Mean Operating Temperature (MOT) of Commercial Roof Assembly and Its Impact on the Energy Performance
Bibliographic record
Abstract
In the thermal design of low sloped roofing assemblies, two parameters are overlooked, one is the surface temperature of the roof assemblies which provides the required temperature gradient for heat flow, and the other is the mean operating temperature of the roof assembly, which has direct implications on the thermal performance of the insulation. An in situ field study was conducted in collaboration with Alberta Roofing Contractors Association (ARCA) on their headquarters building located in Calgary, to generate data on the mean operating temperature of the roof assemblies and to determine whether the thermal design of roofing assemblies using conventional methods is an accurate portrayal of in-service thermal performance. For the present study, two roof assemblies insulated with polyisocyanurate insulation, one with a white reflective roof membrane and the other with the black membrane were selected and instrumented. During the monitoring period, the mean operating temperature (MOT) of the roof assembly whether it is calculated as the average of interior and exterior ambient (MOTAIR,) or the average of surface temperatures (MOTSurface), was found to be below 24 °C (75 °F), which opposes the current roof thermal designs that are being designed using label R-value (thermal resistance) of the insulation reported at the mean temperature of 24 °C (75 °F) rather than temperature-dependent thermal resistance. The comparison of two energy transfer theoretical models, QConvention and QMOT, with the measured data indicated that the conventional approach of roof thermal design underestimates the energy performance of the roof assembly on average by 30%. The use of roof surface temperatures and the corresponding temperature-dependent thermal resistance of the insulation as in QMOT has been demonstrated to improve predictions of the energy performance. In addition the loss in thermal resistance due to blowing agent diffusion in polyisocyanurate was evaluated after two years of in situ installation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".