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Record W4399638869 · doi:10.1115/ssdm2024-121581

Incline Firing Analysis Using ANSYS to Determine Directional Barrel Deformations

2024· article· en· W4399638869 on OpenAlexaff
Ce Huang, Jean-Michel Dhainaut, Jefferson Talley, Phil Du

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Launch and Propulsion Technology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProjectileTrajectoryBarrel (horology)Deformation (meteorology)Finite element methodCantileverStructural engineeringMechanicsRange (aeronautics)Range of a projectileEngineeringPhysicsMechanical engineeringAerospace engineeringMeteorology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.236
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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