Heat transfer enhancement to promote melting and solidification in a PCM-based thermal storage module
Bibliographic record
Abstract
Latent heat thermal energy storage systems can capture and store solar thermal energy, eliminating the misalignment between when solar energy is available and when heating is required, thereby enhancing the reliability of solar energy by facilitating a continuous and consistent energy supply. However, phase change materials used in these systems generally suffer from low thermal conductivities making it difficult to achieve charging and discharging rates suitable for application. The present research investigated the design of a rectangular four-pass thermal storage module by considering three heat transfer enhancement elements: steel wool, aluminum wool and an aluminum honeycomb structure. Each were incorporated into the thermal storage module to analyze their effect on charging and discharging rates as compared to the same four-pass module with no enhancement elements. The findings showed that steel wool performed the most poorly of the elements tested as it lengthened charging times by 60 % (Improvement factor 0.61) and shortened discharging times by only 30 % (Improvement factor 1.28). The highly conductive aluminum wool shortened the charging time by a factor of 1.62 and the discharging time by a factor of 2.33. However, the aluminum honeycomb structure was superior in enhancing the overall module performance for both charging (improvement factor of 3.11) and discharging (Improvement factor of 3.88) as it enhanced thermal penetration into the PCM region away from the HTF tubes while still permitting natural convection within the narrow, vertically oriented cells. The results presented in this study contribute to a deeper understanding of the optimal design for thermal storage modules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".