Particle clogging in the artificial groundwater recharge process is one of the main factors influencing the artificial groundwater recharge efficiency, and particle deposition is the microscopic mechanism of the occurrence and development of particle clogging. Particle deposition in porous media changes the pore structure. The computed tomography (CT) scanning technique is a nondestructive testing method and determines the spatial distribution of pores in porous media. This study combines physical and CT scanning experiments to identify the change process of the pore structure in the artificial groundwater recharge process and compares the pore changes during recharge experiments between two columns containing different media. Porous media changes are observed with the CT scanning technique. The fractal theory is applied in the analysis of CT scan images and physical experiment results. The results of this study indicate that particle deposition can be examined by using CT scan images to obtain pore-related parameters, the internal pore structure of porous media determined through CT scan images can be applied in numerical simulation, and a mathematical model for particle deposition calculation in porous media is established. Compared to the physical experiment measurements, the spatial particle deposition information acquired with the CT scanning technique exhibits a higher accuracy and contains much more relevant data. Not only does this research reveal more clearly the particle clogging mechanism which is based on particle deposition, but also characterize, simulate and predict more accurately the development tendency of particle clogging during artificial groundwater recharge.
This paper presents the assessment of sustainable yield in the Huaibei karst water area of Anhui province, China. A review
of sustainable yield definition is introduced first in this paper, and sustainable development in karst areas is more difficult
due to the complicated hydrogeologic conditions. General hydrogeology of the study area is provided to characterize hydraulic
connections between the karst aquifer and an overlying porous aquifer. Groundwater level declines continuously due to over-exploitation
of the karst groundwater, and two layers of groundwater dropping funnel were formed in Huaibei. These problems not only threaten
the eco-geo-environment, but also compromise the water utilization which depends on the shallow porous water. A “critical
water level” is proposed in this study to assess the sustainable yield, and it is determined by the historical exploitation
data which represent the relationship between the karst water and the shallow porous water uses. A three layer Artificial
Neural Network (ANN) model is used to understand the complex relationship of the karst water level and its influencing factors.
Precipitation, exploitation and water level of latest period are chosen as the input nodes, seasonal records of water level
are simulated by the ANN model. The sustainable yield is calculated by the trail-and-error adjusting method, and is equal
to the pumping rate when the “critical water level” is maintained. The rate of 30.05 MCM/a is the sustainable yield for the
Huaibei karst area in 2008, and it is less than the real pumping rate of 35.92 MCM/a. This assessment is meaningful to the
management for the Huaibei karst water. 相似文献