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Record W3165951055 · doi:10.32920/ryerson.14663172.v1

Numerical modelling of impact damage on carbon fiber reinforced polymer laminates

2021· preprint· en· W3165951055 on OpenAlexafffund
Kavya Roy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
FundersNational Research Council Canada
KeywordsFuselageStructural engineeringMaterials scienceComposite laminatesFibre-reinforced plasticComputer simulationComposite materialShear (geology)Carbon fiber reinforced polymerComposite numberComputer scienceEngineering

Abstract

fetched live from OpenAlex

In recent years, the usage of carbon fiber reinforced polymers in the aviation industry has increased significantly. While numerous advancements have been made in the field of testing and analysis of advanced composites, improvements can still be made in terms of time and cost. This thesis is focused on numerical modelling of nonlinear three-dimensional transient-dynamic impact damage, and also assesses various numerical techniques for reducing computational costs while maintaining the accuracy of results under impact loadings. This thesis does so using three studies and the computational package LS-DYNA. The first study is performed to elucidate the behaviour of a stiffened thin-walled fuselage section subjected to low-velocity, high-energy blunt impact. The fuselage section is comprised of thin skin panels, stringers, frames and shear ties, all of which are modelled as multidirectional carbon fiber laminates. The critical locations during the impact, the failure sequence, and the failure loads, locations and times are all identified. The obtained numerical results are compared to experimental results on low-velocity impacts on composite

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), Insufficient payload (model declined to judge)
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 score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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