Scaling of Incidence Variations With Inlet Distortion for a Transonic Axial Compressor
Bibliographic record
Abstract
Abstract This paper numerically explores the manner in which blade row inlet incidence variation scales with various distortion patterns and intensities. The objectives are to (1) identify the most appropriate parameter whose circumferential variation can be used to assess scaling relationships of a transonic compressor and (2) use this parameter to evaluate two types of non-uniform inflow patterns, vertically stratified total pressure distortions and radially stratified total enthalpy and total pressure distortions. A body force model of the blade rows is employed to reduce computational cost; the approach has been shown to capture distortion transfer and to be able to predict upstream flow redistribution with inlet distortion. Diffusion factor is shown to be an inadequate proxy for streamline loss generation in non-uniform flow, leading to the choice of incidence angle variations as the metric for which we assess scaling relationships. Posteriori scaling of circumferential flow angle variation based on the maximum incidence excursion for varying distortion intensity yields an accurate method for prediction of the impact for other distortion intensities; linear regression of the maximum variation in incidence around the annulus as a function of distortion intensity had R2 > 0.97 for all spanwise locations examined in both the rotor and stator for both vertically and radially stratified distortions. However, changes to far upstream distortion shape yield highly non-linear incidence variation scaling; the results suggest that the pitchwise gradients of far upstream total pressure govern the degree of linearity for incidence variation scaling.
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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 | 0.002 |
| 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".