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Record W4292168494 · doi:10.1017/aer.2022.28

Design of a sub-scale fan for a boundary layer ingestion test with by-pass flow

2022· article· en· W4292168494 on OpenAlexaff
Hans Mårtensson, M. Lejon, D. Ghosh, M. Åkerberg, Faezeh Rasimarzabadi, Martin Neuteboom

Bibliographic record

VenueThe Aeronautical Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTransonicComputational fluid dynamicsScale modelNozzleBoundary layerScale (ratio)Marine engineeringFull scaleDistortion (music)Mechanical fanEngineeringAerospace engineeringSimulationMechanical engineeringAerodynamicsStructural engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract A design of a sub-scale Boundary Layer Ingestion (BLI) fan for a transonic test rig is presented. The fan is intended to be used in flow conditions with varying distortion patterns representative of a BLI application on an aircraft. The sub-scale fan design is based on a design study of a full-scale fan for a BLI demonstration project for a Fokker 100 aircraft. CFD results from the full-scale fan design and the ingested distortion pattern from CFD analyses of the whole aircraft are used as inputs for this study. The sub-scale fan is designed to have similar performance characteristics to the full-scale fan within the capabilities of the test facility. The available geometric rig envelope in the test facility necessitates a reduction in geometric scale and consideration of the operating conditions. Fan blades and vanes are re-designed for these conditions in order to mitigate the effects of the scaling. The effects of reduced size, increased relative tip clearance and thicknesses of the blades and vanes are evaluated as part of the step-by-step adaption of the design to the sub-scale conditions. Finally, the installation effects in the rig are simulated including important effects of the by-pass flow on the running characteristics and the need to control the effective fan nozzle area in order to cover the available fan operating range. The predicted operating behaviour of the fan as installed in the coming transonic test rig gives strong indication that the sub-scale fan tests will be successful.

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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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 routes1
Has abstractyes

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