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Measurement of Three-Dimensional Unsteady Flows Using an Inexpensive Multiple Disk Probe

2001· article· en· W2045877097 on OpenAlexaff
Sheldon Green, Peter Ostafichuk, Steven N. Rogak

Bibliographic record

VenueJournal of Atmospheric and Oceanic Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnemometerMeasure (data warehouse)Wind tunnelRange (aeronautics)AcousticsWork (physics)MechanicsPhysicsPressure sensorFlow (mathematics)CalibrationTransducerOpticsMaterials scienceComputer scienceTurbulence

Abstract

fetched live from OpenAlex

A novel velocimeter consisting of multiple orthogonal disks fitted with pressure transducers has been developed. In such a velocimeter the pressure difference is measured between the center of each disk face and the center of its other face for each of the three orthogonal disks. The three components of fluid velocity can be deduced from the three measured pressure differences. While previously developed anemometers based on dynamic pressure differences (such as yawhead or five-hole probes) can only measure velocities with a small range of directions, the new disk probe can measure three components of velocity, even in highly three-dimensional flows where the approximate direction of the flow is not known. Previous work demonstrated that in steady flows the device could measure velocities to ±1.4% and angles to ±4°. In the present work, involving both field trials and wind tunnel tests, it is shown that the disk probe can measure three-dimensional unsteady flows with accuracy suitable for many meteorological applications. The disk probe tested has a flat frequency response up to 3 or 4 Hz and can measure velocity magnitudes with an accuracy of better than ±0.3 m s−1. Simple modifications to the disk probe would increase its frequency range to 10 Hz or better.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.572

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 head0.014
GPT teacher head0.204
Teacher spread0.191 · 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 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
Published2001
Admission routes1
Has abstractyes

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