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Record W4291476105 · doi:10.1088/1361-6501/ac86e4

A new experimental setup for time-resolved measurements of interactions between gas jets and flexible surfaces

2022· article· en· W4291476105 on OpenAlexaboutno aff
Johannes Burkert, Rüdiger Schwarze

Bibliographic record

VenueMeasurement Science and Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsNozzleBar (unit)Supersonic speedJet (fluid)Materials scienceSurface (topology)OpticsPlane (geometry)MechanicsInletRange (aeronautics)AcousticsPhysicsGeometryMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to present a new setup for time-resolved measurements of the interactions between gas jets and flexible surfaces. Such interactions are dominated by characteristic surface deformations and gas-driven surface waves. Previous works have studied the gas–surface interaction only in a sectional plane but not as a three-dimensional problem. In our setup, surface deformations and waves were generated by the impingement of a supersonic jet on a flexible surface made of a silicon membrane. The inlet pressure of the Laval nozzle was varied from 0.8 bar to 3 bar and the nozzle surface distance was set in the range of 30–120 mm with respect to the flat surface. The entire surface area and its behavior were recorded using a high-speed camera. The evaluation was based on the pixel intensity values of the recorded surface images during the jet impingement. A fast Fourier transformation was applied to the results to determine the dominant frequencies of surface movements in detail. From this, low- and high-frequency events on the surface can be derived. Correlations between the main process parameters, nozzle distance, nozzle inlet pressure and characteristic surface behavior were deduced and mapped.

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)0.001
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 score0.090
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.266
Teacher spread0.226 · 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
Published2022
Admission routes1
Has abstractyes

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