Side Impact Testing and Analyses of Unpressurized Tank Cars
Bibliographic record
Abstract
There has been significant research in recent years to analyze and improve the impact behavior and puncture resistance of railroad tank cars. Ultimately, the results of this work will be used by the Government regulatory agencies in the United States and Canada to establish performance-based testing requirements and to develop methods to evaluate the crashworthiness and structural integrity of different tank car designs. This paper describes results of recent side impact testing and corresponding analyses using detailed finite element analyses (FEA). The test and analyses were performed to evaluate the side impact puncture performance of DOT-111 tank cars. The tank car was filled with water to approximately 97 percent of the volume. The tank was then sealed but not pressurized. The tank car was impacted at the Transportation Technology Center, Inc. by a 297,125-pound ram car with 12-by 12-inch ram head fitted to the ram car impacted the tank center. The analyses were on overall good agreement with the measured impact response. The lading was found to play a more significant role in the impact response than in previous testing and analyses of pressure tank cars. This is not surprising considering the reduced structural stiffness of the tanks compared to thicker pressure tank cars and the reduced effective stiffness from the initially unpressurized tank at impact. The smaller outage volume also contributes to a dramatic increase in the tank pressure as the dent formation reduces the tank volume and compresses the contents of the tank.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".