Loss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS Simulations
Bibliographic record
Abstract
Abstract To enhance turbine efficiency, it is essential to mitigate the loss generated by irreversible phenomena taking place in turbine flows, including boundary layers, shock waves, vortices, and trailing edge wakes. A fast and accurate detection of losses is therefore crucial from the earliest stages of turbine design, in which reduced order models based on oversimplified correlations are employed. Achieving this objective requires a deep comprehension of the physics behind each loss-generating mechanism, a goal attainable through the examination of the 3D flow. While existing criteria allow the identification of various phenomena, accurately quantifying losses generated by vortices remains a challenge: these losses frequently extend beyond the vortical structure. The aim of this paper is to provide a straightforward and effective approach to localize and assess vortex-related losses. This method is grounded in Zlatinov’s decomposition of the entropy generation rate equation into a streamwise and a secondary flow component. A criterion based on the vortex kinematics is used to evaluate the strength of the vortex, thereby enabling the determination of its spatial influence and its contribution to the overall losses. To validate the method, a post-processing code is developed which allows to perform loss breakdown. This tool makes use of existing identification criteria and some new techniques introduced within this work, especially for wake detection. 3D Reynolds-averaged Navier–Stokes simulations are carried out on several configurations, ranging from simple curved ducts to more realistic nozzle guide vanes, to gradually test and validate the computational tool. Results confirm that the highest rates of entropy generation occur outside of the vortical structure, and show good ability to identify both the vortex shape and its area of influence in terms of losses. A drastic improvement in the prediction of vortex losses is especially observed in the case of turbine blades with tip or hub leakage vortices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".