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Influence of structural parameters of layered interception compound reactive liner on combined penetrating and implosion effects

2024· article· en· W4405848399 on OpenAlexaff
Rongchao Wei, Yakun Liu, Jianguang Xiao, Yifang Yang, Jinlin Zhang

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsMD Precision (Canada)
Fundersnot available
KeywordsImplosionInterceptionMaterials scienceEnvironmental scienceMechanicsComposite materialPhysicsPlasmaNuclear physics

Abstract

fetched live from OpenAlex

Abstract In response to the bottleneck problem that the penetration depth of traditional fluoropyr-based reactive material(RMS) shaped charge is seriously insufficient, a Layered Interception Compound Reactive Liner (LICRL) shaped charge structure is proposed. Based on the MPM numerical simulation method, the influence of structural parameters such as the height ratio ϕ and thickness ratio η of the inner and outer liners on the jet formation and its penetration behavior is investigated. The results indicate that the variation in ϕ primarily affects the head shape of jet formation. When ϕ = 2/3, a composite jet comprising a precursor copper jet and a trailing reactive jet is formed, which causes a “Penetrating While Explosion” damage effect on steel targets. The variation in η mainly influences the shaping morphology of the copper jet. When η > 2/3, the damage power of the composite liner is weakened, this is attributed to the poorer shaping morphology of the coaxial copper jet under higher liner thickness ratios and premature fracture of the copper jet due to reactive material response. Compared to traditional single reactive jets, the layered interception compound reactive liner (LICRL) forms jets that penetrate steel targets to a greater depth, and significantly increase the crater diameter compared to single-metal jets.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.301

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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