Numerical Prediction of Lean Premixed Hydrogen-air Deflagrations in Vented Vessels
Bibliographic record
Abstract
In water-cooled nuclear power plants, hydrogen gas can be generated by various mechanisms during an accident. If the resulting hydrogen-air mixture within the facility combusts, existing containment structures may be compromised, and radio-active material can be released to the environment. Thus, to develop mitigation strategies for these scenarios, an improved understanding of the propagation of lean hydrogen deflagrations within buildings and structures is required. Large-eddy-simulation (LES) techniques can be used to model the propagation of flames in such scenarios. Adaptive-mesh-refinement can be used to increase the level of detail provided by the LES simulations, and thus they are applied in this study along with an Integral-length-scale-approximation (ILSA) sub-filter scale model. The LES predictions are compared to experimental data obtained by the Canadian Nuclear Laboratories (CNL). Particularly, the predicted time histories of pressure as well as the maximum overpressure achieved within the vessels are compared to those arising from CNL experiments.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".