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Investigation of hypersonic conic flows generated by a magnetoplasma light-gas gun equipped with Laval nozzle

2019· article· en· W2910100668 on OpenAlexaboutno aff
П. П. Храмцов, V. A. Vasetskiy, U M Hryshchanka, M. V. Doroshko, M. Yu. Chernik, А. И. Махнач, И. А. Ших

Bibliographic record

VenueJournal of Physics Conference Series · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHypersonic speedMach numberNozzleConic sectionShock waveLight-gas gunSchlierenShock (circulatory)OpticsPhysicsHypersonic flowAerospace engineeringFlow visualizationMechanicsExpansion tunnelHypersonic flightShock tubeSchlieren photographyRocket engine nozzleFlow (mathematics)ProjectileEngineeringGeometry

Abstract

fetched live from OpenAlex

In this paper, a new method of producing hypersonic flows is proposed and results of an experimental study of hypersonic flow over cones with half-angles τ 1 = 3° and τ 2 = 12° are presented. The Mach numbers of studied incident flows were M 1 = 18 and M 2 = 14.4, respectively. The use of a light-gas gun, where an accelerating channel was replaced by a Laval nozzle, made it possible to obtain a hypersonic gas outflow with optical density sufficiently high for flow visualization and diagnostics by optical methods. The flow structure was visualized by means of schlieren method with the use of a straight Foucault knife. Shadowgraphs were recorded by a high-speed camera with a frame rate of 300 000 fps and an exposure time of 1 µ s. The Mach number for the incident flow was calculated from the shock wave inclination angle on shadowgraphs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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