Benchmark for reactive transport codes with application to concrete/clay interaction
Bibliographic record
Abstract
Nuclear waste repositories will use a significant quantity of cement: for the construction of access drifts, disposal cells and concrete plugs, and as containment material for low- to intermediate-level waste. Several European countries have chosen claystone formations as possible host rocks (Landais, 2006). Numerous cement/clay interfaces will thus be present in a radioactive-waste repository. Due to contrasting geochemical conditions (including Eh, pH, solution composition), these interfaces are subjected to steep concentration gradients and are highly reactive. Predicting long-term changes (1,000 to 100,000 years) in these cementitious and clayey materials is thus crucial for assessing the behaviour of such infrastructures. Experiments cannot provide sufficiently reliable information over such long time scale. Although natural and archaeological analogues can be very helpful, modelling is the unique tool to analyse and test different evolution scenarios. In order to build a better confidence in such calculations, it is of paramount importance to demonstrate that the results obtained are not dependent on the choice of the numerical reactive transport code to perform the simulation. In order to address this issue, a benchmark problem, divided into three steps with increasing geochemical refinement, has been set up (Marty et al., submitted). In all cases, the solutes transport across the interface between clayey host rock and concrete is diffusion driven and a 1D radial geometry and isothermal conditions (25°C) have been both considered. Both materials are full saturated. The first step of the benchmark only considers porewater solutions and the clayed host rock which is only modelled by an exchanger. The second step introduces the full mineralogy for both the concrete and the claystone considering slow kinetics rates for mineral dissolution-precipitation reactions, whereas the third one focuses on fast reaction rates. Seven international teams have been involved in this benchmarking exercise. All reactive transport codes used (TOUGHREACT, PHREEQC with two different ways of handling transport, CRUNCHFLOW, HYTEC, ORCHESTRA, MIN3P-THCm) gave very similar patterns in terms of predicted solute concentrations and of minerals distribution evolution (Fig. 1). The benchmarking exercise demonstrates that reactive transport tools have reached such a level of maturity as to be confidently used in support of long-term performance assessments.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".