Wall-Resolved iLES of Compressor Tandem-Blades Using High-Order DG Method
Bibliographic record
Abstract
Abstract The purpose of outlet guide vanes in low-pressure compressors is to redirect the incoming airflow axially. As climate, energy, and environmental goals push for greater efficiency, there is increasing pressure to enhance the performance of modern aeronautical engines, resulting in more extreme flow conditions. However, high flow turning often leads to increased losses and a higher risk of flow separation. Tandem blade designs offer a solution by enabling large flow deviations while helping to maintain flow attachment. This is achieved by making the boundary layer on the rear blade more resilient to separation. Despite these benefits, tandem blade configurations are subject to complex flow phenomena, including interactions between the wake of the front blade and the boundary layer on the suction side of the rear blade, laminar separation bubbles on the front blade, laminar-to-turbulent transition, as well as turbulent mixing and merging of the wakes. These effects significantly influence performance and pose challenges for standard RANS models. This paper investigates the performance of tandem blades using wall-resolved Large-Eddy Simulations (wrLES), offering a detailed comparison with RANS results. Specifically, we present wrLES of a low-pressure compressor tandem blade in a cascade configuration, employing a high-order discontinuous Galerkin method (DGM) to capture the complex flow dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".