LES/RANS Simulation of a Supersonic Combustion Experiment
Bibliographic record
Abstract
In this study, Reynolds-averaged Navier-Stokes (RANS) and hybrid large-eddy/Reynoldsaveraged Navier-Stokes (LES/RANS) techniques are used to investigate non-reacting and reacting ows in a supersonic combustion ramjet (scramjet). The scramjet design considered is similar to the experimental setup used by the Institute of Chemical Propulsion of the German Aerospace Center (DLR). The scramjet has a diverging upper wall and a wedge shaped fuel injector at the center. Hydrogen is injected at sonic conditions through 15 holes located at the base of the wedge. To generate the appropriate in ow conditions for the combustor, RANS calculations are performed for the ow through the Laval nozzle through which preheated air enters the combustor. For the non-reacting ow through the combustor, the RANS model provides better agreement with the experimental axial velocity and static pressure measurements, compared to the LES/RANS model. Axial velocity pro les from the LES/RANS simulations are more dissipated, perhaps indicating that the sizes of the larger turbulent structures are over-predicted. The reactive ow is simulated using RANS and LES/RANS techniques using two di erent hydrogen oxidation mechanisms (7-species and 9-species). LES/RANS predictions show the best agreement with experimental axial velocity, static temperature and axial velocity uctuation measurements when the 7-species reaction mechanism is used. All three models (RANS, LES/RANS 9-species, LES/RANS 7-species) predict a lifted ame. Based on the experimental temperature proles, it is clearly evident that the e ective reaction rates are under-predicted in the region just downstream of the wedge base. Agreement with experiment for the reactive cases improves for the LES/RANS methods further downstream.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".