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Record W4413921870 · doi:10.1139/cgj-2025-0177

Long-term soil thermal imbalance analysis of the energy pile considering ambient thermal boundary

2025· article· en· W4413921870 on OpenAlexvenueno aff
Xi Wang, Shi‐Jin Feng, Hongxin Chen, Jincheng Fang

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
FundersShanghai Rising-Star ProgramNational Natural Science Foundation of China
KeywordsPileGeotechnical engineeringTerm (time)ThermalEnvironmental scienceGeologyPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.192
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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