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Record W1966975713 · doi:10.1109/epec.2011.6070244

A lagrangean interactive interface to evaluate ice accretion modeling on a cylinder - a test case for icing modeling on wind turbine airfoils

2011· article· en· W1966975713 on OpenAlexaff
Fahed Martini, Drishty Ramdenee, Hussein Ibrahim, Adrian Ilinca

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsIcing conditionsIcingTurbineContext (archaeology)Wind powerMarine engineeringComputer scienceEnvironmental scienceInterface (matter)SimulationMeteorologyAerospace engineeringEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

In the context of global campaign to mitigate climate change effects, researches on wind turbines industry have known a constant growth during the last few years. Wind velocity is so important for wind turbine productivity and very significant wind resources are available in cold regions where icing effects are also significant. Ice accretion increases the risks of unbalanced mass resulting in losses of annual power production, in vibration problems and in security risks. This coincides with the most abundant days of wind in the year. At the WERL (Wind Energy Research Laboratory), in collaboration with the TechnoCentre Eolien and the AMIL (Anti-icing Materials International Laboratory), numerous studies have been conducted to avoid this problem. In this paper we made use of these studies to simulate a test case of icing around a cylinder as described in Lozowski et al. [1]. We emphasize on the fact that these studies cannot provide very accurate local results due to numerous simplifications. We, therefore, developed at the WERL an interactive interface to simulate the trajectory of water droplets in an airstream until collision on a cylinder. This interface is based on MS-Excel worksheets supported with VBA code (Visual Basic for Applications) using a fourth order Runge-Kutta resolution scheme. The interface provides flexibility to demonstrate various scenarios that can help to validate the subsequent evaluation of collection efficiency based on multiphase CFX simulation.

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 categoriesMeta-epidemiology (narrow)
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 score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.070
GPT teacher head0.296
Teacher spread0.225 · 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.

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

Citations4
Published2011
Admission routes1
Has abstractyes

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