Research on the Dynamic Characteristics of the High Air Speed Nozzle in Isentropic Flow
Bibliographic record
Abstract
Abstract The application of microsatellites has been development with the continuous expansion of space missions in resents years. The microsatellites have several characteristics such as light weight, small size and low cost. The platform flotation table is one of the simulation ways on the ground. With the continuous development of space technology, satellite ground simulation platform flotation table as a key technology which attracts lots of researchers. In this paper, the highspeed nozzle has been analyzed based on the research of Laval nozzle. The state of shock waves can be researched by the Laval nozzle and the air speed from the nozzle can be calculated. The flow field of the high speed nozzle has been simulation by the FLUENT software. The nozzle mode has been built by the GAMBIT and the model is meshed in structured grid. The simulation result shows that the air in the nozzle has many speeds in different place. The stable speeds are 390 m/s and 150m/s in the larynx and trailing end position.The maximum are reached into the nozzle and the speed is 578m/s, which is deviated from the theoretical analysis. At the constant pressure of 4 bar, the mass of nozzle flow is 0.000318758 kg/s. The mass of outlet flow is 0.000318798 kg/s, and the calculation error is 10-8. The thrust of the nozzle is also stable at 3ms, and its stability value is 50.1 mN. The maximum force of the outlet is 176 mN before the nozzle reaches stability. The simulation result can guided for the nozzle design and itis more significance for the high precision control and measurement of nozzles.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".