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Record W4408651284 · doi:10.1016/j.est.2025.116164

Reference data set for injection and extraction cycle of a borehole thermal energy storage field: A numerical and experimental study

2025· article· en· W4408651284 on OpenAlexafffund
C. Millar, M.F. Lightstone, James S. Cotton

Bibliographic record

VenueJournal of Energy Storage · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsBoreholeExtraction (chemistry)Thermal energy storageThermalPetroleum engineeringEnvironmental scienceField (mathematics)Data setThermal energyGeologyComputer scienceChemistryGeotechnical engineeringMathematicsThermodynamicsPhysicsChromatographyArtificial intelligence

Abstract

fetched live from OpenAlex

Borehole thermal energy storage systems are utilized across the globe to capture solar or residual thermal energy and store the heat seasonally. Although many borehole thermal energy storage facilities exist and operate, there are few facilities that are equipped to provide data and be used for research purposes. This paper provides an extensive experimental dataset of the operation of a borehole thermal energy storage field. The operating conditions include a constant temperature heat injection cycle followed by a hold period and a stepped-constant temperature heat extraction cycle. The experiment was initiated on November 23, 2021, and concluded on January 18, 2023. It involved a total of 340 GJ of heat injection and 192 GJ of heat extracted. Measurements of soil temperature and fluid temperature are shown, along with mass flow rates and the calculated thermal power. A COMSOL Multiphysics model with simplifying assumptions was used to demonstrate the modelling capability of the code. The results show that the COMSOL model of the borehole field was able to capture the thermal response of heat injection and extraction cycles to within 4.5 % of the experimental results. • Experimental dataset of operational borehole thermal energy storage facility • Radial and axial soil temperatures, fluid temperature, and flow rate measurements • Validation of a simplified 3D numerical model • Thermal power comparison of experimental and numerical results

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 score0.668
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.029
GPT teacher head0.315
Teacher spread0.286 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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