Numerical fire modeling of crude oil spills: validation report
Bibliographic record
Abstract
For the last six years the National Research Council (NRC) has been collaborating with Transport Canada to investigate fire incidents involving crude oil rail tank cars. The investigation involved conducting intermediate-scale experiments of a tank car engulfed in pool fires fuelled by crude oil to characterize the thermal conditions external to the tank car. The experiments were conducted by the NRC and Sandia National Laboratories using a 1/10th scale cylindrical calorimeter to simulate a tank car. The calorimeter was placed above a 2-m diameter crude oil pool fire. To complement these experiments and to gain further insight the NRC has been constructing a numerical fire model for the conducted crude oil fire tests. These efforts have been envisioned to include four stages. The first stage objective was to explore the feasibility of using numerical models to simulate crude oil fires. Two open source tools were used: Fire Dynamics Simulator (FDS) and Open Source Field Operation and Manipulation (OpenFOAM). Both tools showed promising capabilities to simulate crude oil fires using parallel computation. Presented in this report are the findings from Stage 2 of the “Numerical Fire Modeling of Crude Oil Spills” project. The objective of this stage is to evaluate the ability of numerical models to predict gas phase parameters from crude oil pool fires. The numerical modelling results were validated using the NRC-Sandia crude oil pool fire experiments that were conducted to obtain thermal environment around a 1/10th scale rail tank car (calorimeter) engulfed in a 2-m diameter pool fire fueled by heptane and Bakken and dilbit crude oils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".