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Record W2171037110 · doi:10.1504/ijhvs.2007.013261

Application of SQP and dynamic mode tracking to the determination of the critical speed of rail vehicles

2007· article· en· W2171037110 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Heavy Vehicle Systems · 2007
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsUniversity of WaterlooOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaBombardier
KeywordsSequential quadratic programmingCritical speedControl theory (sociology)EngineeringMode (computer interface)Rotor (electric)Vehicle dynamicsDynamic programmingStability (learning theory)Control engineeringQuadratic programmingComputer scienceAlgorithmAutomotive engineeringMathematical optimizationMathematicsArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

A method is presented for determining the critical speed of rail vehicles. Dynamic Mode Tracking (DMT) is used to identify the dynamic modes for a given speed and a Sequential Quadratic Programming (SQP) algorithm determines the critical speed at which the least-damped mode has zero damping. It is shown that the SQP algorithm without DMT often fails to find the critical speed. In comparison, the combined SQP-DMT algorithm provides a reliable identification of the critical speed. This integrated algorithm can be applied to rotor dynamics, wind turbine dynamics, aeronautics, and road vehicle dynamics for automatically identifying the critical speed corresponding to a linear stability analysis.

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.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.267
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