MétaCan
Menu
Back to cohort
Record W2938614128 · doi:10.1177/0954409719841516

Switch angle design: Formulation, inferences, and uses

2019· article· en· W2938614128 on OpenAlexaff
Nazmul Hasan, Halley Porkess

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · 2019
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsJerkAccelerationControl theory (sociology)Point (geometry)Computer scienceEngineeringMathematicsPhysicsGeometryClassical mechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

Jerk, lateral acceleration, and unbalanced superelevation are induced at the point of switch due to the combined effect of the switch angle and speed. Two sets of formula are derived to compute the values of jerk, lateral acceleration, and unbalanced superelevation due to the switch angle and speed separately. A sudden change in the direction of motion by the switch induces jerk which affects passenger comfort. The jerk due to the sudden change in the direction of motion is formulated in this paper by using the cubic spiral formula. It is demonstrated that the jerk at the point of switch has a positive (direct) relation with the speed and switch angle, but has a negative (inverse) relation with the pivot pitch. It is demonstrated that the switch angle should be optimized to reduce the lateral jerk. To capture the effect of the speed, the effective radius at the point of switch is formulated first and then formulas are derived for the jerk, lateral acceleration, and unbalanced superelevation at the point of switch due to speed. Finally, the aforementioned formulas are added to obtain the total values at the point of switch. The formulas are applied on American Railway Engineering and Maintenance-of-Way Association (AREMA) turnouts and validated by computer simulation using Vampire. These formulas would be helpful for the practicing designers and engineers.

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 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)0.000
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 score0.072
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.010
GPT teacher head0.185
Teacher spread0.174 · 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 teacher head, 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid TransitSame topicRailway Engineering and DynamicsFrench-language works237,207