Ballistic Trajectory Modeling for Missile with Deflectable Nose
Bibliographic record
Abstract
With the development of science and technology, significant changes have taken place in the mode of modern wars. Application of military operating concepts such as ‘surgical precision strike' and ‘decapitation strike' make higher demands on precision-strike weapons. Ballistic trajectory correction ammunition is being rapidly developed due to its lucrative combination of low cost, high cost-effectiveness ratio, high damage rate, and applicability of existing inventory ammunition. Ballistic control technology has distinct advantages both in cost saving and improvement of the ammunition operational performance. Nose deflection is a feasible, effective, and fast-response flight control mode. The nose part of a projectile can be deflected at a certain angle relative to the projectile body axis, including a pressure difference between the windward and leeward sides of the warhead and generating the respective aerodynamic control force. In this study, a two-rigid-body trajectory model is established based on the multiple rigid body system theory. The proposed model is used to predict the flight trajectory of a projectile with the deflectable nose. Finally, the nose deflection effect on the ballistic trajectory variation is analyzed. The research results obtained provide the theoretical basis for the development of adaptive control smart ammunition and its engineering 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".