Evaluation of RANS and URANS approaches for the prediction of hot streak migration in a high-pressure turbine stage
Bibliographic record
Abstract
In industry, RANS simulations are still the backbone of numerical simulations as they offer a good compromise between cost and precision. While being extensively used for turbomachinery research and development, the method can suffer from a lack of predictability when tracking the temperature transport in the high-pressure turbine stage because of the strong coupling with the combustion chamber. In this paper, the RANS and URANS methods are evaluated on the configuration of FACTOR project, where experimental results are available. In a first part, the impact of vane/blade interactions, cooling and turbulence modelling is investigated. The analyses provide a framework to classify the mechanisms affecting the hot streak transport and to assess wether an increase in complexity is necessary for its prediction. In a second part, the representativity of the boundary condition generated from the experiments at the inlet of the turbine is tackled in order to reach an acceptable accuracy. Two problems are put forward and evaluated: measurement difficulties at P40 because of the high levels of swirl and an unsteady nature of the inlet of the high-pressure turbine due to the presence of an instability. High-fidelity combustor/turbine LES results of FACTOR project are used to generate both steady and unsteady boundary conditions at the inlet of the turbine. A method to provide an unsteady inlet boundary condition to the URANS simulations is developed with SPOD treatment. Investigations and comparisons with simulations fed at the inlet with experimental fields show that the first point is of major importance, while the second improves the results to a lesser extent. The sensitivity of this flow field to the inlet conditions is very demanding.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".