Large-Eddy Simulations for Lean Partially Premixed Hydrogen Deflagrations in Vented Vessels Associated with Compromised Nuclear Facilities
Bibliographic record
Abstract
With the increasing abundance of nuclear power in the 21st century, safety and accident prevention is all the more important. Nuclear accidents, like those that occurred at Three Mile Island, Chernobyl, or Fukushima, can release large quantities of hydrogen gas. If combustion of the resulting hydrogen-air mixture occurs, existing containment structures may be compromised, and radioactive material can be released to the environment. Therefore, an improved understanding of the propagation of lean partially premixed hydrogen deflagrations within buildings and structures is critical for the development of appropriate accident management strategies associated with these scenarios. This study aims to use a computational approach to characterize the over-pressure profile of hydrogen deflagrations, in order to improve the design of venting mechanisms for the aforementioned accident management strategies. The study applies a recently-developed large-eddy simulation (LES) methodology, which makes use of a progress-variable-based combustion model coupled with an empirical burning velocity model (BVM), an accurate finite-volume numerical scheme, and a mesh-independent subfilter-scale (SFS) model. The improved combustion model arising from this thesis also accounts for hydrogen stratification and local turbulent mixing effects. LES results for several hydrogen concentrations, chamber sizes, and vent areas are presented to illustrate the extended computational approach. The LES predictions of over-pressure with time are compared to experimental data obtained from the Large-Scale Vented Combustion Test Facility (LSVCTF) of the Canadian Nuclear Laboratories (CNL) for turbulent initial conditions. The findings highlight the potential of the proposed LES approach for accurately describing lean partially premixed hydrogen deflagrations within large-scale vented vessels and containment facilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".