Numerical study on the influence of embedded PCM tubes on the energy storage properties of the geothermal energy pile
Bibliographic record
Abstract
Geothermal energy pile is a remarkable alternative energy source that can provide heating and cooling energy to meet the energy demands in buildings. This study aims to quantify and expand the knowledge on the thermal storage performance of the geothermal pile system embedded with phase change material containers as compared to the one without, as a goal to expand the wide application of the system. In this study, numerical predictions were conducted on lab-scale energy pile systems, namely Design A (i.e., no PCM) and Design B (i.e., with PCM). The work aimed to investigate and quantify the energy storage properties of geothermal energy piles, and the influence of the incorporation of paraffin wax PCM tubes in the pile, at different flow rates. Incorporating PCM in a geothermal energy pile demonstrated considerable improvement in the amount of energy stored and extracted compared to the one with no PCM. For 735, 1470 and 2100 mL/min, Design B exhibited an increase in the energy stored by 42.21%, 44.71% and 46.23%, respectively compared to Design A. On the other hand, for the flow rate 735, 1470 and 2100 mL/min Design B illustrated an increase in the energy extracted by 45.87%, 53.09% and 59.55%, respectively compared to Design A. The use of PCM in the energy pile has a promising solution to enhance the storage capacity and improve the heat exchange capacity.
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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) | 0.000 | 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 | 0.001 | 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".