Turbulence Models of Separated Flow in Shock Wave Thrust Vector Nozzle
Bibliographic record
Abstract
In the present paper, based on typical jet flows in shock wave thrust vector nozzles, turbulence modeling in gas flow dynamics has been numerically explored in 2-D laval nozzle which has a secondary injection on one side of its divergent portion in order to simulate the complex strong pressure gradient and accurately capture flow separation point. Nine turbulence models have been adopted and assessed by comparing the obtained results which involve flow separation point and static wall pressure with the available experimental data. Another 3-D nozzle model is simulated using five different turbulence models and results are also compared with experimental data. The numerical results reveal that Goldberg’s realizable k-epsilon model gives the best results compared with other models in predicting the shock wave position and separation point, while the SA model shows its advantage in predicting the static wall pressure under certain conditions compared to the Goldberg’s realizable k-epsilon model. The computational results were analyzed and the change of the shock structure in different NPRs was discussed in laval nozzle with a secondary flow.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".