Direct Numerical Simulations and Boundary Layer Analysis in Compressor Blade Channel at Various Reynolds Numbers
Bibliographic record
Abstract
Abstract The current study investigated the boundary layer (BL) characteristics in the V103 compressor blade channel under a series of Reynolds number (Re) conditions (Re=1.367×105, Re=1.506×105, and Re=1.645×105) using direct numerical simulation (DNS). Detailed analyses were conducted on the BL, including the separation bubble, transitions, and reattachments on both the pressure and suction surfaces. The analyses suggest that Re has a very limited impact on the size of the laminar separation bubble (LSB) on the pressure surface. However, the LSB on the suction surface significantly shrinks with increasing Re. Moreover, the BL thickness identification method based on Bernoulli’s principle was applied to complex internal flows for the first time and achieved an accurate determination of BL integral quantities. The transition locations, which were estimated using the BL shape factor, shifted upstream with the increase in Re on both pressure and suction surfaces, causing the earlier reattachment points. The study also investigated velocity profiles in the turbulent region of the BLs and successfully extended the inner-layer law from the traditional flat plate to the current curved surfaces, demonstrating the validity and accuracy of the inner-layer law’s formulations in describing the turbulent boundary layer (TBL) velocity profile. These findings provide both numerical and physical insights into the complex geometry BLs in the compressor blade channel at low-to-medium Re, offering strong potential for optimizing blade design.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".