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Record W2184476425 · doi:10.82308/44231

Development of a gaseous detonation driven hyper-velocity launcher

2010· article· en· W2184476425 on OpenAlexfundno aff
Patrick Batchelor

Bibliographic record

VenueeScholarship@McGill (McGill) · 2010
Typearticle
Languageen
FieldMaterials Science
TopicEngineering and Material Science Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInternal ballisticsPropellantDetonationLight-gas gunNuclear engineeringProjectileHeliumBallisticsAerospace engineeringHydrogenMechanicsHypervelocityMaterials scienceExplosive materialMechanical engineeringEngineeringPhysicsChemistryThermodynamicsAtomic physics

Abstract

fetched live from OpenAlex

The concept of using gaseous detonation waves to generate high-pressure propellant for a single-stage gas gun is investigated theoretically and experimentally. The advantage of this approach (in contrast to conventional light gas guns) is that the resulting launcher is inexpensive to construct and simple to operate, yet is capable of achieving velocities in excess of 3 km/s. Theoretical internal ballistics methods have been developed for the optimization of gas gun systems. A prototype detonation driven gas gun system has been developed. Performance of the gun system is optimized by formulating the detonable mixture to use as propellant, with fuel rich hydrogen/oxygen or helium-diluted hydrogen/oxygen appearing the most promising. All the parameters of the launcher design are explored using a quasi-one-dimensional Euler code to model the internal ballistics. Experimental implementation of the concept is performed with a 1.27-cm-inner-diameter (ID), 1.83-m barrel driven by the detonable gas mixture contained in a 1-m-long, 3.2-cm-ID driver. Velocities of 2.7~km/s are demonstrated with 2.5-g projectiles.

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)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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