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Record W6982183496

High-order Simple-input Methods for Thick Laminated Composite Straight and Curved Tubes

2016· dissertation· en· W6982183496 on OpenAlexfundno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2016
Typedissertation
Languageen
FieldArts and Humanities
TopicAncient Near East History
Canadian institutionsnot available
FundersConcordia University
KeywordsComposite numberBuckleBendingDeformation (meteorology)Displacement (psychology)Field (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Composites have proven their great potential for many aerospace applications, where high performance justifies high cost. One of the potential applications of composites is helicopter landing gears. Helicopter landing gears consist of straight and curved tubes. A new analysis and design tool is required to consider the manufacturing technology. In this study, high-order analytical methods are proposed to analyze and design thick laminated orthotropic straight and curved tubes subjected to different boundary and loading conditions.
\nIn the first part of this thesis, the elasticity displacement field of thick laminated composite straight tubes is developed. In this investigation, thick composite cantilever tubes under transverse loading are studied using the newly displacement-based method. This method provides a quick, convenient and accurate procedure for the determination of 3D stresses in thick composite straight tubes subjected to both bending and shear loadings. In addition, this method is used to study stress and strain distributions in thick composite straight tubes with different simple and complex lay-up sequences. Note that thick laminated composite straight tubes subjected to cantilever loading conditions are investigated for the first time. Moreover, the developed method is used to analyze thick laminated composite straight tubes subjected to different mechanical loadings such as axial force, torque and bending moment.
\nIn the second part of this thesis, the general displacement field of thick laminated composite curved tubes is developed. By proposing a new high-order displacement-based method, single-layer composite curved tubes are examined. First, a displacement approach of Toroidal Elasticity is chosen to obtain the displacement field of single-layer composite curved tubes. Then, a layer-wise method is employed to develop the most general displacement field of elasticity for thick arbitrary laminated composite curved tubes. The developed method is used to analyze single-layer and laminated composite curved tubes subjected to pure bending moment. Note that displacement-based Toroidal Elasticity is applied to study thick laminated composite curved tubes for the first time. In addition, the failure analysis on thick composite curved tubes subjected to pure bending moment is conducted. Effects of lay-up sequences of composite curved tubes on stress distributions and failure sequences are investigated, as well.
\nThe accuracy of the proposed methods is verified by comparing the numerical results obtained using the proposed methods against finite element method, experimental data and solutions available in the literature.
\nThe methods that proposed in this thesis do not require meshing. They simplify greatly inputs that the user has to do, once the program for solution is available. This presents a clear advantage over FEM. Therefore, the most important advantage of the proposed methods is that inputs for modeling and analyzing of composite straight and curved tubes with complex lay-up sequences are simple, easy to use and fast to run. In addition, using FEM for the parametric study is cumbersome. By applying the proposed methods, the parametric study for thick laminated composite straight and curved tubes is simple with low computational cost.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.314
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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