Brais Sobral

Evaluating the long-term behaviour of the engineered barrier system (EBS) in a deep geological repository for high-level radioactive waste (HLW) relies on the application of advanced reactive transport models with a high level of physical and chemical detail. In this context, the EBS comprises the waste canister, the compacted bentonite buffer, and the surrounding concrete liner. In parallel, artificial intelligence and machine learning (ML) techniques are advancing rapidly and are increasingly applied to: (a) speed up numerical computations, (b) handle complex multiscale and multiphysics interactions, and (c) support uncertainty quantification and sensitivity analysis. In this work, we develop metamodels aimed at representing steel canister corrosion processes, the formation of corrosion products, and their interactions with compacted bentonite. These metamodels act as efficient surrogate representations of high-fidelity reactive transport simulations, providing accurate approximations while substantially reducing computational cost.A metamodel has been developed for a geochemical system with interactions of steel/bentonite and precipitation of corrosion products. The system includes 3 primary dissolved species (Fe2+, H+ and O2aq), 4 aqueous complexes and two minerals (magnetite and goethite). A set of 500.000 data were sampled with a Latin Hyper Cube (LHC) sequence. Batch simulations were performed with CORE2Dv5 with 3 inputs corresponding to the total concentrations of Fe, H and O2. Outputs include primary and secondary dissolved species, total dissolved and total precipitated concentrations, magnetite, goethite, pH and Eh. The hybrid metamodel is based on Random Forests for group identification and Gaussian Processes for output predicition. A total of 7 groups were defined based on the presence or absence of minerals and some preselected ranges of Eh and pH (pH ≤ 9 and pH > 9). The metamodel provides excellent results for most of the output variables. Working with log-concentrations improves significantly results for some dissolved and precipitated concentrations. When the metamodel is trained by working with concentrations of dissolved Fe, the validation results show some negative concentrations. On the other hand, when the metamodel is trained by working with the logarithm of the concentrations of dissolved Fe, the predicted validation concentrations are always positive, but the metrics of the validation are slightly worse. The accuracy of the metamodel improves significantly by defining 7 groups with Random Forests. The metamodel shows excellent results for predicting mineral and total precipitated concentrations. The CPU for training the Gaussian process increases significantly with the number of training samples. Work is in progress to implement the metamodel into CORE2Dv5 for testing the improvements in CPU time provided by the metamodel.Acknowledgements: The research leading to these results was funded by ENRESA through Research Contracts within the Work Package ACED of EURAD (European Joint Programme on Radioactive Waste Management of the European Union), HERMES Work Package of EURAD 2 (Grant Agreement No. 101166718), the Spanish Ministry of Science and Innovation Project HERCULES (PID2023-153202OB-I00) and the Galician Regional Government (Grant Number ED431C 2025/55).

Groundwater flow and transport models are essential tools for assessing and quantifying the migration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol’s High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream of the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analyzed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity, and hydraulic head calibration metrics. Input parameters include aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductivities, and boundary inflows and conductances. VARS, HDMR, and graphical methods agree to identify the most influential parameters, which for most of the outputs are the hydraulic conductivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations.

Groundwater flow and transport models are essential tools for assessing and quantifying the mi-gration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol’s High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analysed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity and hydraulic head calibration metrics. Input parameters in-clude aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductiv-ities, and boundary inflows and conductances. VARS, HDMR and graphical methods agree to identify the most influential parameters which for most of the outputs are the hydraulic conduc-tivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations.

Lindane (a harmful contaminant) was produced in Sabiñánigo (Huesca, Spain) and deposited at the Sardas landfill. This site contains a large mass of pollutants, which have an extremely large contamination potential of the Ebre River. The site has undergone numerous human interventions that have modified the natural conditions. The site exhibits complex hydrogeological patterns and has been monitored systematically for a long period of time, and a large amount of geological, hydrological, and hydrogeological data are available. Here, a 2D finite element groundwater flow model along a vertical profile heading east–west along the thalweg of the former gully is presented. The main goal is modelling groundwater flow through the landfill, the Gállego River alluvial aquifer, and its interactions with the Sabiñánigo reservoir. The numerical model confirms the prevailing conceptual hydrogeological model of the site. The main results include: (1) Groundwater flows into the landfill mainly along perimeter ditches, which do not properly drain the surface and subsurface runoff (13.84 m³/d) and from the underlying marly rock (8.84 m³/d); (2) The total landfill leachate outflow towards the alluvial floodplain underneath the front slurry wall through a shallow marl layer is equal to 17 m³/d; (3) The oscillations of the Sabiñánigo reservoir water level produce a tidal effect that results in periodic changes of the hydraulic gradient between the alluvial gravels and the reservoir; (4) Groundwater flows generally from the alluvial aquifer towards the reservoir in an average E–W direction with an average Darcy velocity equal to 5 cm/d. The flow direction, however, changes to W–E when the reservoir level rises suddenly and; (5) The hydrodynamic parameters of the alluvial silts and reservoir silting sediments are crucial in determining the influence radius of the inversion of groundwater flow direction when the reservoir level rises suddenly. Model results enhance the confidence of the conceptual model, provide the basis for detailed specific models of the landfill and the alluvial aquifer, and highlight the importance of considering the tidal effect of the reservoir level oscillations. They also provide valuable information for managing the landfill and its impact on the surrounding groundwater system.