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Record W4389540993 · doi:10.17118/11143/20958

Flows in vibrating channel

2023· article· en· W4389540993 on OpenAlexaff
Nafisha Nubayaatt Haq, J. M. Floryan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsChannel (broadcasting)Computer scienceTelecommunications

Abstract

fetched live from OpenAlex

This study examined the impact of surface vibrations on the propulsion enhancement and the resistance reduction in the movement of parallel plates relative to each other.The research concentrated on monochromatic waves and laminar flows.The vibration wave's efficiency was evaluated by measuring the external force required to keep one of the plates moving at a fixed speed.It was found that waves moving in the opposite direction to the flow increased resistance, while the response of the flow to waves moving in the same direction as the flow is more complex and depends on the flow Reynolds number.In general, waves must be fast enough to decrease flow resistance, which creates a distinction between slow and fast waves, a helpful categorization for flows with a relatively low Reynolds number.As the Reynolds number increases, the possibility of resonances with the natural flow frequencies becomes a complication.Resonances are not possible with waves that travel faster than the plate speed and these supercritical waves usually decrease flow resistance.Slower (subcritical) waves can lead to more complex flow responses that tend to increase resistance.A complete elimination of resistance is possible if the waves are of a sufficiently short wavelength and travel at high speeds.This suggests that the mechanism has significant potential for the development of propulsion augmentation system.

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 categoriesInsufficient payload (model declined to judge)
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 score0.334
Threshold uncertainty score1.000

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)0.0000.001

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 head0.016
GPT teacher head0.209
Teacher spread0.194 · 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.

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
Published2023
Admission routes1
Has abstractyes

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