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Record W3096138704 · doi:10.14447/jnmes.v23i3.a08

Preparation and Heat Transfer Performance of Steel Ball Phase Change Concrete

2020· article· en· W3096138704 on OpenAlexvenueno aff
Hong Chang, Lihan Jin

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
FundersPeople's Government of Jilin Province
KeywordsMaterials scienceCementitiousPhase-change materialHeat transferHeat exchangerComposite materialFly ashPileThermal energy storagePhase changePhase (matter)Volume (thermodynamics)Thermal conductivityCementStructural engineeringThermodynamicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Accompanied by a large amount of heat absorption and release during the phase change process, phase change concrete has the advantages of high energy storage density, low volume expansion ratio and approximate isotherm in the heat exchange. It is widely used in the building field. For this, using steel balls as the carrier material and butyl stearate as the phase change material (PCM), the authors combined the phase change energy storage material with the energy pile to prepare a new type of concrete energy pile enhanced with the PCMs. Then, the tests and numerical simulations were conducted to study the optimal mix ratio and thermal conductivity of the phase change concrete. The results show that adding steel balls (10% of the coarse aggregate volume), and slag and fly ash (5% of the cementitious material mass) to the ordinary concrete C30 can greatly improve the heat transfer efficiency of the energy pile. The research findings provide a guidance for engineering practice.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.519

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 communication0.0000.000
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 head0.054
GPT teacher head0.306
Teacher spread0.251 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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