Assessment of a novel <i>k–ω</i> turbulence model for transonic centrifugal impeller simulations
Bibliographic record
Abstract
Numerical simulation of high pressure ratio transonic centrifugal compressors is challenging for the existing turbulence models. A lagged k–ω model proposed by Olsen and Coakley for nonequilibrium effects was first applied to simulate the transonic centrifugal impeller SRV2-O. As comparative case studies, four other turbulence models ( k–ω model, RNG k–ε model, SST-CC model, and EARSM model) were also computed. The comparison showed that ( i) the selection of the turbulence model had a great influence on SRV2-O impeller simulations; ( ii) the lagged k–ω model had an advantage over other models in terms of overall pressure ratio and internal flow characteristics; and ( iii) the lagged model predicted a smaller blockage area caused by leakage vortex breakdown than other models, closer to the experimental result. The detailed parameter examination indicated that the nonequilibrium parameter a0 in the lagged model had little influence on the Mach number distribution and choking mass flow rate but a significant influence on the static pressure on the shroud casing. For a higher Mach number compressor, a smaller a0 is recommended for bettering the simulation accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".