Domestic Hot Water Storage Tank Utilizing Phase Change Materials (PCMs): Numerical Approach
Bibliographic record
Abstract
Thermal energy storage (TES) is an essential part of a solar thermal/hot water system. It was shown that TES significantly enhances the efficiency and cost effectiveness of solar thermal systems by fulfilling the gap/mismatch between the solar radiation supply during the day and peak demand/load when sun is not available. In the present paper, a three-dimensional numerical model of a water-based thermal storage tank to provide domestic hot water demand is conducted. Phase change material (PCM) was used in the tank as a thermal storage medium and was connected to a photovoltaic thermal collector. The present paper shows the effectiveness of utilizing PCMs in a commercial 30-gallon domestic hot water tank used in buildings. The storage efficiency and the outlet water temperature were predicted to evaluate the storage system performance for different charging flow rates and different numbers of families demands. The results revealed that increases in the hot water supply coming from the solar collector caused increases in the outlet water temperature during the discharge period for one family demand. In such a case, it was observed that the storage efficiency was relatively low. Due to low demand (only one family), the PCMs were not completely crystallized at the end of the discharge period. The results showed that the increases in the family’s demand improve the thermal storage efficiency due to the increases in the portion of the energy that is recovered during the nighttime.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".