Investigation to improve hunting stability of railway carriage using semi-active longitudinal primary stiffness suspension
Bibliographic record
Abstract
Railway carriage model moving on tangent tracks is constructed by deriving the associated equations of motion where single-point and two-point wheel-rail contact is considered. The railway carriage is modeled by 31 degrees of freedom which govern vertical displacement, lateral displacement, roll angle and yaw angle of wheelset whereas vertical displacement, lateral displacement, roll angle, pitch angle and yaw angle of carbody and each of two bogies. Linear stiffness and damping parameters of primary and secondary suspensions are provided to the railway carriage model. Combination of linear Kalker's theory and nonlinear heuristic model is adopted to calculate the creep forces in which introduced at wheel and rail contact area. Computer aided-simulation is constructed to solve the governing differential equations of motion using Runge-Kutta fourth order method. Principles of limit cycle and phase plane approach is applied to study the stability and evaluate critical hunting velocity of the system. The numerical simulation model is used to represent dynamic responses of the components of railway carriage subjected to specific parameters of wheel conicity and suspension characteristics. Longitudinal primary stiffness suspension is controlled using semi-active suspension with lateral displacement indicator. The controlled semi-active longitudinal primary suspension is examined to increase the critical hunting velocity and improve hunting stability of railway carriage. Key words: Railway carriage, conventional bogies, tangent tracks, semi-active suspension, longitudinal suspension, critical hunting velocity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".