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Colliding Surface Instability for a High-Velocity Impact

2004· article· en· W17536696 on OpenAlexaboutno aff
V. V. Demchenko

Bibliographic record

VenueJournal of Food Protection · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsInstabilityMechanicsRichtmyer–Meshkov instabilityPhysicsClassical mechanicsInertial frame of reference

Abstract

fetched live from OpenAlex

Physical processes accompanying high velocity impact phenomena with relative velocities 1-1000 km/s attract particular attention of mechanics and physicists over the past decades [1]. This is due to the wide prevalence of these effects in present-day technology and fundamental science. For example, they occur in inertial confinement fusion demonstration experiments with multi-layer targets (breakeven), in the development of spacecraft and vehicle meteor protection, in explosive welding and strengthening, in studying the matter properties in physical experiments with superhigh pressure values of order 0.1-1000Gbar and so on. Sometimes the interaction surface turns out to be unstable and the characteristic recurring disturbances of a conic [2] or a similar shape form on it. In this paper problems of instability development on colliding surfaces for a high velocity impact are studied by numerical simulation method using mass, impulse and energy conservation laws in continuum. Using the numerical computional results the new mechanism of the instability development from the initial shape disturbances on interacting plates surfaces is suggested, in which fundamental aspects are: 1.Mass flows deflection behind a curved shock wave front; 2.The interaction of the secondary compressed and shock waves with the primary shock waves.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0300.005

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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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 designObservational
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
Published2004
Admission routes1
Has abstractyes

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