MétaCan
Menu
Back to cohort

Research on the recoil reduction efficiency of a recoilless launch gun with high projectile velocity

2024· article· en· W4405848251 on OpenAlexaboutno aff
Pengzhan Liu, Wei Jin, Zhiyu Shi, Yong Wang, Xiongfei Zhao

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Launch and Propulsion Technology
Canadian institutionsnot available
Fundersnot available
KeywordsProjectileRecoilPhysicsReduction (mathematics)Nuclear physicsAtomic physicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract Recoilless launch can improve the adaptability of unmanned platforms to weapons by eliminating recoil, but it has the disadvantage of reducing the initial velocity of the projectile. The initial velocity of the recoilless gun can be improved by increasing the charge mass, so research into the recoilless efficiency of recoilless firing with increasing charge mass is of great importance for future applications of recoilless weapons. Based on the combustible cartridge and induction ignition, the one-dimensional homogeneous flow internal ballistic of a recoilless gun with high initial velocity is established. The effect of the Laval nozzle diameter on the efficiency of the recoilless gun is then investigated. The results show that, compared to conventional guns, the recoil can be reduced to 1N-s without reducing the initial velocity of the projectile. A ballistic test on a slide-rail mount is carried out to verify the results of the analysis. The results should make an important contribution to the development of a recoilless rifle.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.031
GPT teacher head0.268
Teacher spread0.237 · 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 designBench or experimental
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

Explore more

Same venueJournal of Physics Conference SeriesSame topicElectromagnetic Launch and Propulsion TechnologyFrench-language works237,207