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Record W3166596946 · doi:10.1109/tdei.2021.009475

Observation and Simulation for the Movement of Metallic Particles in Flowing Transformer Oil under AC/DC and Combined Voltages

2021· article· en· W3166596946 on OpenAlexfundno aff
Xinyu Luo, Cheng Pan, Yuhang Yao, Yumin Chen, Ju Tang

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaCanada School of Energy and Environment
KeywordsMechanicsVoltageElectrodeTransformer oilTransformerMaterials scienceElectric fieldVoltage dropDrop (telecommunication)Electrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

The movement of metallic particles in flowing transformer oil is observed under AC, DC and AC/DC combined voltages. It is found that the particles execute sedimentation followed by repeated up-down motions in the vertical direction, and meanwhile move along with the oil flow in the horizontal direction. The up-down motion is affected by applied voltage. With the application of AC voltage, the bounce height from the grounded electrode does not exceed the half of electrode separation. After the DC component is introduced, the particles can arrive at the high-voltage electrode. When the ratio of combined voltage between AC and DC components is 1:1, the particles drop slightly in the upward motion and rise slightly in the downward motion. As the ratio is reduced, no such slightly dropping or rising is observed. In order to analyze the experimental results, a simulation model about solid-liquid two-phase flow subjected to electric field is constructed. Simulated trajectories of particles are in good agreement with the experiments. Based on the model, the evolution of force condition exerted on a particle during its motion is analyzed, and the relationship between partial discharge (PD) frequency and particle movement is briefly discussed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

Explore more

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