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The Ablation Mechanism of Magnetic Driven Flyer Plate

2014· article· en· W2066963745 on OpenAlexaff
Gang Hua Wang, Ming Xian Kan, Yi Niu, Hai Long Zhao

Bibliographic record

VenueApplied Mechanics and Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsCanadian Association of Emergency Physicians
FundersNational Natural Science Foundation of China
KeywordsMagnetohydrodynamic driveMagnetic fieldThermal conductionMaterials scienceJoule heatingDiffusionMechanicsMagnetohydrodynamicsPhysicsThermodynamicsComposite material

Abstract

fetched live from OpenAlex

It is found that the samples usually melt and even gasify in the magnetic driven high-speed flying plate experiments, which phenomenon goes against the investigation on materials’ equation of state (EOS). To understand this phenomenon, the whole process of magnetic driven flying plate experiment is simulated by the magnetohydrodynamic code MDSC. The ablation mechanism of magnetic driven flying plate is analyzed through the control of thermal conduction coefficient, and resistivity coefficient which is related to the magnetic diffusion velocity, in the energy conversing equation. Inside the flying plate the velocity of stress wave is much higher than that of magnetic diffusion, and hence the current goes into the inside of sample no early than the stress wave, so the magnetic diffusion doesn’t play an important role in the first stage of flyer free surface velocity history. The thermal conduction doesn’t influence the whole free surface velocity history of the flying plate much, while the Ohmic heating has a big effect on it, without which more than 20% error will be produced. The energy transportation in the flying plate medium is mainly caused by the Ohmic heating from magnetic diffusion. Besides, the relationship between the magnetic diffusion and temperature of flying plate is also analyzed.

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 score0.290
Threshold uncertainty score0.329

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 head0.005
GPT teacher head0.178
Teacher spread0.173 · 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
Published2014
Admission routes1
Has abstractyes

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