Surface denitration structure on dynamic combustion performance and muzzle flame of mixed nitrate gun propellant
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The gun propellant is an inaccessible energy and fuel for projectile to achieve destructive capability in a weapon system. Nitrocellulose/nitroglycerine/triethylene glycol dinitrate(NC/NG/TEGDN) based gun propellant with higher energy level than single‐based gun propellant can meet the demands of modern warfare. Currently, deterring strategy is commonly used to improve the combustion progressivity of NC/NG/TEGDN based gun propellant, together with aggravating the harmful emission phenomena such as muzzle smoke and flame. Therefore, it is essential to design and fabricate NC/NG/TEGDN based gun propellant with both good combustion progressivity and low characteristic signal. In this work, a gradiently denitrated NC/NG/TEGDN based gun propellant was successfully prepared by denitration strategy, whose structure was confirmed by FT‐IR, Raman and SEM. Here, closed bomb vessel test and interior ballistic test were conducted. The results showed that the gradiently denitrated NC/NG/TEGDN based gun propellant exhibited better combustion progressivity and interior ballistic performance than raw gun propellant, and these properties can be controlled by modulating the degree of denitration. Meanwhile, the change laws of the muzzle smoke and flame of the gradiently denitrated NC/NG/TEGDN based gun propellant were investigated by the smoke box method and high‐speed photography. Furthermore, a lower characteristic signal for the gradiently denitrated NC/NG/TEGDN based gun propellant was found compared to the deterred gun propellant by theoretical calculations. This work provides a new idea for preparing and applying a gun propellant with excellent comprehensive performance, including high energy, good combustion progressivity and low characteristic signal.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it