MétaCan
Menu
Back to cohort
Record W4401862289 · doi:10.23977/jemm.2024.090208

Analysis of influence of running time on heat transfer efficiency of buried tube heat exchanger under different working conditions

2024· article· en· W4401862289 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat exchangerTube (container)Heat transferMechanicsConcentric tube heat exchangerMaterials scienceShell and tube heat exchangerEnvironmental scienceMechanical engineeringNuclear engineeringEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

There are still some problems with the actual heat transfer in ground-source heat pump systems. In order to study the influence of different working conditions on the heat exchange efficiency of the buried tube heat exchanger, the temperature field around the buried tube heat exchanger is simulated by using fluent software. The results show that the closer the temperature field is to the tube wall, the greater the temperature change is, and it has no effect on the soil layer with larger radial distance. In summer condition, soil temperature and heat transfer increase with increasing inlet velocity of working medium. When different backfillers are used, the backfillers with relatively large thermal conductivity can improve the heat transfer and improve the heat transfer efficiency. By analyzing the influence of different working conditions on the heat transfer efficiency of the buried pipe heat exchanger, the research results can provide reference value for the practical design of buried pipe and the future heat transfer simulation optimization of the heat pump system.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.204 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Engineering Mechanics and MachinerySame topicHeat Transfer and OptimizationFrench-language works237,207