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Record W2295451244 · doi:10.1109/cjece.2015.2496205

Dynamic Analysis of Core Temperature of Low-Voltage Power Cable Based on Thermal Conductivity

2016· article· en· W2295451244 on OpenAlexvenueno aff
Xiaokai Meng, Zhiqiang Wang, Guofeng Li

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsnot available
FundersFundamental Research Funds for the Central Universities
KeywordsPower cableThermal conductivityMaterials scienceCore (optical fiber)Finite element methodVoltageComposite materialConductivityPower (physics)ThermalNuclear engineeringMechanicsLayer (electronics)Structural engineeringElectrical engineeringEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

The core temperature measurement of 0.6/1-kV low-voltage ethylene propylene rubber-insulated single-core power cable based on thermal conductivity is proposed in this paper. First, the cable temperature is measured by considering a variety of environmental temperatures and load currents. Second, in order to improve the computational accuracy, a 3-D finite-element model is established and simulated using the ANSYS software. Finally, according to the difference between the experimental and simulation results, the thermal conductivity of the insulation layer and sheath layer is altered. After modification of the thermal conductivity, the temperature of the simulation can follow the metrical data better. Simulation results verify that the proposed method is able to estimate the core temperature with high sensitivity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.001
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 head0.003
GPT teacher head0.167
Teacher spread0.164 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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