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Record W1990411312 · doi:10.1115/imece2010-37583

Analysis of Embedded Blade Root Carrot and T-Bolt Connections Subject to Cold Weather Conditions Using a Finite Element Model

2010· article· en· W1990411312 on OpenAlexafffundabout
Patricio Lillo, Curran Crawford

Bibliographic record

VenueVolume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and B · 2010
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaFederal Aviation Administration
KeywordsRoot (linguistics)Blade (archaeology)Finite element methodStructural engineeringTurbine bladeWork (physics)Scope (computer science)Boundary value problemThermalTurbineEnvironmental scienceEngineeringMechanical engineeringComputer scienceMeteorologyMathematics

Abstract

fetched live from OpenAlex

Canada has aggressive targets for introducing wind energy across the country, but also faces challenges in achieving these goals due to the harsh Canadian climate. One issue which has received little attention in other countries not experiencing these extremes is the behavior of the composite blades in winter conditions. The scope of this work is to determine and analyze the static stresses on the blade root during operational conditions at extreme cold temperatures. The paper analyses the stresses in the root of the composite blades, specifically two blade-hub connection methods: embedded root carrots and T-bolts. Finite element models of the root are proposed to properly simulate boundary conditions, applied loading and thermal stresses for a 1.5 MW wind turbine. Finally, it is shown that the blade root is strongly affected by the thermal stresses caused by the mismatch and orthotrophy of the coefficients of thermal expansion of the blade root constituents.

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 score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations2
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueVolume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and BSame topicStructural Load-Bearing AnalysisFrench-language works237,207