Course Correction Fuze Concept Analysis for In-Service 155 mm Spin-Stabilized Gunnery Projectiles
Bibliographic record
Abstract
Guidance and control of artillery projectiles will be critical to future military operations. With the large quantities of unguided artillery shells stockpiled around the world, the course correction fuze could provide an attractive and cost-effective solution for munition control. This paper proposes a drag brake and a spin brake course correction fuze concept, and compares their performances against the roll-decoupled four canard configuration. Specific guidance and control functions were designed and tuned for each. The analysis was based on a typical 155 mm spin-stabilized artillery projectile. Dispersion sources included variations in muzzle velocity and gun’s azimuth and elevation relative to nominal conditions, and wind velocity perturbations. Monte Carlo simulations were performed to analyze the delivery accuracy. Results show that the drag brake concept compensates for muzzle velocity and longitudinal wind perturbations efficiently. The spin brake concept compensates for perturbations in lateral wind efficiently and, to a lesser extent, in gun’s azimuth. The rolldecoupled four canard configuration counteracts gun’s azimuth and elevation perturbations very well. A course correction fuze combining the drag brake and spin brake concepts is shown as a good solution to increase the projectile accuracy when all disturbances studied are present.
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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 | 0.001 |
| 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".