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Record W2133107605 · doi:10.1109/ccece.2007.40

Advanced Thermal Field Sensitivity Analysis of Power Cables

2007· article· en· W2133107605 on OpenAlexaff
M. S. Al-Saud, M. A. El-Kady, R.D. Findlay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAmpacityFinite element methodSensitivity (control systems)ThermalPower cableBoundary value problemPower (physics)Field (mathematics)Thermal analysisMaterials scienceEngineeringMechanical engineeringStructural engineeringComputer scienceElectronic engineeringElectrical engineeringLayer (electronics)Electrical conductorMathematicsPhysicsComposite material

Abstract

fetched live from OpenAlex

An advanced finite-element based technique is described in this paper for accurate sensitivity evaluation of power cables. The technique is applied to a practical three 132 kV XLPE underground cable system in the Saudi power system with multi-layer non-homogenous soil. The perturbed finite element sensitivity evaluation model accommodates most of the thermal parameters and the boundary conditions. In this work, the effect of multilayered thermal conductivities and boundary parameters variations on the actual cable system ampacity is investigated. Also, a comparison is held of the obtained results with the conventional finite element approach in order to show the applicability and usefulness of the developed methodology for the purpose of exploiting the operating parameter variations effects in straight sensitivity manner without repeating the thermal field analysis for such parameter changes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.215
Teacher spread0.212 · 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 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

Citations3
Published2007
Admission routes1
Has abstractyes

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