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Based on thermal and solid coupling topology reliability sensitivity analysis of wind turbine transmission system

2025· article· W7131211529 on OpenAlexaff
Ying Yuan, Zhigang Wang, Peng Li, Xin Guan

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutions123 Certification (Canada)
Fundersnot available
KeywordsTurbineReliability (semiconductor)Transmission (telecommunications)Transmission systemThermalWind powerCoupling (piping)Sensitivity (control systems)

Abstract

fetched live from OpenAlex

When transmission system of MW doubly-fed wind turbine runs in process of actual operation, gear transmission pair are effected by wind load in the running environment and appear fatigue crack, tooth root fracture etc. as mechanical failure. The main reason is that natural frequency of transmission system changes with change of operating conditions, then system produce resonance. Conventional reliability research on transmission system of wind turbine, only analysis and calculation base on structure and material itself on running environment but not take thermal stress change of gears into account. In the paper base on coupling analysis theory of thermal and solid, introducing method of topology analysis, reestablishing analysis control equation of thermal and solid coupling, and studying on natural frequency changes of thermal stress characteristic in wind turbine gear transmission system with method of probability analysis, which put forward research design method of wind turbine optimization and transmission system reliability.

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)0.002
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.005
GPT teacher head0.227
Teacher spread0.221 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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