Analysis of a long-term pumping and tracer test using the channel network model

1998 ◽  
Vol 32 (3-4) ◽  
pp. 203-222 ◽  
Author(s):  
B Gylling ◽  
L Birgersson ◽  
L Moreno ◽  
I Neretnieks
1999 ◽  
Vol 556 ◽  
Author(s):  
B. Khademi ◽  
L. Moreno ◽  
I. Neretnieks

AbstractThe Channel Network model describes the fluid flow and solute transport in fractured media. The model is based on field observations, which indicate that flow and transport take place in a threedimensional network of connected channels. The channels are generated in the model from observed stochastic distributions and solute transport is modelled taking into account advection and rock interactions, such as matrix diffusion and sorption within the rock. The most important site-specific data for the Channel Network model are the conductance distribution of the channels and the flow-wetted surface. The latter is the surface area of the rock in contact with the flowing water. These parameters may be estimated from hydraulic measurements. For the Äspö site, several borehole data sets are available, where a packer distance of 3 metres was used. Numerical experiments were performed in order to study the uncertainties in the determination of the flowwetted surface and conductance distribution. Synthetic data were generated along a borehole and hydraulic tests with different packer distances were simulated.The model has previously been used to study the Long-term Pumping and Tracer Test (LPT2) carried out in the Äspö Hard Rock Laboratory (HRL) in Sweden, where the distance travelled by the tracers was of the order hundreds of metres. Recently, the model has been used to simulate the tracer tests performed in the TRUE experiment at HRL, with travel distance of the order of tens of metres. Several tracer tests with non-sorbing and sorbing species have been performed.


1988 ◽  
Vol 114 (3) ◽  
pp. 424-441 ◽  
Author(s):  
Tahir Husain ◽  
Walid A. Abderrahman ◽  
Hasin U. Khan ◽  
Suhail M. Khan ◽  
Asfahan U. Khan ◽  
...  

2012 ◽  
Vol 452-453 ◽  
pp. 700-704
Author(s):  
Feng Rong Zhang ◽  
Annik Magerholm Fet ◽  
Xin Wei Xiao

At present, the domestic research on the scale of macroscopic logistics has yet belonged to the blankness, therefore, this research tries using LV in circulation and LV in stock to measure the logistics volume and forecasting it in a long period. In order to overcome the phenomenon of “floating upward” in long-term period, this paper establish the improved Grey RBF to forecast the LV next 5-10 year in Jilin province of China. The results show that the increased circulation of goods is the main reason leading to increased logistics volume, and the simulation also shows that the improved gray RBF neural network model is a good method for the government to establish the logistics development policy.


2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yatong Chen ◽  
Huangxun Chen ◽  
Shuo Yang ◽  
Xiaofeng Gao ◽  
Yunhe Guo ◽  
...  

In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, and transportation. However, most existing mobile crowdsensing systems lack vast user bases and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale and provide long-term guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform and provide a solution to cope with the problem of user overlapping among intermediaries. We theoretically prove the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency. Furthermore, we evaluate our incentive mechanisms by extensive simulations. Evaluation results validate the effectiveness and efficiency of our proposed mechanisms.


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