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Launch Dynamic Characteristic of High Initial Velocity Grenade Launcher with Low Recoil

2013· article· en· W2129656034 on OpenAlexaboutno aff
Jie Song, Zhen Liao, Ming Qiu

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Launch and Propulsion Technology
Canadian institutionsnot available
Fundersnot available
KeywordsRecoilBarrel (horology)NozzleSpring (device)StiffnessInternal ballisticsMechanism (biology)MechanicsEngineeringMaterials scienceMechanical engineeringPhysicsSimulationStructural engineeringAerospace engineeringNuclear physics

Abstract

fetched live from OpenAlex

In order to reduce the recoil of High Initial Velocity Grenade Launcher (HIVGL) and obtain better firing precision, two reduction recoil structures - short recoil barrel structure and laval nozzle structure were used for HIVGL. Virtual prototype simulation model of HIVGL were established, the dynamics curves of automatic mechanism of HIVGL and human shoulder force curves were obtained when HIVGL was launched by dynamic simulation software - ADAMS. The motion characteristics of automatic mechanism and the variation regulation of the maximum shoulder force were analyzed with different combinations of HIVGL structures and nozzle chamber charge amount. The barrel floating spring stiffness, pre-pressure and the effect of barrel stroke on spring energy storage were also analyzed. As proved by the research results, the maximum recoil of HIVGL can be greatly reduced by introducing short recoil barrel structure and laval nozzle structure. This result provides a beneficial reference for structure design and optimization of HIVGL.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.009
GPT teacher head0.269
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

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