Fan Performance Scaling With Inlet Distortions
Bibliographic record
Abstract
Applications such as boundary-layer-ingesting (BLI) fans and compressors in turboprop engines require continuous operation with distorted inflow. A low-speed axial fan with incompressible flow is studied in this paper. The objectives are to (1) identify the physical mechanisms which govern the fan response to inflow distortions and (2) determine how fan performance scales as the type and severity of inlet distortion varies at the design flow coefficient. A distributed source term approach to modeling the rotor and stator blade rows is used in numerical simulations in this paper. The model does not include viscous losses so that changes in diffusion factor are the primary focus. Distortions in stagnation pressure and temperature as well as swirl are considered. The key findings are that unless sharp pitchwise gradients in the diffusion response, strong radial flows, or very large distortion magnitudes are present, the response of the blade rows for strong distortions can be predicted by scaling up the response to a weaker distortion. In addition, the response to distortions which are composed of nonuniformities in several inlet quantities can be predicted by summing up the responses to the constituent distortions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".