Assessing the effect of night ventilation on PCM performance in high-rise residential buildings
Bibliographic record
Abstract
This article investigates the possibility to enhance the use of latent heat thermal energy storage (LHTES) as an energy retrofit measure by night ventilation strategies. For this scope, phase change materials (PCMs) are integrated into wall and ceiling surfaces of high-rise residential buildings with highly glazed facades that experience high indoor diurnal temperatures. In particular, this article investigates the effect of night ventilation on the performance of the PCMs, namely, the daily discharge of the thermal energy stored by PCMs. Following previous experimental tests that have shown the efficacy of LHTES in temperate climates, a system comprising two PCM layers with melting temperatures selected for a year-around LHTES was considered. To quantify the effectiveness of different night ventilation strategies to enhance the potential of this composite PCM system, simulations in EnergyPlus TM were performed. The ventilation flow rate, set point temperature, and operation period were the main tested parameters. The performance of the PCMs in relation to the variables was evaluated based on indoor operative temperature and cooling energy use variations in Toronto and New York in the summer. The solidification of the PCMs was analyzed based on the amount of night ventilation needed in each climate condition. The results quantify the positive impact of combining PCMs with night ventilation on cooling energy reductions and operative temperature regulation of the following days. In particular, the results indicate higher benefits obtainable with PCMs coupled with night ventilation in the context of Toronto, since this city experiences higher daily temperature fluctuations. The impact of night ventilation design variables on the solidification rate of the PCMs varied based on each parameter leading to different compromises based on the PCM and climate characteristics.
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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 | 0.001 |
| 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 | 0.000 | 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".