Maneuverability Analysis of the Conventional 155 mm Gunnery Projectile
Bibliographic record
Abstract
The feasibility of guiding the in-service 155 mm conventional indirect fire spinning projectiles was evaluated by developing an algorithm solution for the guidance and control and proving its applicability in an analytical study. Unguided projectiles are stockpiled in large quantities around the world, so a retrofitting solution to add a guidance and control capability is an attractive option. However, there was a concern that it would be difficult to guide an originally unguided projectile which was designed for a ballistic flight. So, the existence of a solution in terms of capable guidance and control algorithms was explored to perform a maneuverability analysis using an analytical approach based on modeling and simulation. The concept studied was a 155 mm equipped with a roll-decoupled course correction fuze providing corrections in both azimuth and elevation planes. This course correction fuze replaces the existing fuze with one containing the sensors, computer and actuators, all integrated in a small form factor. The results show that guidance and control of this projectile is possible at the higher airspeeds, specifically above Mach 1, in harsh launch and environmental conditions. However, at the lower airspeeds, below Mach 1, the airframe input-output interaction becomes predominant and the projectile dynamic response becomes too slow for effective guidance and control.
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 | 0.001 |
| 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".