Influence of Blade Loading Profile on Wake Dynamics in High-Pressure Turbine Cascades
Bibliographic record
Abstract
The influences of blade loading profile on wake convection and wake/wake interaction were studied in two different blade designs for high-pressure (HP) turbines (front-loaded (FL) and aft-loaded (AL)), installed in linear cascades. A high-speed moving bar (HSMB) apparatus replicated wake shedding, and a closed loop wind tunnel produced engine-relevant Mach numbers (Ma = 0.7) and Reynolds numbers (Re = 3 × 105). The FL blades had approximately 10% greater total pressure loss when operated with unsteady wake passage. Phase conditioned particle image velocimetry (PIV) measurements were made in the aft portion of the blade channel and downstream of the blade trailing edge. The turbulence kinetic energy (TKE) in the wake was approximately 30% higher for the FL blades when the wake entered the measurement field-of-view. The pressure field in the upstream region of the FL blade design is believed to induce high magnitude strain rates—leading to increased TKE production—and more aggressively turn and dilate the unmixed wake—leading to increased mixing related losses. The higher TKE for the FL blades largely dissipated, being approximately equal to the AL wake by the time the wake reached the end of the blade passage. The interaction of the convected wake with the wake from the blade trailing edge caused periodic vortex shedding at the second harmonic of the convected wake frequency. This interaction also modulated the strength of the trailing edge wake. However, little difference was found in the modulation amplitudes between different cases due to similar strengths of the convected wakes in this region. The higher wake TKE in the upstream portion of the blade channel for the FL blades, therefore, is expected to be the cause of the higher total pressure loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".