Validation of Transonic Axial Compressor Stage Unsteady-State Rotor-Stator Simulations
Bibliographic record
Abstract
The paper describes the validation of steady-state and unsteady CFD simulations against experimental data from a single-stage, transonic axial compressor stage. The experimental data include overall compressor performance, high-resolution traverses of total pressure and total temperature, and unsteady-state total pressure measurements downstream of the rotor. Various numerical rotor-stator interaction methods were applied across the operational speedline. These included a steady-state method mixing plane, and a transient time inclination method that accounts for the unsteady rotor to stator pitch change. Best practice procedures were employed to assess and quantify the sensitivity of the simulations to numerical error like mesh and timestep size, turbulence model error and systematic errors related to tip gap, and steady-state or unsteady-state flow assumptions. The effect of turbulence on the mean flow is modelled by a URANS approach based on the SST model and its extensions for laminar-turbulent transition and reattachment prediction. The complex unsteady flow interactions between the rotor and stator were validated against steady-state and unsteady experimental data. The compressor flow is explained in detail including transonic flow features like shock boundary layer interactions. Comparisons are made between the various rotor-stator interaction simulation methods and the experimental data.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".