Optimization of building envelope latent heat storage integration based on internal surface solar insolation profiles
Bibliographic record
Abstract
Phase change materials (PCMs) utilize solar energy for latent heat storage (LHS), a method of storing thermal energy through a material’s solid to liquid phase change. When LHS systems are implemented in buildings the thermal energy stored and released during phase changes provides passive temperature regulation and reductions in total conditioning loads. Often, in PCM installations, the PCM is uniformly distributed across entire interior surfaces of a space disregarding the opportunity to optimize PCM placement based on daily and annual solar geometry trends. Strategic placement of PCM within interior surfaces can maximize solar energy storage while reducing material and implementation costs. The objective of this study was to determine ideal thermal storage placement within building spaces based on direct solar radiation rays. A MATLAB model which utilizes cartesian coordinates and solar ray tracing was developed to generate interior surface insolation exposure maps and was validated with ESP-r. The MATLAB model demonstrated a strong correlation with the insolation analysis tool used by ESP-r, thus validating the insolation exposure maps generated by the model. In general, it was found that for a small cubic space with a south facing window in the northern hemisphere, the floor received the largest portion of insolation exposure on an annual basis. Within this surface, northern and southern subsections received the largest portions of insolation exposure for typical heating and cooling seasons respectively, indicating the PCM placement could be tailored to subsections of the floor to optimize its performance during distinct conditioning periods.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".