Long-term soil thermal imbalance analysis of the energy pile considering ambient thermal boundary
Bibliographic record
Abstract
Long-term operation is crucial for energy piles, significantly affecting the pile thermal performance and soil thermal field. However, research on long-term behavior remains limited. This study conducted model tests with a scaled energy pile, involving 1 week of continuous thermal injection, followed by a validated 3D thermo-hydro-mechanical numerical model. The model simulated a 10-year operation under annual ambient temperature in Shanghai and typical building thermal loads. Test results showed ambient temperature significantly influenced the thermal field distribution, even in deeper soil layers within a short period. Long-term simulations revealed that constant or insulated soil surface conditions overestimated pile temperatures and caused excessive soil temperature rises. Soil thermal imbalance expanded from the pile vicinity to surrounding regions, with thermal accumulation shifting downward and forming a transition zone in the upper soil. Regions with soil temperatures exceeding the initial level were defined as the thermal impact zone, simplified as a circular area at the same depth. Over time, the thermal impact range stabilized annually and varied linearly with depth. However, the prolonged operation caused the range to progressively contract toward the pile, indicating depth-wise “shrinkage”. These observations suggest energy pile design can be optimized to mitigate thermal interference on adjacent piles.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".