Toward CFD‐Based Correlations for Single‐State High‐Pressure Transonic Turbine Stage
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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