Investigation of Passenger Ride Comfortin a Railway Wagon forVarious Suspension Parameters
Bibliographic record
Abstract
Comfort for passenger in rail vehicles is still currant topic.Passengers often choose the preferred kind of transport just based on the comfort level.This article is focused on the investigation of passenger ride comfort in a railway wagon for various suspension parameters.The theoretical approach comes from a description of the applied method necessary to create a virtual model of an evaluated railway wagon and it also includes the method for evaluation of the passenger ride comfort.The passenger ride comfort in a wagon is evaluated by means of the standard method, at which, the passenger ride comfort indices marked as NMV in the determined locations on a wagon body floor are calculated.The calculation of the passenger ride comfort NMV index requires to know values of the accelerations signals in all three directions as an input.The section of the article presents the obtained results of simulation computations.There were changed the stiffness-damping parameters of the primary and secondary suspension system and their values have been changed in three levels.The railway wagon vehicle has been running on a model of a real track section.The passenger ride indices NMV have been calculated for all three variants of the wagon fifteen points located on the wagon body floor.The research results have shown, that lower values of stiffness of the secondary suspension system affects the passenger ride comfort more significantly than the lower values of stiffness-damping parameters of the primary suspension.
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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 | 0.001 |
| 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".