Fan Response to Inlet Swirl Distortions Produced by Boundary Layer Ingesting Aircraft Configurations
Bibliographic record
Abstract
Boundary layer ingesting aircraft configurations create substantial flow distortions in inlets of turbofan engines and alter propulsive efficiency and performance. The swirl distortion component of these inlet flow profiles changes the incidence angle of air entering the fan which alters the amount of flow turning and work performed by the fan. This paper presents the results of an experimental investigation of fan response to inlet swirl distortions in an operating turbofan engine. Three-dimensional flow measurements were taken in the bypass annulus behind the fan rotor of a Pratt & Whitney Canada JT15D-1 turbofan research engine rig experiencing inlet distortion from a StreamVane swirl distortion generator. The StreamVane was designed to impose a swirl distortion profile matching a computational fluid dynamics model of a conceptual blended wing body aircraft engine inlet. Results from the investigation revealed that the swirl distortion altered the fan rotor flow turning, persisted downstream of the fan rotor plane, and entered the fan exit guide vanes. When compared to non-distorted inlet flow, the average fan outlet total-to-atmospheric pressure ratio decreased approximately 1.5%, while the flow angles exiting the fan deviated by up to ±10°. Both results indicate reductions in propulsor effectiveness and support the requirement for distortion tolerant fan optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".