Tank Car Sloshing on Rail Transportation Safety Under Various Loading, Track and In-Train Force Conditions
Bibliographic record
Abstract
Analytical work was conducted to study if movement of liquid in a tank car (or sloshing) could contribute in any way to derailments of trains carrying dangerous goods liquids. A liquid sloshing model was developed for railway tank car with formulas generated based on available finite element analysis data. An empty tank car dynamics simulation model validated with measured data was used as the base model to implement the liquid sloshing model. Hundreds of thousands of dynamics simulations were conducted for the tank car with liquid cargo at various fill ratios and with equivalent solid (i.e., rigid) cargo on more than 1000 measured curves. The results show that under some conditions tank car sloshing could increase the risk of derailment. The detrimental effect of tank car sloshing on rail safety increases with the increase of outage, trailing tonnage, grade, car length difference, curvature, train speed and track geometry irregularities. Quantitative risk analysis could be improved by considering the effects of tank car sloshing on derailment risk. The findings can be used by regulators and the railroads to improve train marshalling practice and risk mapping of railway networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".