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Record W4317884633 · doi:10.1115/1.4056730

An Investigation of a Newly Developed Bistable Load-Type Supersonic Fluidic Oscillator for Generating Large-Amplitude Pressure Pulsations

2023· article· en· W4317884633 on OpenAlexafffund
Sichang Xu, Eugene Ryzer, G. W. Rankin

Bibliographic record

VenueJournal of Fluids Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Windsor
FundersMitacsOntario Centres of Excellence
KeywordsMechanicsAmplitudeDimensionless quantityOscillation (cell signaling)BistabilityFluidicsAcousticsSchlierenFlutterSupersonic speedControl theory (sociology)Materials sciencePhysicsOpticsEngineeringComputer scienceAerodynamicsOptoelectronicsElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Unique aspects in the development of bistable load-type fluidic oscillators that satisfy the requirement of producing large-amplitude pressure fluctuations during the charging of vessels for potential implementation in industrial processes such as the superplastic forming process are addressed in this paper. A pseudo-3D computational fluid dynamic model is shown to be capable of accurately predicting the experimental values of the dimensionless frequencies and pressure fluctuation amplitudes as well as the experimental Schlieren images of the flow field obtained over a wide range of operating conditions. The pseudo-3D model is also used to provide details of the fluid motion in the oscillator which could not be measured experimentally when investigating the operation of the device. The flow switching mechanism is identified as a consequence of a reduction of the flow deflection angle due to the increase of the downstream pressure load by the charging of feedback tanks. Some examples of the usefulness of the model as a cost-effective industrial design tool are also demonstrated. The effects of changing the number and size of the feedback tank volumes on the device frequency and amplitude of the oscillation are clearly shown using dimensionless variables.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.244
Teacher spread0.228 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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