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Record W4320894758 · doi:10.1139/cgj-2022-0518

Numerical investigation on the thermo-mechanical behaviour of twin energy tunnels

2023· article· en· W4320894758 on OpenAlexvenueno aff
Jinquan Liu, Chao Zhou

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)Displacement (psychology)Work (physics)PhysicsGeologyGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

Energy tunnel provides an innovative and efficient approach to harvesting geothermal energy. Some investigations on a single energy tunnel have been reported in the literature. This study investigated the behaviour of twin energy tunnels in two configurations (i.e., parallel and perpendicularly crossing) by using a thermo-hydro-mechanical coupled numerical model developed and validated in previous work. The responses of ground and a tunnel (denoted by ET1) to three thermo-activation modes were determined: cooling ET1 itself alone (ET1 active ), cooling a nearby tunnel alone (ET2 active ), cooling both tunnels simultaneously ((ET1 + ET2) active ). The computed results reveal that the interaction between two tunnels becomes negligible when the clear distance reaches four times the tunnel diameter. When the clear distance is smaller, the interaction could impact the tunnel and soil displacements. In ET1 active and (ET1 + ET2) active , the vertical responses (i.e., ground settlement, ET1 settlement and vertical convergence) in both tunnel configurations can be close to or even exceed the allowable values in tunnel management guidelines, depending on ground and tunnel conditions. The horizontal responses of ET1 (i.e., horizontal displacement and convergence) are always insignificant. The thermo-activation of ET2 alone has minor effects on ET1, except a strong impact on ET1 settlement in the case of crossing tunnels.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.226
Teacher spread0.197 · 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 designTheoretical or conceptual
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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207