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Record W4411292101 · doi:10.1142/s0219455426503475

Penetration Characteristics of Deformable Penetrators on Steel Plates

2025· article· en· W4411292101 on OpenAlexaff
Pyounghwa Kim, Goangseup Zi, Timon Rabczuk, Seungjun Kim

Bibliographic record

VenueInternational Journal of Structural Stability and Dynamics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsPenetration (warfare)Structural engineeringMaterials scienceComposite materialMechanicsForensic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This study investigates the dynamic interactions between deformable penetrators and ductile metallic plates. Specifically, the analysis primarily focuses on the energy required by the penetrator under various target plate conditions. The study analyzes the energy dissipation components of this system, including the ratio of the energy dissipated by the penetrator to the energy dissipated by the plate. Furthermore, the energy dissipation within the plate is examined, including the proportions of kinetic energy and internal energy of the plate. A detailed analysis of the components constituting the internal energy of the plate is also conducted. Through this analytical study, it is observed that regardless of the plate thickness, most of the required kinetic energy of the penetrator is absorbed as the internal energy of the plate. It is observed that most of the internal energy may be attributed to the plastic deformation of the plate. Additionally, as the inclination angle of the target plate decreases below [Formula: see text], the penetration depth increases, leading to a higher dissipation of the penetrator energy, and, consequently, an increase in the required kinetic energy. These findings enable the design of target plates based on the penetrator performance characteristics.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.323

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 head0.271
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; 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
Published2025
Admission routes1
Has abstractyes

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