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Record W7132513857

Evaluation of numerical methods to model adhesives used in ballistic protection structures

2023· article· en· W7132513857 on OpenAlexvenueno aff
Devon Downes, M. Nejad Ensan, Lucy Li

Bibliographic record

VenueNPARC · 2023
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsProjectileCeramicFinite element methodPlasticityArmourConstitutive equationPolycarbonateBallisticsAdhesive
DOInot available

Abstract

fetched live from OpenAlex

A numerical investigation is presented in this work to study the ballistic performance of Aluminum Oxide (Al203) ceramic plates bonded to a polycarbonate backing subjected to impacts from 7.62 mm diameter calibre projectiles. The ceramic plate measured 102 × 102 mm with a thickness of 5.8 mm. Numerical models were developed using the explicit finite element code LS-DYNA, which possesses contact, erosion and material failure capabilities particularly suitable for ballistic simulation. The Johnson–Holmquist, Piecewise Linear Plasticity and Plastic Kinematic constitutive material models were used to model the materials behaviors of the ceramic, projectile materials and backing material, respectively. The residual velocity and depth of penetration (DoP) into the backing material was obtained and compared with available experimental data. The simulation was capable of capturing the DoP into the backing with a high degree of accuracy compared to the experimental tests. The results showed that when the ceramic plates were impacted by a projectile, the damage mechanism of the fragmented ceramic included radial and circumferential crack lines along with a pulverized center and a highly eroded bullet. The failure of the bonding adhesive between the ceramic plate and polycarbonate backing was also accurately captured by using the plasticity compression tension material model to simulate the adhesive.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.092
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0010.000

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 head0.143
GPT teacher head0.424
Teacher spread0.280 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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