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Record W4288096147 · doi:10.1017/aer.2022.67

An altitude capable rig for studying engine inlet velocity profile effects: Boundary layer generator

2022· article· en· W4288096147 on OpenAlexaffabout
Faezeh Rasimarzabadi, C.J. Clark, Martin Neuteboom, David Orchard, Hans Mårtensson

Bibliographic record

VenueThe Aeronautical Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBoundary layerBoundary layer suctionFuselageBoundary layer controlBoundary layer thicknessThrustParametric statisticsAerospace engineeringPropulsionBoundary (topology)AerodynamicsVortex generatorGenerator (circuit theory)Flow separationLayer (electronics)Mechanical engineeringMaterials scienceEngineeringMechanicsPower (physics)PhysicsVortexMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract A new aerodynamic open-circuit test rig for studying boundary layer ingestion (BLI) propulsion has been developed by National Research Council of Canada. The purpose is to demonstrate the advantages of BLI in reducing the power required for a given thrust and to validate the performance of BLI fan concepts. The rig consists of a boundary layer generator to simulate boundary layer development over an aircraft fuselage. The boundary layer generator can be used to create a natural boundary layer due to skin friction but also comprises an array of perforated plates through which pressurised air can be blown to manipulate the boundary layer thickness. The size of the boundary layer thickness can be controlled upstream of the fan blades. Parametric studies of boundary layer thickness were then feasible. The test calibration was conducted to validate the concept.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2022
Admission routes2
Has abstractyes

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