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Rotational superradiance in fluid laboratories

2016· preprint· en· W2953080031 on OpenAlexfundno aff
Vítor Cardoso, Antonin Coutant, Maurício Richartz, Silke Weinfurtner

Bibliographic record

VenuearXiv (Cornell University) · 2016
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilFundação de Amparo à Pesquisa do Estado de São PauloInstitut Périmètre de physique théoriqueRoyal SocietyIndustry CanadaOntario Ministry of Economic Development and InnovationGovernment of Canada
KeywordsSuperradiancePhysicsCylinderPhenomenology (philosophy)InstabilityMechanicsClassical mechanicsOpticsGeometry

Abstract

fetched live from OpenAlex

Rotational superradiance has been predicted theoretically decades ago, and is the chief responsible for a number of important effects and phenomenology in black hole physics. However, rotational superradiance has never been observed experimentally. Here, with the aim of probing superradiance in the lab, we investigate the behaviour of sound and surface waves in fluids resting in a circular basin at the center of which a rotating cylinder is placed. We show that with a suitable choice for the material of the cylinder, surface and sound waves are amplified. By confining the superradiant modes near the rotating cylinder, an instability sets in. Our findings are experimentally testable in existing fluid laboratories and hence offer experimental exploration and comparison of dynamical instabilities arising from rapidly rotating boundary layers in astrophysical as well as in fluid dynamical systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.173
Teacher spread0.154 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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