Low-overload internal ballistics in UAV ejection using multiple time-sequenced compressed-air chambers
Bibliographic record
Abstract
Compressed-air ejection systems are characterized by short actuation times and high instantaneous flow rates, which subject unmanned aerial vehicles (UAVs) to significant overloads during launch. These conditions impose stringent structural requirements on UAVs, adversely affecting weight and cost control. To achieve a high launch velocity with low overload, this study proposes a multi-chamber ejection method with time-sequenced actuation. This approach is based on an internal ballistics model that incorporates real-gas properties. This approach reduces launch overload while maintaining the required muzzle velocity. An internal ballistics model for UAV compressed-air ejection has been developed and experimentally validated, utilizing real-air properties. Furthermore, a multi-chamber ejection strategy was introduced. Simulations were conducted to analyze the internal ballistic performance of systems with two or three identical or different high-pressure chambers. The results demonstrate that using multiple chambers significantly reduces the maximum overload-by 20.91%, 26.08%, and 33.24% for two identical, two different, and three different chambers, respectively-while achieving the same muzzle velocity as a single-chamber system.
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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) | 0.000 | 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".