Enhancing building energy efficiency: Innovations in glazing systems utilizing solid-solid phase change materials
Bibliographic record
Abstract
This study investigates the energy efficiency of a double-glazing window (DGW) integrating a solid–solid phase change material (SSPCM) with limited thickness, applied to the inner glass pane within the air gap. Numerical model, validated against experimental data, is developed using a finite volume method in ANSYS Fluent. In this model, the Discrete Ordinates (DO) model is applied to simulate radiation, while the enthalpy-porosity approach is used to capture the solidification and melting processes in the phase change material . With this model, the energy performance of the system is analyzed under various transient temperature values (10 to 30 °C) and ranges (1 to 5 °C) during the coldest and hottest days of the year, as well as during cloudy and sunny days in Montreal (Dfb), Vancouver (Cfb), and Miami (Aw). According to the obtained results in Montreal, the DGW-SSPCM system consistently saves energy under summer sunny conditions, with optimal performance when the SSPCM remains transparent. However, it incurs energy losses in cloudy days, where the energy lost is 2.3 times greater than the energy saved in sunny days. In Vancouver, the system shows consistent energy savings, particularly at T c = 30 °C, with average savings of 20.5 kJ (23 %) under summer sunny conditions. The system is most beneficial in Vancouver, where winter energy savings in cloudy days (50.6 kJ) are 7.1 times greater than the losses in sunny days (7.1 kJ). In Miami, the system results in energy losses by 60 % and 5 % (at T c = 30 °C) under both summer sunny and cloudy conditions, respectively, indicating unsuitability for its climate. During winter sunny conditions, all three cities experience energy losses, with Vancouver showing the lowest of 7.1 kJ (3 %) and Montreal the highest of 64.4 kJ (19 %) at T c = 30 °C. In winter cloudy conditions, the system saves energy in all cities, with the highest savings in Miami of 54.5 kJ (26 %) at T c = 30 °C. Overall, the SSPCM-DGW system has proven to be beneficial in Vancouver across various conditions in terms of energy and visual performance. These findings highlight the necessity of considering localized climate factors when designing and implementing energy-efficient glazing systems. Finally, the SSPCM-DGW system has provided complete visual clarity during office hours, making it more suitable for commercial buildings.
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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 | 0.000 |
| 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) | 0.001 | 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".