Unsteady Vortices and Blade Loading in a High-Pressure Turbine
Bibliographic record
Abstract
Unsteady flow in a transonic, single-stage, high-pressure, axial turbine has been investigated numerically by solving the URANS (Unsteady Reynolds-Averaged Navier-Stokes) equations with the SST (Shear Stress Transport) turbulence model. Interest has focused on the identification and effects of the quasi-stationary vane and blade horseshoe vortices, vane and blade passage vortices, vane and blade trailing edge vortices, and blade tip leakage vortices. Moreover, two types of unsteady vortices, not discussed explicitly in the previous literature, have been identified and termed “axial gap vortices” and “crown vortices”. All vortices have been clearly and distinctly identified using a modified form of the Q criterion, which is less sensitive to the set threshold than the original version. The use of pathlines and iso-contours of static pressure, axial vorticity and entropy has been further exploited to distinguish the different types of vortices from each other and to mark their senses of rotation and strengths. The influence of these vortices on the entropy distribution at the outlet has been investigated. The observed high total pressure losses in the turbine at blade midspan have been connected to the action of passage vortices. The formation and disappearance processes of unsteady vortices located in the spacing between the stator and the rotor have been time-resolved. These vortices are roughly aligned with the leading edges of the rotor blades and their existence depends on the position of the blade with respect to the upstream vanes. In addition, the present study focuses on the unsteady blade loading that influences vibratory stresses. Contours of the time-dependent surface pressure on the rotor blade have demonstrated the presence of large pressure fluctuations near the front of the blade suction sides; these pressure fluctuations have been associated with the periodic passages of shock waves originating at the vane trailing edges.
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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 | 0.000 | 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 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".