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Record W4389541192 · doi:10.17118/11143/20838

Feasibility and parametric studies of the performance on ground thermalstorage in diurnal operation as cold storage

2023· article· en· W4389541192 on OpenAlexafffund
Lam Dang, Wey H. Leong, Alan S. Fung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCold storageThermal energy storageParametric statisticsThermalEnvironmental scienceNuclear engineeringComputer scienceEngineeringMeteorologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

In an effort to reduce global warming, there is a crucial need to develop a system that can cool buildings efficiently and environmental-friendlily.As a result, a ground thermal storage (GTS) was designed to use a chiller or groundsource heat pump (GSHP) to cool the ground (from 7 pm to 7 am of the next day) as a cold storage and then directly use it to cool buildings (from 7 am to 7 pm).Parametric simulations were performed using TRNSYS to better design the GTS.It was found that closer borehole spacing was better for diurnal cold storage purposes.The effect of pre-cooling period starts to level off after about 3 weeks of 12-hour night-time ground cooling before the start of cyclic building cooling and ground cooling.Other parametric investigations included various ground moisture contents, flow rates, borehole depths, and thermal conductivities of the borehole grout.The outlet fluid temperature from the borehole and borehole heat transfer were examined.

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)0.000
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.053
GPT teacher head0.284
Teacher spread0.230 · 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 routes2
Has abstractyes

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