A Multi-Zone Model for Hydrogen Accumulation and ventilation in Enclosures
Bibliographic record
Abstract
Due to the small characteristic molecular size of hydrogen, small leaks are more common in hydrogen systems compared to similar systems with hydrocarbons. This, together with the high reactivity, makes an efficient ventilation system very important in hydrogen applications. There are several models available for ventilation sizing that are based on either a well-mixed assumption or a fully stratified situation. However, experiments show that many realistic releases will be neither, and therefore additional models are needed. One possibility is to use CFD-models, but the small release sizes for pinhole releases (<<1 mm) make it difficult to find an appropriate mesh without excessive computational time (especially since the simulations need to be iterated to find the optimum ventilation size). An alternative approach, which is described and benchmarked in the current paper, is to use a multi-zone model where the domain is divided into several large cells where the mass exchange is simplified compared to CFD, and thus simulation time is reduced. The flow in the model is governed by mass conservation and density differences, due to concentration gradients, using the Bernoulli equation. The release of gas generates a plume which is modelled based on an empirical plume model which gives the entrainment and hydrogen source term for each cell. The model has a short run time and will therefore allow optimization in a short time frame. The model is benchmarked against five experiments with helium at the Canadian Nuclear Laboratories (CNL) in Canada and one hydrogen experiment performed at Lodz University of Technology in Poland. The result shows that the model can reasonably well reproduce accumulation in the experiments with small release without ventilation, but appears to slightly underestimate the level of stratification and the interface height for ventilated cases where the source is elevated from the floor level.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".