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Record W2966320677 · doi:10.5539/mas.v13n8p76

The Heat-Dissipation and Dehumidification System of the Hybrid Multi-Layer Gravity Heat Pipe Applied to Granary

2019· article· en· W2966320677 on OpenAlexvenueno aff
Guoyong Su, Yu Wu, Wei Gao

Bibliographic record

VenueModern Applied Science · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHeat pipeStack (abstract data type)Materials scienceDissipationHeat transferMechanicsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Based on the basic working principle and heat transfer characteristics of gravity heat pipe in combination with the grain stack particle's stacking characteristics, this study changes the structure of traditional heat pipe to change the heat transfer mode between the grain stack and the gravity heat pipe so as to improve the grain's heat-dissipation rate and heat-dissipation efficiency. Generally, this system can satisfy the internal heat dissipation requirements of grain stack only under the action of a non-power fan driven by the air in the atmosphere and the temperature difference between inside and outside of the fan. When the internal temperature sensor of the grain stack detects that the internal temperature of the grain stack is high only under the action of the non-power fan, the pipeline booster fan will be started. At the same time, when the gas exchange occurs between the internal gas in grain stack and the external air, the dehumidification and drying of the grain stack can be realized through the gas drying device of the product. Through theory and simulation, this paper conducts a comparative analysis on the variation law of grain stack's temperatures under the action of gravity heat pipe and no gravity heat pipe so as to explore the heat-dissipation system's working efficiency of the new structure gravity heat pipe. The gravity heat pipe and the non-power fan in the system are all green products, which makes this design product have better heat-dissipation effect and less energy consumption.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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