Efficiency of rocket engine thrust vector control by solid obstacle on the nozzle wall
Bibliographic record
Abstract
The thrust vector control of a rocket engine by disturbing the supersonic flow in its nozzle is used for missile development for various purposes in different countries. Disturbance of the supersonic flow in the jet engine nozzle can be caused by various obstacles on the nozzle wall: solid obstacle, liquid or gas jet, combinations of solid obstacle with injected jets. The simplest and most effective way to create a disturbance is to disturb it by setting a solid cylindrical obstacle on the nozzle wall. The high efficiency is explained by the lack of the working fluid consumption on board the aircraft to create a control force, or its minimum amount necessary to protect the obstacle from the high-temperature oncoming gas flow in the rocket engine nozzle. This paper presents the study results of gas flow simulation with cylindrical obstacle perturbation on the wall of the Laval rocket engine nozzle in its subsonic and supersonic parts. The optimal placement in the nozzle is determined to obtain the maximum lateral control force. As a result of research, it was found that the perturbation of a supersonic flow in a rocket engine nozzle by a cylindrical obstacle has practically the same character when its position changes along the length of the nozzle. In the subsonic part of the nozzle in the median plane, the perturbed pressure on the wall has a positive sign, and on the obstacle wall its sign-alternating. When an obstacle is in the subsonic part of the nozzle, the integral value of the lateral force is negative in comparison with positive for the supersonic part.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".