Radionuclide transport modelling: The Smart Kd-concept in reactive transport codes

A key component of performance assessment for radioactive waste repositories in deep geological formations is the long-term prediction of potential radionuclide transport to the geosphere over periods longer than 100,000 years. Radionuclide sorption on minerals (host rocks and geotechnical barriers) is one of the most important retardation process. One big challenge for radionuclide transport calculations with large-scale heterogeneous geochemical compartments is the integration of realistic physico-chemical models and their parameters at affordable computational costs.
In performance assessment, sorption is an important retardation process and typically considered by constant distribution coefficients (Kd) that can be easily included in transport codes. One of the advantage of this approximation is their computational efficiency, but it cannot reflect changes in geochemical conditions. On the other hand, mechanistic surface complexation models used for process understanding can be directly coupled to transport codes with geochemical solvers, but usually only at high computational costs. An effective alternative to the above mentioned approaches is provided by the smart Kd concept (www.smartkd-concept.de) [1, 2], specifically developed to describe variable radionuclide sorption in transport models as consequence of changing geochemical conditions in time.
The fundamental strategy of the smart Kd-concept is to firstly compute multidimensional matrices (namely look-up tables) of distribution coefficients based on surface complexation and cation exchange models. The smart Kd-values are computed for different radionuclides as a function of a wide range of geochemical parameters. Such parameters are typically pH, ionic strength, and dissolved ions, e. g. calcium, carbonate. The look-up table is generated using the geochemical code PhreeqC [3].
The information stored in the look-up table can then be accessed by reactive transport codes at each point in time and space. This approach was already implemented in the d3f++ code [4]. Here, an additional implementation and validation of the smart Kd-approach in OGS6 [5, 6] is demonstrated. For this purpose, three benchmark test were defined with increasing complexity. Complexity is mainly related to the number of components (radionuclides) included in the simulation. Results obtained with the OGS6 were compared with OGS6#PhreeqC3.5.0, PHAST [7] and d³f++.