Computational Fluid Dynamics Modeling of Fire and Human Evacuation for Nuclear Applications
Bibliographic record
Abstract
Abstract The nuclear industry has seen an increased use of computational fluid dynamics (CFD) technology as a high-fidelity tool for design-basis and beyond-design-basis accident simulations. Among its applications, CFD modeling of fire and smoke propagation in confined zones (e.g., a main control room (MCR)) is a promising approach, since detailed experimental investigation under various accident scenarios would be difficult. Egress analysis considering human behaviors is of significant importance to an effective accident mitigation strategy, and high-fidelity analysis tools now encompass these parameters in the simulation and design of emergency evacuations. In this study, the fire and smoke propagation in a MCR is modeled using the large eddy simulations (LES) code fire dynamics simulator (FDS), along with an evacuation module, EVAC to simulate the emergency egress under an electrical cabinet fire scenario. The FDS results presented in this paper constitute the first step at Canadian Nuclear Laboratories (CNL) in advancing the CFD modeling of fire and evacuation for nuclear applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".