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Record W3095240212 · doi:10.1063/12.0000837

Ejecta from liquid gallium during planar impact experiments

2020· article· en· W3095240212 on OpenAlexaff
Jason Loiseau, Justin Huneault, William Georges, Andrew Higgins

Bibliographic record

VenueAIP conference proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsRoyal Military College of CanadaMcGill University
Fundersnot available
KeywordsEjectaMaterials scienceShock (circulatory)SpallGalliumShock waveMass fluxComposite materialMechanicsMetallurgyPhysics

Abstract

fetched live from OpenAlex

When a solid or liquid metal surface is subject to shock loading, ejecta may be produced by two separate phenomena: growth of surface asperities from Richtmyer-Meshkov instability (RMI); and cavitation (micro-spall) at the surface from shock release if the material is liquid or shock-melts. In the present study we shock-loaded liquid gallium to modest pressures (4.5 GPa). The liquid gallium was sealed in a steel capsule and impacted with an explosively-drive steel flyer plate. Thickness of the gallium sample was varied to change the shock loading from a square pulse (supported) to an unsupported triangular pulse. Two surface conditions were also considered: a clean liquid surface, and an oxidized surface. Gallium free surface and ejecta cloud velocities were recorded using photonic Doppler velocimetry. Ejecta mass flux was measured using piezoelectric pins. Ejecta mass flux versus ejecta cloud velocity was extracted from integration of the pin voltage response assuming inelastic collision. The oxidized samples generated high-speed ejecta (uejecta/usurface > 1.5), indicative of RMI-driven jetting of asperities. The samples with liquid surfaces generated only low-speed ejecta (uejecta/usurface < 1.5), indicative of only micro-spall. For the oxidized samples, the unsupported shock case generated significantly more, higher-speed, ejecta compared to the supported shock case. In contrast, there was little influence of shock support on the mass flux profile for the clean liquid surface cases.

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 score0.032
Threshold uncertainty score0.774

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)0.0010.000

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 head0.020
GPT teacher head0.228
Teacher spread0.207 · 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
Published2020
Admission routes1
Has abstractyes

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