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Analysis of the transient performance of coaxial and u-tube borehole heat exchangers

2022· article· en· W4210775143 on OpenAlexaff
B.E. Harris, M.F. Lightstone, Stanley Reitsma, James S. Cotton

Bibliographic record

VenueGeothermics · 2022
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCoaxialHeat exchangerPipingTransient (computer programming)Tube (container)BoreholeMaterials scienceHeat transferThermalMechanicsMechanical engineeringEngineeringComposite materialThermodynamicsGeotechnical engineeringPhysicsComputer science

Abstract

fetched live from OpenAlex

While coaxial borehole heat exchangers often have lower borehole thermal resistances than u-tubes, studies in the literature have mixed results as to whether the coaxial design always outperforms the u-tube. This study contributes a systematic comparison between the two designs, accomplished using a custom numerical model in OpenFOAM that provides detailed predictions of the heat transfer within the ground heat exchangers . The modelling and subsequent analysis of system thermal resistances demonstrated that the u-tube and coaxial heat exchanger performances differed the most during the early, transient phase of operation. Furthermore, modelling showed that the coaxial design does not necessarily exceed the u-tube performance long term; testing of coaxial heat exchangers with polyethylene piping showed small differences in outlet temperature compared to the u-tube after 72 h. Additional testing showed that using a steel coaxial outer tube could provide a 22% improvement to performance over the u-tube design. These findings are used to compare predictions of thermal resistance with available analytical models. The impact of the results on the potential for length reductions is also discussed.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations37
Published2022
Admission routes1
Has abstractyes

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