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Record W4389584905 · doi:10.17118/11143/20917

Dynamic failure of Armox 500T steel against high-speed long-rodimpact

2023· article· en· W4389584905 on OpenAlexaff
Luyue Mao, Alexandra Komrakova, James D. Hogan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceMaterials science

Abstract

fetched live from OpenAlex

Numerical modeling approaches become effective engineering techniques in the field of structural-scale designs of armor systems under dynamic impact loading and a suitable material model enables to accurately reproduce experiments and investigate impactrelated phenomena (i.e., change of mechanisms).In this study, the Generalized Incremental Stress-State dependent damage MOdel (GISSMO) is applied to explore the dynamic failure and fracture of Armox 500T steel under high-velocity ballistic impact by a longrod projectile in LS-DYNA explicit solver.The Johnson-Cook model is used to describe the behavior of the tungsten heavy alloy projectile and the GISSMO is employed to describe the stress state-and strain rate-fracture behavior of Armox 500T steel target.Here, the GISSMO for Armox 500T is validated and the Johnson-Cook model for tungsten heavy alloy is calibrated against ballistic impact experiments involving depth of penetration measurements on stacked Armox 500T plates.Once validated, the model is used to explore designs of new steel-based armor system configurations for various impact conditions (i.e., impact angle of obliquity, standoff distance, and target span).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.078
Threshold uncertainty score0.998

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)0.0030.003

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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

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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