Incline Firing Analysis Using ANSYS to Determine Directional Barrel Deformations
Bibliographic record
Abstract
Abstract In this paper, the directional deformations of a 120mm smoothbore tank gun were analyzed during several initial firing angles. This analysis sets up the barrel as a cantilever beam and accelerates the projectile with a given pressure time load history. This study aims to understand the deformation patterns associated with various firing angles with a Finite Element Analysis (FEA) approach and seeks to obtain valuable initial conditions to improve long-range firing accuracy at a relatively large firing angle. The primary objective of the paper was to uncover a physical rationale behind the “rifle rule”, which suggests that when shooting at an inclined surface, the projectile tends to strike higher. The findings are intended to inform the development of a 3D trajectory analysis simulator. The axial, vertical, and horizontal deformation of the barrel at 0, ±15, ±30, and ±45 degrees are compared, as well as validating the results with the projectile’s exit velocity. Lastly, the results show that the vertical deformation of the barrel is significant enough to vary the trajectory of the projectile and should be considered during an inclined/declined firing scenario. While the axial and horizontal deformations are small enough to be considered trivial and unlikely to have a significant effect on the accuracy.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".