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Record W2105567414 · doi:10.5539/cis.v4n5p59

Dynamic Response of Electromagnetic Launcher’s Rail Subjected to Cosine Pressure

2011· article· en· W2105567414 on OpenAlexvenueno aff
Ryan Wen Liu, Ligong Zhang, Bai Xiang-zhong, Feng Liu

Bibliographic record

VenueComputer and Information Science · 2011
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Launch and Propulsion Technology
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDeflection (physics)VibrationComputer scienceBeam (structure)MATLABTimoshenko beam theoryAcousticsStructural engineeringPhysicsClassical mechanicsEngineering

Abstract

fetched live from OpenAlex

In order to extend the rail life and improve the firing accuracy, the electromagnetic launcher’s rail can be modeled as a beam on elastic foundation with simply supported beam by moving load. Euler beam theory is applied to build the Mechanical model and the analytical solution of the equation subjected to cosine magnetic pressure is derived in detail, which has successfully avoided the errors which are caused by using the uniform pressure to approximately replace the variable force. Numerical analysis of the dynamic response on rail by the MATLAB software shows that as the exciting frequency increases, the peek of the maximal deflection and vibration velocity increase gradually. Taking the same speed of load into account, the dynamic response of rail is obviously smaller than constant force. Therefore the reliable theory basis is provided for the design and control the rail, which promotes the practical application of the electromagnetic Launcher.

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 score0.004
Threshold uncertainty score0.013

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)0.0040.001

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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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
Published2011
Admission routes1
Has abstractyes

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