MétaCan
Menu
Back to cohort
Record W4402468836 · doi:10.1016/j.ast.2024.109554

Enhanced LaRC05 failure criteria for investigating low-velocity impact on fiber-reinforced composites: An experimental and computational study

2024· article· en· W4402468836 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAerospace Science and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsNational Research Council CanadaCarleton University
FundersAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaMinistère de la Défense NationaleCanadian Armed Forces
KeywordsComposite materialMaterials scienceFiber-reinforced compositeFiberStructural engineeringEngineering

Abstract

fetched live from OpenAlex

• The proposed FE modeling methodology can accurately predict the impact response of composite laminates. • Fiber breakage, pull-out, splitting, kinking, crushing, and matrix cracking are predicted using the enhanced LaRC05 criteria. • Delamination and intralaminar matrix cracking interactions are modeled. • The matrix fracture plane and the fiber kink band angle can be found 48 % faster using the SRGSS algorithm. • The detailed sequence of impact damage occurrence is predicted by analyzing the histories of dissipated energies. A finite element model was developed using both continuum and discrete damage modeling techniques to provide detailed predictions for ply-by-ply damage progression in composite laminates during low-velocity impact (LVI) events. A new fiber failure model was incorporated into the LaRC05 failure criteria to predict fiber pull-out and fiber crushing during the fiber damage evolution. In addition, the selective range golden section search (SRGSS) algorithm was implemented to efficiently predict fiber breakage, pull-out, splitting, kinking and crushing, and matrix cracking. The delamination was captured by cohesive element layers embedded between every adjacent composite ply. The interactions of intralaminar matrix cracking and delamination were modeled by deploying cohesive elements within each composite ply. The prediction results were validated by 30 J and 75 J drop-weight tests with different-sized impactors, as well as X-Ray CT inspections on 254 mm by 304.8 mm [0/45/90/-45] 4 s IM7/977–3 laminates. The model predicted the maximum deflection and contact duration with <2 % error, and the peak load, damaged areas, and absorbed energy with <8 % error. The matrix fracture plane and the fiber kink band angle were found with 1° precision 48 % faster via the SRGSS algorithm. The detailed sequences of damage occurrence were predicted by analyzing the energy dissipation histories through various damage modes. Although this modeling methodology was developed for LVI scenarios, it has broad applications for predicting failures in composites.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.312
Teacher spread0.297 · 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