Rail Tank Car Total Containment Fire Testing: Results and Observations
Bibliographic record
Abstract
The frequent incidences of Non-Accident Releases (NARs) of lading from tank cars have resulted in an increasing interest in transporting hazardous materials in total containment conditions (i.e., no pressure relief devices). However, the ability of tank cars to meet thermal protection requirements provided in the Code of Federal Regulations under conditions of total containment has not been established. The intent of this effort was to evaluate through a series of third-scale fire tests, the ability of tank cars to meet the thermal protection requirements under total containment conditions, with a particular focus on caustic ladings. A previous paper on this effort described the test design and planning effort associated with this research effort. A series of seven fire tests were conducted using third scale tanks. The test fires simulated fully engulfing, hydrocarbon fueled, pool fire conditions. The initial tests were conducted with water as a lading under jacketed and non-jacketed conditions and also with different fill levels (98% full or 50% full). Additionally, two tests were conducted with the caustic, Sodium Hydroxide as the lading, each test with a different fill level. In general, the tanks with water were allowed to fail or reach near-failure conditions, whereas, the tests with the caustic lading were not allowed to proceed near failure for safety reasons. This paper describes the results and observations from the fire tests, and discusses the various factors that affected the fire test performance of the test tanks. Review of results from the one-third scale tests, and subsequent scaling to full-scale suggest that a full-scale tank car filled with 50% NaOH solution is unlikely to meet the 100-minute survival requirement under conditions of total containment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".