Enhancing latent heat storage systems: The impact of PCM volumetric ratios on energy storage rates with auxiliary fluid assistance
Bibliographic record
Abstract
The present study investigates the effect of different volume ratios of PCM on the melting process and energy storage in the improved thermal energy storage (TES) system using auxiliary fluid. The purpose of using the auxiliary fluid is to benefit from the density difference between the auxiliary fluid and the PCM, which improves the convection heat transfer in the auxiliary fluid and increases the melting speed of the PCM. The auxiliary fluid, which has a higher density, is placed on the solid PCM at the beginning of the melting process and takes the place of the melted PCM during the melting process. This displacement causes better heat transfer between auxiliary fluid, PCM, and hot wall. Five different PCM/auxiliary fluid volume ratios are studied, including 30, 40, 50, 60, and 70% of PCM. The rate of energy storage in the system increases to 0.341 kW/kg, the highest rate of energy stored in the system and in PCM, corresponding to a volume ratio of 30% of PCM. Although the total energy stored in the system increases with an increase in the PCM volume ratio, the energy storage rate in the system increases with a decrease in the PCM volume ratio. Therefore, the optimal use of the most appropriate volume ratio of PCM is of great importance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".