Smart-Monitoring for Nuclear Waste Deposits

 

Our society is using the geological subsurface both as a source of different natural resources as well as for storage and deposit of different types of human waste like nuclear waste. However, a complete picture of the subsurface is often not available, since boreholes or geophysical surveys only reveal some aspects of the subsurface. Therefore, complementary physics-based simulations are often used today to reconstruct thermo-fluid-mechanically coupled processes like groundwater flow or temperature distribution in the subsurface and predict, for example, the distribution of potentially contaminated groundwater in the future. Such simulation models have to be calibrated with observations and measured data. But it is not clear, which data are best suited for this calibration.

With respect to the search for a nuclear waste deposit, we want to find answers to this question for optimal data acquisition and smart-monitoring strategies. At the end of the project, new methods and approaches should be available that allow to systematically evaluate different data acquisition strategies regarding their value add-on for a specific requirement, like the quantification of radioactive contamination within a region of the subsurface due to contaminated water, and to develop smart-monitoring strategies based on these results. The latter is of eminent importance to ensure the safety of nuclear waste deposits.

Within this project, we have three main goals:

1. The development and provision of benchmark scenarios for the possible host rocks of a nuclear waste deposit: claystone, salt and crystalline rocks. Parameter estimation, optimization of data acquisition and the stability of forecasts shall be systematically analyzed based on these scenarios.
2. The implementation of a model cascade, which includes both the thermo-fluid-dynamical processes in the subsurface as well as following models like the spatial distribution of the accumulated level of radiation due to contaminated groundwater. The automatization of this process allows for simulations for different scenarios as well as the following statistical analysis and evaluation, for example, in terms of radiation zone maps.
3. The development of a modern and robust parameter estimation and data assimilation based on meta models and active Bayesian learning. Beginning with methods of optimal experimental design, optimal data acquisition and smart-monitoring strategies will be developed.

The collaborative research project "Improvement of the predictive quality of simulations related to permanent disposal sites for nuclear waste by optimal data acquisition and smart monitoring" is conducted by the chairs of "Applied Geophysics and Geothermal Energy" and "Methods of Model-based Development in Computational Engineering" of RWTH Aachen University as well as the Institute for Modelling Hydraulic and Environmental Systems of the University of Stuttgart.