Thermal and energy performance assessment of multi-layer façade systems with latent heat storage materials: an analysis of climatic conditions in Montreal and Brisbane
Bibliographic record
Abstract
This study explores the energy efficiency of smart-glazing technologies that incorporate a thin layer of solid-solid phase change material into multi-layer fenestration systems. The research involved performing numerical simulations using the finite volume method within computational fluid dynamics, with results validated against experimental data. The analysis examined the system's energy performance across a range of transient temperatures and weather conditions, including the coldest and hottest days of the year, as well as sunny and cloudy periods. This evaluation was carried out for Montreal (Quebec, Canada) and Brisbane (Queensland, Australia), which are categorized under the Köppen-Geiger climate codes Dfb and Cfa, respectively. The study highlights how the climatic conditions and transient temperatures of the phase change material affect the system's transparency fraction and energy savings. It also reveals significant differences in energy savings between the two cities, underscoring the impact of local climate conditions. Overall, the findings indicate that the improvement in energy performance provided by the smart-glazing system is substantially influenced by the climatic factors and parameters analyzed in this research.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".