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Record W2996078113 · doi:10.3390/en13010096

Long-Term Temperature Evaluation of a Ground-Coupled Heat Pump System Subject to Groundwater Flow

2019· article· en· W2996078113 on OpenAlexafffundabout
Nehed Jaziri, Jasmin Raymond, Nicoló Giordano, John Molson

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeat pumpGroundwaterGroundwater flowDewateringEnvironmental scienceAdvectionFlow conditionsBoreholeHeat transferFlow (mathematics)Hydrology (agriculture)Heat exchangerGeotechnical engineeringAquiferEngineeringMechanicsMechanical engineering

Abstract

fetched live from OpenAlex

The performance of ground-coupled heat pump systems (GCHPs) operating under significant groundwater flow can be difficult to predict due to advective heat transfer in the subsurface. This is the case of the Carignan-Salières elementary school located on the south shore of the St. Lawrence River near Montréal, Canada. The building is heated and cooled with a GCHP system including 31 boreholes subject to varying groundwater flow conditions due to the proximity of an active quarry being irregularly dewatered. A study with the objective of predicting the borehole temperatures in order to anticipate potential operational problems was conducted, which provided an opportunity to evaluate the impact of groundwater flow. For this purpose, a numerical model was calibrated using a full-scale heat injection test and then run under different scenarios for a period of twenty years. The heat exchange capacity of the GCHP system is clearly enhanced by advection when the Darcy flux changes from 6 × 10−8 m s−1 (no dewatering) to 8 × 10−7 m s−1 (high dewatering). This study further suggests that even the lowest groundwater flow condition can be beneficial to avoid a progressive cooling of the subsurface due to the unbalanced building loads, which can have important impacts for design of new systems.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.993

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 integrity0.0000.000
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations4
Published2019
Admission routes3
Has abstractyes

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