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Record W1980819085 · doi:10.1049/iet-smt.2014.0112

Design of booster shed parameters for post insulators under heavy icing conditions using geometric modelling and the Taguchi method

2015· article· en· W1980819085 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Science Measurement & Technology · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaHydro-QuébecUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsTaguchi methodsBooster (rocketry)IcingEngineeringStructural engineeringMathematicsStatisticsAerospace engineeringPhysicsMeteorology

Abstract

fetched live from OpenAlex

A generic design approach is presented to optimise the parameters of booster sheds (BSs) installed on post station insulators in heavy icing conditions. This approach is based on geometric modelling and Taguchi method and it allows to improve the electrical performance of the insulators. It deals with geometrical parameters of BSs including their number, inclination angle and diameter. Numerical analyses using geometric modelling, MATLAB programming and Minitab software were carried out in this study. The presented approach seems to be a good alternative to experimental measurements which are costly and difficult to perform. This investigation shows that the optimised value of BS inclination angle is equal to the upper shed angle of the insulator. Moreover, it indicates that generally the maximum feasible values for the diameter and number of BSs are the best options.

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.

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)0.007
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.491
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.143
GPT teacher head0.320
Teacher spread0.177 · 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