Formulation of Time-Domain Random-Walk Compartment Model for Radionuclide Migration from a Geologic Repository

2008 ◽  
Vol 163 (1) ◽  
pp. 137-146 ◽  
Author(s):  
Daisuke Kawasaki ◽  
Joonhong Ahn
2014 ◽  
Vol 50 (7) ◽  
pp. 5759-5773 ◽  
Author(s):  
Vladimir Cvetkovic ◽  
Aldo Fiori ◽  
Gedeon Dagan

Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yuexiu Wu ◽  
Quansheng Liu ◽  
Andrew H. C. Chan ◽  
Hongyuan Liu

It is essential to study nuclide transport with underground water in fractured rock masses in order to evaluate potential radionuclide leakage in nuclear waste disposal. A time-domain random-walk (TDRW) method was firstly implemented into a discrete element method (DEM), that is, UDEC, in this paper to address the pressing challenges of modelling the nuclide transport in fractured rock masses such as massive fractures and coupled hydromechanical effect. The implementation was then validated against analytical solutions for nuclide transport in a single fracture and a simple fracture network. After that, the proposed implementation was applied to model the nuclide transport in a complex fracture network investigated in the DECOVALEX 2011 project to analyze the effect of matrix diffusion and stress on the nuclide transport in the fractured rock masses. It was concluded that the implementation of the TDRW method into UDEC provided a valuable tool to study the nuclide transport in the fractured rock masses. Moreover, it was found that the total travel time of the nuclide particles in the fractured rock masses with the matrix diffusion and external stress modelled was much longer than that without the matrix diffusion and external stress modelled.


Author(s):  
Francesco Cadini ◽  
Jacopo De Sanctis ◽  
Enrico Zio ◽  
Diana Avram ◽  
Tommaso Girotti ◽  
...  

Prediction of radionuclides release is a central issue in the performance assessment of nuclear waste repositories. To this aim a model of radionuclides migration through the repository barriers must be set up, accounting for the uncertainties affecting the process. In this context, the present paper presents the application of Monte Carlo simulation to a Markovian modeling framework proposed in the literature; two cases are presented to highlight the value added by the flexibility of the Monte Carlo simulation approach.


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