Assessment of Fan Stall Point Identification Ability of Steady RANS Computations With the Helicity-Corrected Spalart-Allmaras Turbulence Model
Bibliographic record
Abstract
Abstract Accurate axial fan/compressor stall point prediction using computational fluid dynamics (CFD) has generally been carried out using unsteady computations, often with slowly ramped back pressure. While this approach tends to be accurate with unsteady Reynolds-Averaged Navier Stokes (URANS) computations, it is computationally expensive, often requiring 4–6 rotor blade passages to be included in the computation and necessitating the solution of many rotor revolutions’ worth of flow time. Recently, some work has shown that the helicity-corrected Spalart-Allmaras (HCSA) turbulence model is able, in steady RANS computations, to predict fan stall points within a few percent of experimentally measured operability limits. In this paper, we directly predict stall points by using a steady, multiple reference frame (MRF) or “frozen rotor” approach which employs the HCSA turbulence model. The machine studied is a low-speed axial fan. Computations are carried out using the open-source CFD software package OpenFOAM. The approach is able to predict the stalling flow coefficient to within 0.006 of the experimentally-measured value. Detailed assessments of the flow field at the last stable operating point and at a point just into stall yield show that the HCSA model is able to predict the same stall inception mechanism captured in URANS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".