Dynamic simulation of a high-speed train with interconnected hydro-pneumatic secondary suspension
Bibliographic record
Abstract
A secondary suspension configuration that integrates the Interconnected Hydro-Pneumatic Struts (IHPS) to the air spring system is proposed in this investigation for railway vehicles. Using the dynamic performance of IHPS, this suspension aims to provide smaller vertical supporting stiffness and larger anti-roll resistance compared to the traditional configuration, the air spring is connected to an emergency rubber spring in series with quite large stiffness. By replacing the rubber spring with IHPS, the proposed suspension configuration contributes to vibration absorption as well as anti-roll stiffness of the vehicle. The IHPS has two hydraulic cylinders installed in parallel to support the suspended mass. Each hydraulic cylinder has three oil chambers, and the oil chambers between the left and right struts are cross-connected through pipelines. Considering the oil compressibility and the vibration of liquid in the interconnected pipes, the mathematical model of IHPS is formulated and established in MATLAB. A multi-body dynamic railway vehicle model is built in SIMPACK, into which the IHPS is integrated through a co-simulation technique. Model validations on the IHPS are performed and its static and dynamic stiffness is examined. Numerical simulations show that the IHPS suspension reduces the vertical acceleration on the car body floor at a frequency between 1 and 3 Hz than the traditional air spring system with/without an anti-roll bar configuration. The vertical Sperling index of the vehicle using the IHPS suspension is smaller than that of the traditional suspensions, and it is more significant when the air spring deflates. However, the vertical acceleration with IHPS is larger than the traditional suspensions at 13∼55 Hz when the air spring deflates.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".