Theoretical and experimental studies on the interior ballistic of large UAV ejection based on trifluoromethane phase transition
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have demonstrated immense value in the military sector. This research proposes the use of Trifluoromethane as a novel cold ejection medium. Trifluoromethane, being easily compressible, exhibiting high safety and low infrared characteristics, is well-suited for small-volume high-pressure chambers. The feasibility of Trifluoromethane for UAV ejection has been confirmed through experiment. Furthermore, a thermodynamic numerical model has been established for the ejection medium to investigate the effects of key parameters on ballistic performance. The study's findings demonstrate that as the volume of the high-pressure chamber increases, the ejection velocity of the UAV is enhanced, but the improvement slows down. Meeting the ejection velocity specifications for the UAV, reducing the volume of the high-pressure chamber can lower the peak pressure within the low-pressure chamber. An increase in the release pressure of the high-pressure chamber can enhance the ejection velocity, but the improvement slows down. Lowering this pressure can effectively reduce the UAV's acceleration. There is a maximum valve diameter beyond which the ejection velocity remains constant, however, the peak acceleration can still increase. Enlarging the volume of the low-pressure chamber can effectively reduce the UAV's peak acceleration. This study provides a safe and efficient technical solution for the cold ejection of large UAVs.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".