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Record W4390615389 · doi:10.1080/21650373.2023.2299365

Development and characterization of volume-stabilized grouts used for borehole heat exchangers

2024· article· en· W4390615389 on OpenAlexafffund
Jian Zhao, Guangping Huang, Rajender Gupta, Wei Victor Liu

Bibliographic record

VenueJournal of Sustainable Cement-Based Materials · 2024
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGypsumCementMaterials scienceGroutCompressive strengthComposite materialHeat exchangerThermal stabilityTernary operationPortland cementBoreholeVolume (thermodynamics)MineralogyGeotechnical engineeringGeologyChemistry

Abstract

fetched live from OpenAlex

This study aims to develop volume-stabilized grouts made of ordinary Portland cement (OPC)-calcium sulfoaluminate (CSA) cement-calcium sulfate (CS) ternary systems for borehole heat exchanger (BHE) applications. A series of experimental tests were carried out to characterize their properties and evaluate the applicability of grouts used in BHE. The grouts made of ternary systems exhibited satisfactory flowability within four hours and excellent volumetric stability compared with the grouts made of OPC only. Moreover, the thermal conductivities of grouts composed of ternary systems reached a range of 0.582–0.628 W/mK under oven-dry conditions and 1.940–1.949 W/mK under saturated conditions. In addition, grouts exhibited high unconfined compressive strength within a range of 14.19-16.31 MPa, indicating that the grouts have sufficient capacity to maintain the stability of boreholes. Overall, the grouts made of ternary systems exhibited good flowability, remarkable volumetric stability, and high thermal conductivity under saturated conditions. Among them, the grout composed of 70% OPC, 15% CSA cement and 15% gypsum showed superior volumetric stability and satisfactory performance in other aspects, indicating that it has the potential for BHE applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.515

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 science0.0000.000
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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 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

Citations5
Published2024
Admission routes2
Has abstractyes

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