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Record W4387127487 · doi:10.1115/gt2023-100561

Evaluation of Inlet Conditions of an AFT Mounted BLI Design in a Sub-Scale Test Rig

2023· article· en· W4387127487 on OpenAlexaffabout
Faezeh Rasimarzabadi, Hans Mårtensson, Catherine Clark, Martin Neuteboom

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFuselageNacelleBoundary layerPropulsionInletThrustTraverseBoundary layer suctionBoundary layer thicknessEngineeringMarine engineeringBoundary layer controlStructural engineeringAcousticsAerospace engineeringTurbineMechanical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract A unique sub-scale rig for studying boundary layer ingestion (BLI) propulsion has been developed by National Research Council of Canada (NRC). The rig is capable of varying the operating pressure levels and Mach numbers independently. It consists of a boundary layer generator to simulate boundary layer development over an aircraft fuselage. The boundary layer thickness upstream of the fan blades is controlled independent of other parameters using air injected through a perforated plate. The purpose is to demonstrate the advantages of BLI in reducing the power required for a given thrust and to evaluate varying inlet conditions on BLI fan performance. This paper provides details on set-up and procedure for testing an aft-mounted, distortion-tolerant fan tested at a number of different operating conditions. The results shows how the fan speed can affect the boundary layer thickness at the nacelle inlet. Different strategies for probe traversing are evaluated to find suitable procedures for obtaining valid data. Considering that there are 24 vanes, the 36° intervals give a better representation of the average compared to 45° intervals, since the clocking relative to the stator wakes significantly affects the total pressure. The traversing has to be done individually in order to avoid that the downstream probes data be affected by the upstream probes. The numerical results are compared with the experimental data and show reasonably good agreement.

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)0.001
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 score0.084
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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