Computational Fluid Dynamics (CFD) Modeling and Analysis of Hydrocarbon Vapor Cloud Explosions (VCEs) in Amuay Refinery and Jaipur Plant Using FLACS
Why this work is in the frame
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Bibliographic record
Abstract
Process safety helps prevent the unexpected and unplanned release of flammable and toxic chemicals, leading to poisonous gas clouds, fires, and explosions. Vapor cloud explosions (VCEs) are among the most severe hazards to humans and the environment in process facilities. Therefore, process safety demands to use best and reliable techniques to model VCEs in process industries and storage tanks of flammable chemicals. In this regard, the Computational Fluid Dynamics (CFD) models are more appropriate, as these models provide three-dimensional (3D) modeling of all sequences of events in an accident. In this study, CFD is used to model VCE in two industrial accidents: the Amuay refinery disaster (happened in 2012) and the Indian Oil Corporation’s (IOC) Jaipur terminal (2009). This work studies 3D CFD modeling of flammable cloud explosion in the real-time configuration for both accidents. FLACS (FLame ACceleration Simulator), a CFD software, is used to simulate the loss of hydrocarbon containment, cloud formation, and explosion in both industrial case studies. The ignition locations and grid sizes were varied to analyze their influence on explosion overpressure, temperature, vapor velocity, and fuel mass. This work also investigated the effect of geometry complexity on the explosion. Results showed that, as opposed to the coarse grid, the fine grid provides more precision in the analysis. The study also reveals an explosion overpressure of the order 4–15 bar (g) for the given case studies. This study’s results can help perform a qualitative and quantitative risk assessment of the Amuay refinery accident and Jaipur fire. The simulation of different scenarios can help develop and improve safety guidelines to mitigate similar accidents.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it