University of Paderborn

About: University of Paderborn is a education organization based out in Paderborn, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Control reconfiguration & Software. The organization has 6684 authors who have published 16929 publications receiving 323154 citations.

Mobility in wireless networks

Abstract: This article surveys mobility patterns and mobility models for wirelss networks. Mobility patterns are classified into the following types: pedestrians, vehicles, aerial, dynamic medium, robot, and outer space motion. We present the characteristics of each and shortly mention the specific problems. We shortly present the specifics of cellular networks, mobile ad hoc networks, and sensor networks regarding mobility. Then, we present the most important mobility models from the literature. At last we give a brief discussion about the state of research regarding mobility in wireless networks.

VOF-Simulation of the Lift Force for Single Bubbles in a Simple Shear Flow

Abstract: Bubbles in shear flows experience a lift force, causing them to migrate sideways while they are rising. This lateral migration is investigated in numerical simulations, which are carried out with an extended version of the highly parallelized code FS3D, employing an advanced Volume-of-Fluid method. The movement of single bubbles in linear shear flows is simulated to obtain the magnitude of the lift force - expressed by the lift force coefficient C L - for various bubble diameters and material data. Simulation results are in good agreement with experiments for medium liquid phase viscosities. An investigation of the dynamic pressure on the bubble surface explains why large bubbles migrate in the opposite direction compared to small bubbles.

On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems

Abstract: In this report we describe how continuation methods can be used for the numerical treatment of multi-objective optimization problems (MOPs): starting with a given Karush-Kuhn-Tucker point (KKT-point) x of an MOP, these techniques can be applied to detect further KKT-points in the neighborhood of x. In the next step, again further points are computed starting with these new-found KKT-points, and so on. In order to maintain a good spread of these solutions we use boxes for the representation of the computed parts of the solution set. Based on this background, we propose a new predictor-corrector variant, and show some numerical results indicating the strength of the method, in particular in higher dimensions. Further, the data structure allows for an efficient computation of MOPs with more than two objectives, which has not been considered so far in most existing continuation methods.