Comparison of Computer Simulation and Mock-up Laboratory Testing of the Thermal Performance of Unitized Curtain Wall: A Case Study
Bibliographic record
Abstract
Glazing systems contribute to the greatest energy loss within the enclosure of the building due to their high thermal transmittance. For this reason, numerous studies have focused on predicting the thermal performance of curtain wall systems. This research focuses on evaluating the correlation between computer simulation and lab testing of a given curtain wall design. Two (2) four-story full-size specimens were tested under specific conditions in a large-scale chamber and measured temperatures recorded during the condensation test were compared with simulated interior surface temperature. Results obtained show a mean temperature difference between the measured and simulated temperatures of 4.12 °C and 3.46 °C for the base and improved design, respectively. In addition, the difference in the overall U-value between each testing method resulted in a EUI difference below 3%. The findings from this study indicate that even though simulations are a valuable tool to predict the thermal performance of curtain wall systems, a larger temperature difference between both methods is observed when there is a discrepancy between simulated and actual detail. Moreover, the difference in the overall U-value between mock-up and simulation did not significantly affect the energy performance of the building.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".