Toward CFD‐Based Correlations for Single‐State High‐Pressure Transonic Turbine Stage
Bibliographic record
Abstract
Correlations are of great importance in the preliminary design of a gas turbine engine. They are useful to determine the efficiency and thus the Specific Fuel Consumption (SFC) of the engine, before even defining blade geometries. Also, correlations play a very important role in the understanding of the behavior (performance) of the engine, under different operating conditions. Since correlations are obtained from expensive rig tests and cascades, and since cascades cannot represent all situations in an actual engine, the necessity of finding more realistic and cost-effective representations of the actual flow phenomena is becoming more important. A novel approach would be to use CFD as the experimental test cell to generate such correlations and even to extend their limited regime of applicability. In this paper, using a 3-D finite element viscous, compressible, turbulent code, CFD-based correlations for a high-pressure transonic turbine have been created and validated against Cold Flow Turbine Rig test data. This has been done by studying the effect of changing tip clearance, blade speed, stage pressure ratio and vane stagger angle on the performance characteristics of a turbine stage.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".