Thermally Activated Walls for Reducing Energy Consumption of Cold-Climate Buildings
Bibliographic record
Abstract
The global increase in energy usage and greenhouse gas (GHG) emissions is largely due to the ascending trend of energy consumption in buildings. To address the negative impacts of this trend, designing energy-efficient buildings is crucial. As a potential solution, thermal energy storage (TES) systems, specifically using active TES in buildings’ mass have been proposed. This thesis focuses on reducing thermal loads (i.e., space cooling and heating loads) in cold-climate buildings by investigating the implementation of two methods of active TES in walls: the use of domestic cold water (DCW) for space cooling and ventilated concrete block wall (VBW) with supply air to zone (SAZ). DCW can be circulated through thermally massive walls before regular household consumption (e.g., shower) (herein “DCW-wall”) to provide free cooling without wasting DCW. The study evaluated the cooling potentials of DCW-wall system through 3D transient thermal simulations and revealed that the system is effective in providing cooling energy to the zone. With low inlet DCW temperatures, the system was able to deliver a significant amount of cooling energy per day, which could contribute to a substantial portion of the annual energy demand for space cooling in cities with cold climates like Toronto. In VBW system air is circulated between a zone and the voided cores of a VBW, where the air exchanges heat with the wall before returning to the zone. To evaluate the system's performance, typical-day and annual energy analyses were conducted under various boundary conditions and air circulation speeds. The study found that a VBW with a 2 m/s air circulation speed throughout the day can lead to 67% more thermal energy storage when compared to having no air circulation. The annual analysis compared the energy performance between a VBW and a traditional wood-frame wall in different cold climates. In addition, an annual energy analysis showed that substituting a traditional wood-frame wall with a VBW can yield a total assisting heating and cooling of 35 kWh/m2 (wall area) for Edmonton, Canada throughout the year. Overall, this thesis presents two methods that can potentially reduce space thermal loads in cold-climate buildings through active TES solutions in wall system. The results of this research can provide valuable insights for building design and energy management in order to create more energy-efficient 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".