MétaCan
Menu
Back to cohort
Record W4411782189 · doi:10.1080/23744731.2025.2523200

Ground heat exchanger sizing using borehole outlet transfer function

2025· article· en· W4411782189 on OpenAlexafffund
Gabriel Dion, Philippe Pasquier

Bibliographic record

VenueScience and Technology for the Built Environment · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSizingBoreholeHeat exchangerEnvironmental scienceHeat transferNuclear engineeringPetroleum engineeringMechanical engineeringEngineeringMechanicsGeotechnical engineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

Sizing equations offer an efficient method for sizing ground heat exchangers, and they generally rely on the finite line source model to construct g-functions that represent ground temperature response. However, g-functions describe temperature at the borehole wall, excluding thermal exchange effects inside the borehole. This study employs transfer functions defined at the borehole outlet of a ground heat exchanger as an alternative ground response model, thus incorporating all borehole components and thermal exchange processes. Three new sizing equations for different heat pulse definitions using transfer functions are introduced, as well as a minimization algorithm that enables iterative optimization of the borehole length considering heat pump operational constraints. Key results demonstrate that the newly developed approaches lead to smaller borehole lengths, with an average reduction of 6% when compared to established methods. Furthermore, numerical verification of the proposed transfer functions using a 3D numerical model is provided, as well as a comparative analysis of temperature predictions from transfer functions and g-functions. The analysis demonstrates that the proposed sizing approaches are robust alternatives to ones using g-functions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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 designNot applicable
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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueScience and Technology for the Built EnvironmentSame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207