North American high-performance frame types with double, triple, and quad glazing combinations were simulated and calculated according to the NFRC and CEN standard methods. Overall, the trend showed that for the specific window combinations of this study, the higher the performance of the insulated glazing unit (IGU), the lesser the differences in the whole window U-value of both methods. The results showed an overall difference of 1 to 11% in whole window U-value when using the NFRC and CEN standards, lower than other studies. Generally, the NFRC standard resulted in the lower U-value for each case. Recommendations for harmonization of the two standards include aligning boundary conditions, frame cavity models, and material conductivities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".