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Record W3200160569 · doi:10.1115/gt2021-58849

Internal Aerodynamic Performance Evaluation of Double Entrance S-Duct Intake at Moderately High Subsonic Mach Number

2021· article· en· W3200160569 on OpenAlexaff
Satpreet S. Sidhu, Asad Asghar, W. Allan, Robert Stowe, Rogerio Pimentel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsDefence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsMach numberAerodynamicsDuct (anatomy)FuselageStatic pressureMechanicsInletTotal pressureAirflowAcousticsAerospace engineeringPhysicsMaterials scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Inlets are an essential element of aircraft propulsion systems. Aircraft with fuselage-embedded engines require intake ducts with bends to direct oncoming air into the engine. Consequently they often experience flow separation, losses, total pressure distortion, and swirling flow near the engine faces, all of which are detrimental to engine stability and performance. In some aircraft, double-entrance ducts are used to meet geometric constraints and maintain the required airflow. The present paper investigated aerodynamic performance of a bifurcated Y-duct with S-bends in both horizontal and vertical planes. Intake performance was evaluated at inlet Ma = 0.63 by measuring the surface static pressure along the four stream-wise rows of pressure taps and total pressure and 3D velocities using 5-hole probe across the exit plane of the intake duct. The data were used to determine the static and total pressure recovery, together with associated radial and circumferential distortion coefficients and swirl intensity. This work provides a rare experimental data-set for a twin-entrance, moderately high-subsonic, double S-duct intake. It compared reasonably with the most similar work published, that of single-entrance ducts at higher Mach number. Pressure recovery was on par while swirl was noted to be reduced when compared with those geometries. Complementary computational fluid dynamics was useful in the qualitative comparisons as well.

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 score0.112
Threshold uncertainty score0.998

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)0.0030.000

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 head0.234
Teacher spread0.220 · 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
Published2021
Admission routes1
Has abstractyes

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