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Record W4255575949 · doi:10.32920/ryerson.14647128.v1

Underground Energy Storage Utilizing Concrete Building Foundation: Experimental and Numerical Approach

2021· preprint· en· W4255575949 on OpenAlexafffund
Magdy M. Mousa, Ayman M. Bayomy, Julie Wang, Seth B. Dworkin, M. Z. Saghir

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation
KeywordsBoreholePileThermal energy storageFoundation (evidence)Laminar flowParametric statisticsThermalDrillingEnvironmental scienceFlow (mathematics)Volumetric flow rateGeotechnical engineeringMechanicsNuclear engineeringMechanical engineeringEngineeringMeteorologyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Space heating and cooling represents 63% of total building energy demand. In the present study, the concept of concrete foundation piles was used as an underground storage medium. This system requires no additional drilling costs or space, unlike conventional boreholes. A lab-scaled experiment facility was designed to experimentally investigate the thermal response of a concrete pile during the charging and discharging processes. The amount of energy stored and released during each process was evaluated. A flow rate parametric study was also conducted to explore the effect of the laminar and turbulent flow behaviour. In order to complement the experimental study, an extensive CFD model was developed and compared with the experimental data. There was good agreement between the numerical and experimental results for each process at different flow rates. The results revealed that increasing the flow rate increases not only the heat rejection and extraction but also the storage efficiency. Keywords: borehole, energy pile, thermal storage, thermal response, ground source heat pump

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.032
GPT teacher head0.273
Teacher spread0.240 · 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 designTheoretical or conceptual
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
Published2021
Admission routes2
Has abstractyes

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