Potential for high transient doses due to accumulation and chemical zonation of long-lived radionuclides across the geosphere-biosphere interface

Planning for the disposal of spent nuclear fuel is at an advanced stage in several nations around the world. Licensing of the disposal facility requires correspondingly detailed assessment of the future performance of the facility. With increased site-specific detail available to the assessment, local characteristics play an increasingly important role in determining the potential radiological risk posed by releases to the biosphere. In this paper we go beyond existing reference biosphere models and investigate the potential for specific accumulation mechanisms. The implications for the modelling carried out in long timescale performance assessment are discussed

Empirical model for estimating groundwater flow into tunnel in discontinuous rock masses

The high volume of water inflow into tunnel plays a significant role in the design of drainage systems and exerts bio-environmental effects. In engineering practice, analytical and empirical methods that are commonly used to estimate water inflow in sedimentary rock masses, lack sufficient accuracy. The geostructural anisotropy in a fractured rock has a great impact on water inflow. In discontinuous media, anisotropy and heterogeneity of the fractured rock masses are highlited. Hence, these methods are not efficient to calculate water inflow to tunnel in such media, due to the assumed isotropic hydraulic coefficient. In this regard, an empirical formula is developed in this study for hydraulic conductivity in the fractured rock masses for analytical methods, alternately used to predict water inflow. To achieve this, a discrete network flow model was performed. The simulation resulted in a dataset that is helpful in developing hydraulic conductivity empirical formula for well-known Goodman equation. The geostructural parameters, such as the joint orientation, aperture, spacing and joint interconnectivity were included to determine this formula. The acquired empirical equation was utilized in the evaluation of groundwater inflow to middle-depth Amirkabir tunnel in north of Iran. In comparison to the observerd flow, analytical methods resulted in higher overestimation, especially in the sites with high anisotropy. However, empirical model led to a better estimation of water inflow to tunnel

Prediction of water inflow into underground excavations in fractured rocks using a 3D discrete fracture network (DFN) model

© 2017, Saudi Society for Geosciences. Groundwater flow is a major issue in underground opening in fractured rocks. Because of finding the fracture connectivity, contribution of each fracture in flow, and fracture connectivity to excavation boundary, the prediction of water flow to underground excavations is difficult. Simulation of fracture characteristics and spatial distribution is necessary to obtain realistic estimation of inflow quantity to tunnel and underground excavations. In this research, a computer code for three-dimensional discrete fracture network modeling of water inflow into underground excavations was developed. In this code, the fractures are simulated as ellipsoid while geometrical properties of the fractures are reproduced using a stochastic method. Properties such as the size, orientation, and density of the fractures are modeled by their respective probability distributions, which are obtained from field measurements. According to the fracture condition, the flow paths in rock mass are determined. The flow paths are considered as channels with rectangular sections in which channel width and fracture aperture determine geometry of channel section. Inflow into excavation is predicted ignoring matrix permeability and considering the hydrogeological conditions. To verify presented model, simulation results were compared to a part of the Cheshmeh-Roozieh water transfer tunnel in Iran. The results obtained from this research are in good agreement with the field data. Thus, the average of the predicted inflow has just an approximation error equal to 17.8%, and its standard deviation is 8.6 l/s, which is equal to 21% of the observed value that demonstrates low dispersion of the predicted values