Off-Design Prediction of Transonic Axial Compressors: Part 1 — Mean-Line Code and Tuning Factors
Bibliographic record
Abstract
With yearly advances in CFD techniques and methodologies, and the increased capacity and capabilities of computer CPU, GPU, and information storage, CFD has become a powerful design tool. However, despite its vast strengths, a CFD analysis is still based on the sound development of the 1D mean-line analysis methodology. This paper (part 1 of 2) describes an off-design axial compressor mean-line code, tested in a specialized engineering software for the development and analysis of a whole gas turbine engine, and the various tuning factors used to obtain an off-design performance match. It will be shown that, to obtain a proper match of the off-design performance of single-stage transonic axial compressors, both the rotor and stage pressure ratio, and the rotor temperature ratio are required to be converged upon. To do so, the off-design mean-line analysis requires the incorporation of a set of inlet & exit blockage factors and deviation angles that vary with the compressor performance conditions. This approach differs from the literature-based procedural assumptions (or rule-of-thumb) of fixed inlet and exit blockage factors of approximately “0.98”, and the use of a unique deviation angle based on Carter’s rule. The results obtained in this paper are then used to develop a generalized off-design mean-line loss modelling methodology (part 2 of 2) capable of predicting the off-design performance of four well documented NASA transonic axial compressors.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".