Off-Design Prediction of Transonic Axial Compressors: Part 2 — Generalized Mean-Line Loss Modelling Methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Part 1, of this two-part paper, an off-design mean-line code and a generalized methodology to obtain “tuning” factors were presented. It was shown that the modified factors were capable of predicting the off-design performance of four well documented NASA transonic axial compressors. In this paper, Part 2, a generalized methodology to create correlations for the rotor and stator total pressure losses, deviation angles, and blade row inlet and exit blockage factors is presented. The generalized mean-line loss modelling methodology will allow the compressor designer to decommission the use of the performance map scaling techniques. In its place, the generalized predictive methodology will accurately estimate the off-design performance of transonic axial compressors and can be used to fill the gaps of missing data.
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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 it