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Record W2344155988 · doi:10.1177/1548512916643768

Development of an efficient numerical model for shaped charge analysis

2016· article· en· W2344155988 on OpenAlexaff
Devon Downes, Amal Bouamoul, M. Nejad Ensan, Yves Baillargeon

Bibliographic record

VenueThe Journal of Defense Modeling and Simulation Applications Methodology Technology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsDefence Research and Development CanadaNational Research Council Canada
Fundersnot available
KeywordsShaped chargeRotational symmetryMechanicsNonlinear systemFinite element methodConstitutive equationJet (fluid)Materials scienceArmourDeformation (meteorology)Computer simulationCharge (physics)Structural engineeringPhysicsClassical mechanicsEngineeringComposite material

Abstract

fetched live from OpenAlex

The process of shaped charge jet formation, fragmentation, and penetration in rolled-homogenous armor steel target plates was investigated using the explicit, nonlinear Lagrangian finite element method. The investigation was conducted in two dimensions utilizing an axisymmetric configuration for a shaped charge with a 40 mm cone diameter. The results obtained using numerical simulations were compared to the experimental results obtained from tests. In addition, the dynamic behavior of the jet was compared to the visual data obtained from X-rays to confirm jet formation and fragmentation predictions. The advanced features of the developed shaped charge model include adaptive remeshing to follow the high deformation pattern of the jet, appropriate constitutive material models and equations of state to account for high strain rate, and restart files to allow the simulation to be performed in stages.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.134
GPT teacher head0.392
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2016
Admission routes1
Has abstractyes

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