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Record W3090871492 · doi:10.1139/cgj-2020-0118

Geotechnical considerations in the design of borehole heat exchangers

2020· article· en· W3090871492 on OpenAlexvenueno aff
Despina M. Zymnis, Andrew J. Whittle

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
FundersAlexander S. Onassis Public Benefit FoundationTsinghua University
KeywordsBoreholeParametric statisticsHeat exchangerGeotechnical engineeringSettlement (finance)EngineeringHeat pumpEnvironmental scienceCivil engineeringMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

The use of ground source heat pumps to transfer heat to and from the ground via borehole heat exchangers (BHEs) is among the most energy efficient techniques for space heating and cooling. Broader applications in urban environments require more careful evaluation of hydro-mechanical behavior of soil to ensure their reliable long-term performance and to minimize adverse effects on adjacent structures. An advanced soil model has been integrated within a finite difference (FD) framework, to solve coupled thermo-hydro-mechanical (THM) problems in an axisymmetric space. The FD simulator is applied to a prototype project and the ground conditions represent typical stress history profiles found in Geneva, Switzerland. An extensive parametric study is undertaken to study the long-term THM response of clay for a broad range of heat exchanger design parameters. The results show that significant settlements can be induced due to the long-term operation of BHEs (i.e., over periods from 10 to 50 years). The parametric study gives useful guidelines for heat exchanger design and identifies a design space of optimum solutions that meet pre-specified foundation settlement criteria. The study provides a qualitative investigation and a systematic framework for analyzing the long-term THM response of clay to seasonal heating and cooling.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.239
Teacher spread0.194 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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