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Record W4388924020 · doi:10.31224/3360

Exploiting heat gains along horizontal connection pipes in existing borehole heat exchanger fields

2023· preprint· en· W4388924020 on OpenAlexaff
Stephan Dueber, Raúl Fuentes, Guillermo A. Narsilio

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsBoreholeConnection (principal bundle)Heat exchangerPlate fin heat exchangerGeologyPetroleum engineeringPlate heat exchangerEnvironmental scienceGeotechnical engineeringMechanicsMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This study investigates three options for utilising the additional capacity of borehole heat exchanger (BHE) fields through gains along horizontal connection pipes, whose contribution is routinely ignored.The analysis considers thermal load profiles with different heating to cooling ratios and finds that the effect of horizontal pipes becomes more significant with unbalanced loads.The study explores the potential of extended operation, increased loads and an optimised operating strategy to exploit the idle capacity gain from the horizontal connection pipes.It is shown that for the scenarios investigated, the BHE field operational time can be extended by more than 25 years without violating the critical fluid temperatures in the design phase.Alternatively, the thermal load can be increased by up to 26 %.In addition, the study highlights the potential of an optimised operating strategy involving adjustments to the number of BHEs operated to reduce power consumption and therefore reducing operating costs in existing systems by utilising heat gains from the horizontal connection pipes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.299
Teacher spread0.213 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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