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Record W4389584783 · doi:10.17118/11143/20916

Hybrid modeling of composite laminates for structural-scale armormodels

2023· article· en· W4389584783 on OpenAlexafffund
Yogesh Kumar, Patricia I. Dolez, James D. Hogan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Dynamics Land Systems
KeywordsArmourComposite laminatesComposite numberScale modelMaterials scienceScale (ratio)Structural engineeringComposite materialComputer scienceEngineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract: This presentation focuses on the development of a multi-scale hybrid finite element model for composite laminates used as a front and backing layer of a protection system in land armor vehicles. Laminated composites are widely used for impact resistance, energy absorption, stiffness, and structural integrity. A unidirectional carbon fiber reinforced polymer composite laminate is simulated using the MAT_ENHANCED_COMPOSITE_DAMAGE material model in the LS-Dyna® finite element software. The composite model consists of a sub-scaled volume around the impact zone of the protective armor layer, with the remaining areas being a macro-scale homogenous component. The calibrated material model is integrated with other materials to form a structuralscale model that also includes alumina hexagonal tiles and a steel plate which are bonded to the composite layers using a polyurethane adhesive interlayer. Once integrated, this system-scale model is simulated under high-velocity long rod impacts and calibrated with experiments. Overall, this work will give insights towards understanding the influence of the composite layers on the protection system performance and damage propagation through the different layers of the model, which will help in designing improved systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.522

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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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