Rail Tank Car Total Containment Fire Testing: Planning and Test Development
Bibliographic record
Abstract
Given the frequent incidences of Non-Accident Releases (NARs) of hazardous materials from tank cars, there 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. Also, the modeling tool commonly used by industry to evaluate thermal protection, AFFTAC, has not been validated under these conditions. 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, and also, to validate AFFTAC for such conditions. This paper describes the test design and planning effort associated with this research, including the design and evaluation of a fire test setup to simulate a credible, fully engulfing, pool fire that is consistent and repeatable, and the design and hydro-static testing of a third-scale tank specimen. The fire design includes controls on the spatial distribution and temperature variation of the flame temperature, the heat flux, and the radiative balance, to best reflect large liquid hydrocarbon pool fire conditions that may be experienced during derailment scenarios.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".