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Record W2007667574 · doi:10.1115/detc2010-28730

A Study of the Effect of Geometry Changes on the Structural Stiffness of a Composite D-Spar

2010· article· en· W2007667574 on OpenAlexaff
Swaroop B Visweswaraiah, Damiano Pasini, Larry Lessard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsSparStiffnessStructural engineeringIsotropyAirfoilBending stiffnessBeam (structure)GeometryStiffness matrixMaterials scienceMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The paper examines the impact of varying two geometric cross-section parameters of an advance composite D-spar on its structural stiffness. For a given blade topology, the orientation of the D-spar web with respect to the beam axis and the distance of the D-spar web from the leading edge of the blade have been selected here as the variables of study, as they govern the elastic properties of the composite cross-section. A code has been developed to calculate the matrix terms of the Euler-Bernoulli cross-sectional stiffness utilizing the closed form expressions of the structural properties formulated by assuming both Thin-Walled composite Beam theory (TWB) and Classical Laminate Theory. The code has been validated through the Variational Asymptotic Beam Sectional analysis (VABS) for the cross-sectional stiffness matrix. Two cases have been studied for a quasi-isotropic laminate D-spar. The first is for a symmetric airfoil, whereas the second is for an unsymmetrical airfoil. The variation of the stiffness parameters for the quasi-isotropic D-spar including the coupling parameters has been visualized into parametric maps. The paper also examines the impact that these geometric variables have on the stiffness-to-mass ratio to show that along with the ply orientations they play a major role in the aeroelastic tailoring and structural optimization of a composite 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.184

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 head0.004
GPT teacher head0.206
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2010
Admission routes1
Has abstractyes

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