An Initial Digital Twin Architecture for Long-Term Radionuclide Transport Modeling in Deep Geological Repositories

The long-term safety of Deep Geological Repositories (DGRs) requires rigorous assessments capable of predicting radionuclide transport over million year timescales. While the Digital Twin (DT) concept offers a robust framework for such assessments, the traditional requirement for bidirectional, real time communication is currently unfeasible due to the absence of active physical repositories. We propose a modular DT prototype application framework designed to evolve from a high fidelity simulation environment into a fully synchronized system as field data emerge.

At its core, this framework utilizes standardized data schemas to harmonize heterogeneous, site-specific field data from crystalline host rock including mineral composition, pore water chemistry, and surface properties. These standardized datasets are integrated via a specialized API into a modular orchestration pipeline that connects 1D and 2D fracture simulations with reactive transport codes such as PHAST, OpenGeoSys, and PFlotran. By containerizing these secondary physics models into Docker environments, the framework ensures high computational flexibility and reproducibility. This approach allows for the seamless integration of Machine Learning models and complex physics-based workflows while maintaining isolated execution environments.

Acknowledging the post-closure reality of a DGR where sensors may fail or lose power supply this framework prioritizes the characterization of source term evolution (radionuclide fluxes) through a "build fill close abandon" logic. Current  focus is on building features to establish resilient data formats and interface protocols to create a future proof foundation for geological safety. We demonstrate how containerization and robust interface design can transform divergent research projects into a unified, reproducible DT framework, applicable to any domain where long term predictive modeling is required despite limited real-time data.