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Record W2073513045 · doi:10.1115/1.4005968

Effects of Core Flow Swirl on the Flow Characteristics of a Scalloped Forced Mixer

2012· article· en· W2073513045 on OpenAlexafffund
Zhijun Lei, Ali Mahallati, Mark Cunningham, Patrick Germain

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaPratt and Whitney Canada
KeywordsVortexMechanicsReynolds numberTurbofanTurbulenceTurbulence kinetic energyCore (optical fiber)PhysicsFlow (mathematics)Materials scienceMixing (physics)Optics

Abstract

fetched live from OpenAlex

This paper presents a detailed experimental investigation of the influence of core flow swirl on the mixing and performance of a scaled turbofan mixer with 12 scalloped lobes. Measurements were made downstream of the mixer in a coaxial wind tunnel. The core-to-bypass velocity ratio was set to 2:1, temperature ratio to 1.0, and pressure ratio to 1.03, giving a Reynolds number of 5.2 × 105, based on the core flow velocity and equivalent diameter. In the core flow, the background turbulence intensity was raised to 5% and the swirl angle was varied from 0 deg to 30 deg with five vane geometries. At low swirl angles, additional streamwise vortices were generated by the deformation of normal vortices due to the scalloped lobes. With increased core swirl, greater than 10 deg, the additional streamwise vortices were generated mainly due to radial velocity deflection, rather than stretching and deformation of normal vortices. At high swirl angles, stronger streamwise vortices and rapid interaction between various vortices promoted downstream mixing. Mixing was enhanced with minimal pressure and thrust losses for the inlet swirl angles less than 10 deg. However, the reversed flow downstream of the center body was a dominant contributor to the loss of thrust at the maximum core flow swirl angle of 30 deg.

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 score0.290
Threshold uncertainty score0.448

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 head0.006
GPT teacher head0.189
Teacher spread0.184 · 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

Citations26
Published2012
Admission routes2
Has abstractyes

Explore more

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