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Record W2360137601 · doi:10.1177/0309524x16645484

Modelling load and vibrations due to iced turbine operation

2016· article· en· W2360137601 on OpenAlexaff
Simo Rissanen, Ville Lehtomäki, Jani Wennerkoski, M. Wadham-Gagnon, Klaus Sandel

Bibliographic record

VenueWind Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsCentre Intégré de Santé et Services Sociaux de la Gaspésie
FundersTekes
KeywordsTurbineIcingAerodynamicsAirfoilRotor (electric)AeroelasticityMarine engineeringTurbine bladeEngineeringWind powerEnvironmental scienceStructural engineeringVibrationAerospace engineeringMechanical engineeringMeteorologyPhysicsAcousticsElectrical engineering

Abstract

fetched live from OpenAlex

Wind energy in icing and low-temperature climate has a huge growth potential, but rotor icing effects on turbine dynamics and lifetime are not well known and simulations with iced rotor are not required in current IEC 61400-1 turbine design standard. In this article, simulations with iced rotor are compared to measured mechanical loads. The dynamic behaviour of the wind turbine was simulated with FLEX5 aeroelastic code for Senvion MM92 2 MW wind turbine. Simulations with typical iced airfoil lift and drag coefficients, aerodynamic and mass imbalances for iced rotor were performed and compared to measured iced turbine loads. Resulting iced turbine simulation parameters can be used in defining new design load cases for cold climate turbines. The most representative simulation parameter combination was achieved with a symmetric aerodynamic penalty applied on all blades and an asymmetric rotor mass imbalance of 166 kg ice load on two blades and 83 kg ice load on one blade.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.179
Teacher spread0.170 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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