Phase change materials effect on the thermal radius and energy storage capacity of energy piles: Experimental and numerical study
Bibliographic record
Abstract
Geothermal energy is a renewable energy source whose use has been increased dramatically. It is primarily harvested by employing ground source heat pump (GSHP) technology due to its high coefficient of performance (COP) and low greenhouse gas emissions. This study aims to present a potential solution to the challenges preventing a higher adoption rate for ground source heat pump technology using building foundation piles as a ground heat exchanger (GHE) and implementing phase change materials (PCM) containers into the concrete shell. The study was conducted experimentally using two lab-scaled foundation piles (with and without PCM), with 3 layers of insulation. CFD numerical model was developed and validated against the experimental data. The modified model, by replacing the three insulations layers with a sand layer, was used to study the effect of different operating conditions on the heat storage capacity. Results revealed that implementing the PCM containers increased the energy storage from 16.4 to 48.2 kJ/kg (in the case of PCM 2), while the temperature distribution was always lower during the charging, due to the smaller thermal radius of the piles. By increasing the flow rate from the laminar regions to the turbulent regions, the storage capacity was increased by 10%. the study recommended using a turbulent flow inside the GHE and selecting the PCM melting temperature according to the time at which the peak load occurs.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".