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Record W2046914615 · doi:10.1142/s0219876213500564

AN IMPLEMENTATION OF THE SMOOTHED PARTICLE HYDRODYNAMICS FOR HYPERVELOCITY IMPACTS AND PENETRATION TO LAYERED COMPOSITES

2013· article· en· W2046914615 on OpenAlexaff
G. R. Liu, Chao Zhou, G. Y. WANG

Bibliographic record

VenueInternational Journal of Computational Methods · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsBank of Canada
FundersDefence Science and Technology GroupNational University of Singapore
KeywordsSmoothed-particle hydrodynamicsHypervelocityProjectileMechanicsParticle (ecology)Penetration (warfare)Materials scienceQuasistatic processPhysicsEngineeringGeology

Abstract

fetched live from OpenAlex

Driven by applications in the design of protective structure systems, the need to model high velocity impact is becoming of great importance. This paper presents a Smoothed Particle Hydrodynamics (SPH) procedure for 3D simulation of high velocity impacts where high rate hydrodynamics and material strength are of great concern. The formulations and implementations of the Johnson–Cook strength and damage model considering temperature effect, and Mie–Gruneison and Tilloton equations of state are discussed. The performance of the procedure is demonstrated through two example analyses, one modeling a cubic tungsten projectile penetrating a multi-layered target panel and the other involving a sphere perforating a thin plate. The results obtained, with comparisons made to both experimental results and other numerical solutions previously reported, show that our SPH-3D implementation is accurate and reliable for modeling the overall behavior of the high rate hydrodynamics with material strength.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.377
Teacher spread0.361 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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