Hemp-Lime Composite Integration with Phase Change Materials and their Application in Cold Climates
Bibliographic record
Abstract
This research explores developing, characterizing, and analyzing a new low-carbon composite material combining hemp-lime (hempcrete) and phase change materials (PCMs) for enhanced thermal performance in buildings. The study is divided into four main phases. In the first phase, novel hemp-lime composites were created using recycled, locally sourced, low-embodied energy binders and pozzolans. These composites were experimentally characterized for mechanical, thermal, and moisture buffering properties. Results showed that density affects hempcrete's properties and locally sourced pozzolans like metakaolin and recycled brick performed better than traditional hydraulic lime. The second phase included developing new hemp-lime composites with metakaolin and microencapsulated PCMs (MPCM). Numerical simulations compared the energy performance of timber-frame walls with hempcrete and HPCMs. The inclusion of MPCMs enhanced the heat storage potential of the composites. Consequently, in the third phase, a novel hysteresis modelling approach was proposed to improve the accuracy of phase change simulations. The new model was validated experimentally and compared with existing hysteresis methods. The study highlighted the importance of selecting appropriate hysteresis models and PCM integration techniques. Finally, numerical simulations investigated the effect of different heating schedules on hempcrete-PCM wall assemblies. The scenarios tested included heating setback temperature and temperature ramp-up. Results indicated that changing setpoints significantly influences PCM behaviour and wall thermal performance. Overall, this research demonstrates the potential of PCM-enhanced hemp-lime composites as sustainable building materials with improved thermal mass capacity suitable for cold climates like Canada.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".