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Record W2888777232 · doi:10.1115/gt2018-76661

S-Duct Diffuser Offset-to-Length Ratio Effect on Aerodynamic Performance of Propulsion-System Inlet of High Speed Aircraft

2018· article· en· W2888777232 on OpenAlexaff
Asad Asghar, W. Allan, Marc LaViolette, Robert Stowe, Derrick Alexander, Grant Ingram

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsMartec (Canada)Defence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsMechanicsAcousticsDuct (anatomy)PropulsionMaterials scienceAerodynamicsDragInletTotal pressureStall (fluid mechanics)Offset (computer science)Static pressureOpticsPhysicsEngineeringAerospace engineeringMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

This paper reports the internal performance evaluation of S-duct diffusers with different offset-to-length ratios. The geometric parameters of S-duct diffusers are currently of great interest because of increasing demand for stealth and consequently, their effects on drag and aero-engine stability margin. The generic S-duct diffuser selected as a baseline had a rectangular-entrance and circular exit. Test articles were tested with the high subsonic, Ma = 0.8 and 0.85, flow and were manufactured using 3D printing. stream-wise static pressure and exit-plane total pressure were measured in a test rig using surface pressure taps and a 5-probe rotating rake, respectively. The baseline and variant S-ducts were also simulated through computational fluid dynamics. The investigation indicated the presence of stream-wise and circumferential pressure gradients leading to a separated flow in the S-duct diffusers and distortion at the exit plane. The static pressure recovery decreased and total pressure loss increased with an increase in the offset-to-length ratio. The circumferential distortion at the engine face clearly indicated a trend with respect to the offset-to-length ratio, however radial distortion did not.

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 score0.123
Threshold uncertainty score0.794

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 head0.004
GPT teacher head0.194
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
Published2018
Admission routes1
Has abstractyes

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