Evaluation of CFD tools for crude oil fire simulations (modeling steps in openFOAM and FDS)
Bibliographic record
Abstract
Numerical modelling technique has been proposed to simulate fire incidents involving crude oil tank cars. This technique could complement scaled experiments to gain insight into the physics of large fire incidents and therefore better manage involved risk. Benefits of the numerical modeling can be summarized as follows: - Ability to extrapolate results of the scaled down experiments to a full scale incident; - Explore several fire scenarios that might be challenging to conduct experimentally such as effect of wind, tank location, tank orientation and spill size; and - Obtain higher spatial resolution of data such as temperature, incident heat flux and radiative and convective heat transfer. To this end, two computational fluid dynamics (CFD) tools have been explored to be used in the simulations of crude oil pool fire. The first tool was the “open Field Operation and Manipulation” (openFOAM). The other tool was the “Fire Dynamics Simulator” (FDS). Both tools have been explored in terms of governing equations solved, sub models used, discretization methodology and structure of input file(s). Also, modelling steps in each tool were outlined and explained in this report. A preliminary numerical model of a heptane pool fire, tested in Sandia’s fire facility, was used for demonstration purposes. Advantages and disadvantages of each tool were listed in the “Summary and Conclusions” section of the report. Both tools could be used to simulate crude oil fires involving engulfed objects. After carefully examining each tool, it would be beneficial to use both tools for the next steps of numerical modeling work. The goal then will be to recommend a robust tool to investigate fire incidents related to the transport of crude oil.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".