Surface denitration structure on dynamic combustion performance and muzzle flame of mixed nitrate gun propellant
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".