Characterization of Building Integrated Phase Change Materials Using Experiments and Simulation
Bibliographic record
Abstract
Phase change materials (PCMs) are a latent thermal storage solution which passively store energy, such as solar energy, and delay its release into a space, reducing peak cooling loads in summer and prolonging passive heating in winter. PCMs are most effective when their melting temperatures are near the room temperature setpoint. In locations with two distinct conditioning seasons and thus two different setpoints throughout the year, two PCMs may be optimal for reducing both conditioning loads. However, research on these optimizations remains limited. The objective of this research was to expand the knowledge of buildings with PCM, such as two PCMs of different melting temperatures to optimize for two space conditioning seasons. This was achieved through thermophysical property experiments and single room modelling to predict trends in PCM performance, full-scale in-situ experiments, and full house modelling across Canada and the United States. For the in-situ experiments, two chambers with interior dimensions of 2.4 m by 2.4 m by 2.4 m were designed, constructed, and commissioned to experimentally compare a dual PCM wallboard to a reference-case room without PCM. The PCMs decreased the heating and cooling loads by 14.5% and 12.0%, respectively, from January until July 2023 in Ottawa, Canada. Full house modelling was conducted using EnergyPlus, and the time of use implications of PCMs were assessed both economically and in terms of the carbon emissions. The PCMs reduced space heating loads throughout the night, but due to the low space cooling load, the peak emissions and economic reductions were relatively small. The modelling analysis was expanded across Canada and the United States on four house archetypes to quantify the energy, emissions, and economics associated with PCM. In moderate and warm locations, greater space conditioning reductions occurred with a single PCM, whereas in highly heating dominant locations, two PCMs with different melting temperatures provided greater benefits. The emissions payback period was as low as 0.3 years, due to the reductions in space heating and cooling loads. The results suggest significant promise for PCMs in reducing the carbon footprint associated with building space conditioning, particularly for emissions intensive space conditioning systems.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".