RANS prediction of the aerodynamic losses in a linear turbine cascade with an upstream cavity and a purge flow
Bibliographic record
Abstract
In a turbomachinery, gaps separate mobile and fixed parts of the hub. In the case of a turbine, often a purge flow is blown through these gaps in order to seal and avoid hot gas to penetrate deep in the turbine disk components and over-solicit them. The interaction of this purge flow with the main flow creates and amplifies vortex structures, responsible for pressure losses. Accurately predicting the aerodynamic losses could allow the design of an efficient cavity geometry and the choice of a purge mass flow that minimises the losses while ensuring the sealing of the cavity. The Boussinesq approximation fails in such flows where a high turbulence level and anisotropy are present. Therefore, in RANS, the use of two-equation linear eddy-viscosity turbulence models is questionable. The present work proposes to assess a second order Reynolds Stress model as well as two eddy-viscosity models in a linear turbine cascade with an upstream cavity from which a purge flow emanates. The specificity of the cascade is the high external turbulence intensity (6%) which is all the more challenging in a RANS approach. Different purge mass flows and three configurations are evaluated: two with different cavities and one without any. The RANS simulations are compared to experimental data and high fidelity large eddy simulations. The RANS RSM simulation shows the best agreement with the measurements and all the RANS models show the same trends: the presence of a cavity induces additional pressure losses; increasing the purge mass flow helps to seal the cavity entry but nourishes the passage vortex and thus leads to additional losses. Finally, the high external turbulence intensity distinguishes the RSM model which accurately reproduces the vortex structures while eddy-viscosity models over-predict the turbulent diffusion.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".