Thermal management of shelter building walls by PCM macro-encapsulation in commercial hollow bricks
Bibliographic record
Abstract
Maintaining building indoor temperature within comfort zone has been identified as one of the major reasons for energy consumption. The incorporation of phase change materials (PCMs) in various regions in buildings is introduced as a solution that can significantly reduce energy consumption. In the current research, a three-dimensional computational fluid dynamics approach is taken to evaluate the influence of incorporating three PCMs from the Rubitherm® RT line (RT15, RT18, and RT22) in two widespread commercial hollow brick types in Iran for keeping the indoor environment of a shelter warm in a cold climate. These enhanced bricks are compared with their solid and hollow counterparts. Furthermore, efforts were made to examine various configurations of these PCMs, with melting temperatures in obedience or contrast with the thermal stratification of the bricks. Several combinations of the PCM with air layers are studied to assess the best way to place the air and PCM layers. It was found that the RT18 PCM-filled brick can present lower heat fluxes than the hollow one for over 42 h. Also, the findings showed that RT22 PCM-filled brick can provide 66.79% lower average heat flux compared to the hollow brick for the first 24 h of the process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".