A coupled hydro-mechanical model for simulation of gas migration in host sedimentary rocks for nuclear waste repositories
Bibliographic record
Abstract
In a deep geological repository (DGR) for nuclear wastes, several mechanisms such as waste form degradation and corrosion could lead to gas generation. The produced gas can potentially overpressurize the repository, alter the hydraulic and mechanical properties of the host rock and affect the long term containment function of the natural (host rock) and engineered barriers. Thus, the understanding of the gas migration within the host rock and engineered barriers and the associated potential impacts on their integrity is important for the safety assessment of a DGR. In this paper, a coupled hydro-mechanical model for predicting and simulating the gas migration in sedimentary host rock is presented. A detailed formulation coupling moisture (liquid water and water vapor) and gas transfer in a deformable porous medium is given. The model takes into account the damage-controlled fluid (gas, water) flow as well as the coupling of hydraulic and mechanical processes (e.g., stress, deformation). The model also considers the coupling of the diffusion coefficient with mechanical deformation as well as considers the modification of capillary pressure due to the variation of permeability and porosity. The prediction capability of the developed model is tested against laboratory scale and in situ experiments conducted on potential host sedimentary rocks for nuclear waste disposal. The model predictions are in good agreement with the experimental results. The numerical simulations of the laboratory and field gas injection tests provide a better understanding of the mechanisms of gas migration and the potential effects of excessive gas pressure on the host sedimentary rocks. This research work has allowed us to identify key features related to gas generation and migration that are considered important in the long term safety assessment of a DGR in sedimentary host formations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".