Exploring the cycle of H2 gas using numerical modelling in the context of a deep geological repository
Bibliographic record
Abstract
Hydrogen gas (H 2 ) generation due to microbially influenced corrosion (MIC) is an important evaluation to build confidence in long term safety of the Canada’s proposed high level nuclear waste deep geological repository (DGR). Numerical modelling can be a powerful tool as the DGR design life far exceeds the timescales of laboratory or field studies. This work presents the first numerical modelling study exploring long-term H 2 dynamics under DGR environments. The key processes relevant to H 2 production and consumption are identified and two numerical models are presented; one that focuses on H 2 transport through the bentonite buffer and host rock, and another that considers production of H 2 through MIC and the biotic H 2 consumption (modelled through a simplified approach). This work is to investigate whether the net amount of H 2 would surpass the solubility limit leading to H 2 gas formation, using conservative assumptions of HS − and H 2 flux conversion when sulfate is a non-limiting species. The modelling study showed that long-term H 2 production from MIC may depend on HS − supply to the UFC, H 2 transport properties, and biotic H 2 consumption processes. While the HS − supply could increase the H 2 formation, H 2 transport through the rock and biotic H 2 consumption processes were shown to control the accumulation of H 2 . Amongst various modelling scenarios, the H 2 solubility limit was never surpassed, indicating the unlikelihood of H 2 gas pressure build-up in a DGR under these modelling conditions. Altogether, this study provides valuable insight into H 2 production, consumption, and transport dynamics in a DGR environment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".