Flow control in supersonic flow field based on micro jets
Bibliographic record
Abstract
The flow field around supersonic aircraft is usually accompanied by complex flow phenomena, such as shock wave and shock wave/boundary layer interaction, which cause some adverse effects on aircraft performance. Seeking effective flow control methods has been a hot topic for many researchers. As an important method to improve the flow characteristics in supersonic flows, micro jet technology and its control mechanism have been paid much attention. In this article, we used compression corner calculation model and conducted detailed numerical investigations in the supersonic flow field with different injection pressure ratios, various actuation positions, and different nozzle types. The interaction between the micro jets and supersonic upstream flows generates complex flow structures, which contain bow shocks, barrel shocks, Mach disk, counter-rotating vortex pairs, and so on. The flow characteristics with micro jet schemes are superior to those in the no-control case. The controlling performance of micro jet is mainly determined by the following aspects. First, the downwash effect of counter-rotating vortex pairs can bring high-energy fluid into the bottom of the boundary layer to activate low-energy fluid and then strengthen the ability of resisting the flow separations. Second, the bow shock, which is generated upstream of the micro jet, significantly decelerates the downstream flows. Thus, the shock intensity at the corner is weakened and the characteristic of shock wave/boundary layer interaction is improved. In addition, the effective function range of MJ, that is, the distance between the counter-rotating vortex pair and the wall surface, is also an important factor. When both the counter-rotating vortex pairs and the bow shock are further from the wall, the flow characteristics around the corner in a larger area can be improved. Research shows that the micro jet scheme with Laval nozzle gives better controlling effect on shock wave/boundary layer interaction when the injection pressure radio is set to be 0.6, with the actuation location being 20 times the jet outlet diameter upstream of the corner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".