Energy and Optical Assessment of Glazing Systems Equipped with Solid-Solid Phase Change Materials in Cold Climates
Bibliographic record
Abstract
This research explores the thermal and optical behavior system of a double-glazed window (DGW) integrated with a Solid-Solid Phase Change Material (SSPCM) layer, evaluated under the climate conditions of Montreal, Quebec, Canada. Transient numerical simulations are performed for representative extreme days for both the hottest and coldest days of the year under clear and overcast sky. A parametric study is carried out to assess how SSPCM behavior and window orientation (North, East, South, and West) affect the energy and daylighting performance of the system. The simulations account for Natural Convection (NC) within the air gap to model buoyancy-induced airflow and compare to none NC. Findings reveal that ignoring NC in winter can lead to an overestimation of heat transfer by approximately 12% to 19%. In contrast, the influence of NC in summer is negligible, enabling its exclusion to significantly reduce computational time without loss of result accuracy. In summer, the SSPCM undergoes full phase transitions in all orientations, remaining transparent throughout standard working hours; however, this optical shift does not translate into notable thermal improvement. On the other hand, Winter results show that the latent heat storage capacity of the SSPCM effectively limits indoor heat loss, leading to measurable energy savings. Among all orientations, the south-facing configuration achieves the highest performance, with an 8.2% reduction in heating energy demand and the longest period of visual transparency. The most effective solution identified is a south-facing DGW design with a 15 °C transition temperature SSPCM layer installed on the interior surface. This configuration offers a strong balance between improved thermal efficiency during the heating season and sustained daylight availability, making it a compelling choice for energy-conscious commercial buildings aiming to optimize year-round comfort and performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".