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Record W2315511163 · doi:10.1061/9780784412121.451

Efficiency of a Community-Scale Borehole Thermal Energy Storage Technique for Solar Thermal Energy

2012· article· en· W2315511163 on OpenAlexaboutno aff
Ronglei Zhang, Ning Lu, Yu‐Shu Wu

Bibliographic record

VenueGeoCongress 2012 · 2012
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
FundersNational Renewable Energy Laboratory
KeywordsBoreholeThermal energy storageEnvironmental scienceThermalSolar energyThermal energyHeat transferNuclear engineeringMeteorologyGeologyThermodynamicsEngineeringGeotechnical engineeringElectrical engineeringGeographyPhysics

Abstract

fetched live from OpenAlex

Solar thermal has been quite efficient in harvesting solar energy, but has not been used widely at the community-scale as thermal energy is difficult to store. Borehole thermal energy storage (BTES) has been recently researched by several countries for its suitability in storing excess heat generated from solar thermal panels during the summer times. The first community-scale BTES system in North America was installed in the town of Okotoke, Alberta, Canada in 2006 in order to supply partial winter heating energy for 52 residual houses. To better understand the working principles of BTES and to improve BTES performance for future applications at larger scales, a three-dimensional heat transfer model is established, using the 5- year in-situ monitoring data. The model realistically imposes the time-dependent heat injection and withdrawals rates measured at the site. A total of 10 continuous years of annual cycle are simulated. The modeling results are compared with the measured temperature data over the simulation times and space. The time-dependent temperature distributions within the borehole region agree well with the measured temperature profiles. The predicted energy recovery efficiency approaches to 27% after 10 years, which also compares well with the current year efficiency of 25% at the site.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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.

Study designBench or experimental
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

Citations19
Published2012
Admission routes1
Has abstractyes

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