We present simulation results of flows in the finite Knudsen range, which is in the slip and transition flow regime. Our implementations are based on the Lattice Boltzmann method and are accomplished within the Peano framework. We validate our code by solving two-and three-dimensional channel flow problems and compare our results with respective experiments from other research groups. We further apply our Lattice Boltzmann solver to the geometrical setup of a microreactor consisting of differently sized channels and a reactor chamber. Here, we apply static adaptive grids to further reduce computational costs. We further investigate the influence of using a simple BGK collision kernel in coarse grid regions which are further away from the slip boundaries. Our results are in good agreement with theory and non-adaptive simulations, demonstrating the validity and the capabilities of our adaptive simulation software for flow problems at finite Knudsen numbers.
With approx. 6 million macro cells worldwide and a gross energy consumption of approx. 100 TWh per year as of 2013, mobile networks are one of the major energy consumers in the ICT sector. As trends, such as cloud-based services and other traffic-intensive mobile applications, fuel the growth of mobile traffic demands, operators of mobile telephony networks are forced to continuously extend the capacity of the existing infrastructure by both implementing new technologies as well as by installing new cell towers to provide more bandwidth for mobile users and improve the network's coverage. In order to implement energy-efficient reconfiguration mechanisms in mobile telephony networks as proposed by the project Communicate Green, it is essential to anticipate traffic hotspots, so that a network's configuration can be adjusted in time accordingly. Hence, predicting the movement of mobile users on a cellular level of the mobile network is a crucial task. In this paper, we propose a Movement Prediction System based on the algorithm of Yavas et al. that allows to determine the future movement of a user on a cellular level using precomputed movement patterns. We extended the algorithm to be capable of preselecting patterns based on time and contextual data in order to improve prediction accuracy. The performance of the algorithm is evaluated based on real and live user movement data from the OpenMobileNetwork, which is a platform providing estimated mobile network topology data. We found that the algorithm's prediction quality is sufficient, but requires an extensive amount of recorded user movements to perform well.
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