Design and fabrication of gradiently‐denitrated layer structure of seven‐hole gun propellant and its burning performance
Bibliographic record
Abstract
Abstract Improving the progressive burning performance of gun propellant is an effective approach to increase the muzzle velocity of bullet. The seven‐hole gun propellant characterized by a perforated structure with progressive burning performance has a broad application in the field of medium and small caliber weapons. Therefore, researches aiming to improve its burning performance are indispensable. In this paper, a seven‐hole gun propellant with the gradiently‐denitrated layer structure (GDLS) that the energetic functional groups increased gradually from the surface to the inside was designed and fabricated. Theoretical calculation indicated that the nitrogen content of nitrocellulose in seven‐hole gun propellant has a positive relationship with the burning rate coefficient and impetus. The results of burning calculation showed that the progressive burning performance of seven‐hole gun propellant were improved with the thickness of GDLS increases or the burning rate coefficient of the surface decreases. Moreover, the seven‐hole gun propellant with GDLS was successfully prepared, named gradiently denitrated seven‐hole gun propellant (GDSGP), which was proved by the results of FT‐IR, Raman and SEM. The different denitration conditions enabled the GDSGP with a good progressive burning performance, as confirmed by closed bomb test. Furthermore, the progressive burning performance of GDSGP increased first and then decreased with extending the denitration time. On the basis of design and study of GDSGP, the control and optimization of the burning performance of GDSGP will be more accurate in practical applications.
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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 | 0.001 | 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".