Validation of a CFD Model of a Labyrinth Seal for Low Pressure Turbines Using a Fluid-Thermal Tool Tuned Through Experimental Measurements
Bibliographic record
Abstract
Labyrinth seals are elements commonly used in turbomachinery to reduce the hot flows leakages through the unavoidable gaps separating the blade tips and the facing stator parts.These leakages, as well known, are responsible, under the same fuel consumption, for a lower turbine and compressor efficiency resulting in a reduced engine performance.For this reason, a lot of researches, available in the literature, are devoted to the study and optimization of these elements, usually performed by means of Computational Fluid Dynamics (CFD) numerical models.To verify and improve the CFD simulations accuracy, experimental results, usually obtained by designing ad hoc tests and focused on the sealing region, are necessary.In the present work the experimental tests are carried out by using a Test Article (TA), representing one entire turbine stage and the next stator.The data, provided by experiments, are used to tune a fluid and thermal model reproducing the phenomena taking place in the TA.In particular, those results which describe the flow splitting in the sealing region can be used to measure the effectiveness of a given labyrinth seal configuration.In this way, it is possible to verify the reliability of a CFD model representing the same labyrinth seal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| 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".