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Record W3199067293 · doi:10.3390/pr9091636

The Long-Term Mitigating Effect of Horizontal Ground-Source Heat Exchangers on Permafrost Thaw Settlement

2021· article· en· W3199067293 on OpenAlexafffund
Amir Fatollahzadeh Gheisari, Pooneh Maghoul, Hartmut Holländer, Rob Kenyon, Rob Sinclair, Maryam Saaly

Bibliographic record

VenueProcesses · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsKGS Group (Canada)Polytechnique MontréalUniversity of Manitoba
FundersMitacs
KeywordsPermafrostSettlement (finance)Heat exchangerGeotechnical engineeringEnvironmental scienceMultiphysicsInletFinite element methodGeologyEngineeringStructural engineeringGeomorphologyMechanical engineering

Abstract

fetched live from OpenAlex

This study investigated the long-term effect of horizontal Ground-Source Heat Exchangers (GSHEs) on mitigating permafrost thaw settlement. In the conceptual system, a fan coil was used to chill the recirculating fluid in the linear High-Density Polyethylene (HDPE) ground loop system. A fully coupled thermo-hydro-mechanical finite element framework was employed to analyze multiphysics processes involved in the thaw settlement phenomenon. To investigate the sustainability of the system, a period of 50 years was simulated. Two operational modes were defined: one without and the other with HDPE. Different heat carrier velocities and inlet temperatures, and heat exchanger depths were examined to explore their effects on the thaw settlement rate. It was concluded that the proposed system can effectively alleviate the predicted permafrost thaw settlement over the study period. Moreover, the heat carrier temperature was found to have a prominent impact on the thaw settlement rate amongst other parameters.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2021
Admission routes2
Has abstractyes

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