Numerical prediction of fire dynamics and the safety zone in large‐scale multiple pool fire in a dike using flamelet model
Bibliographic record
Abstract
Abstract This research aims to develop a computational fluid dynamics (CFD) methodology for estimating the safety zone around a dike with multiple pool fire (MPF). This study predicts the safety zone and burning characteristics of heptane MPF inside a square dike in calm wind and worst‐case crosswind situations using unsteady simulations. The heptane MPF is modelled using flamelet approach with large eddy simulation (LES) turbulence model incorporating the soot generation. Discrete ordinates and Moss–Brookes model estimate the effect of participating medium radiation on prediction of safety distance and soot production. The discretization is incorporated using an isotropic trimmed cell mesher with local refinement to resolve the flame characteristics in Simcenter STAR CCM+. An extensive grid‐independence study has been executed to find the optimal mesh. The flame temperature and O 2 and CO 2 mass fraction predictions in calm wind conditions are in good agreement with the experimental findings of Koseki and Yumoto and the maximum flame temperature predictions are within 2.8% error. The safety zone is predicted using the estimated radiative heat flux. The effect of different ordinate sets (angular discretization S4, S6, and S12) approach on safety distance prediction is investigated. The validated grid independent CFD model combined with flamelet generated manifold model and LES turbulence model is proposed to predict the safety zone for industrial MPF in crosswind scenarios, thereby preventing human fatalities and property loss.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".