Bibliographic record
Abstract
Applications such as boundary-layer-ingesting 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. Previous work in the literature has shown that the same flow mechanisms contributing to the response of a fan to distortion are at play in incompressible and transonic flows. The objective is to 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 approach has been shown to capture overall stage performance and flow field behavior with distortions having length scales much longer than the blade pitch. The approach requires only knowledge of the blade geometry, but the model does not include viscous losses. As a result, efficiency is not assessed but instead a metric based on changes in diffusion factor is defined which is conjectured to be related to efficiency changes. Distortions in stagnation pressure, swirl, and stagnation temperature are considered. By studying the distortions individually, it is found that the diffusion metric scales linearly with the intensity of the distortions (i.e. the ratio of minimum to maximum values) but that changes in distortion location relative to the fan axis produce nonlinear changes in the diffusion metric. Combinations of distortions are also studied and it is found that the diffusion metric associated with the combined distortion can be predicted using a summation procedure for the metrics associated with the individual constituent distortions. The mechanism found to govern the effectiveness of this summation procedure is the incidence distortions at rotor and stator inlet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".