The theory of active contours is applied to microscopic images of cells. A model is developed that approximates cell borders by dynamic curves. This model is based on gradient vector flow (GVF), an external force that acts on the contours. Both active contours and the GVF force field are defined as functions that minimize certain functionals. The corresponding Euler-Lagrange equations are derived and analyzed theoretically. A number of auxiliary algorithms are designed to aid the performance of the main snake algorithm, including a preprocessing algorithm, a method for detecting cell centers and an algorithm that detects areas devoid of cells. Results of the snake algorithm are presented, along with practical considerations regarding parameter choice. Finally, statistical methods are applied to the results to demonstrate their usefulness.

Kitepower is a start up that generates energy using kites. The Kitepower system consists of airborne components, and a ground station where a drum is connected to a generator/electrical motor. For generating energy a so called pumping cycle is performed. In a pumping cycle energy is generated during the traction phase when the kite pulls the drum, and some of this generated energy gets consumed during the retraction phase when the kite is reeled in. The net energy is stored in a rechargeable battery pack. In this thesis a model and a control is derived for the ground station of the Kitepower system, for the purpose of making physically realistic simulations. The Kitepower system is divided into a kite system and a ground station system. For the kite system already a proper quasi-steady model exists, but in order to do simple kite simulations and analyses a simplified version of this kite model is derived in chapter 3. In chapter 4 a ground station model is derived by using basic physical concepts and in chapter 5 a velocity and a force control are proposed for the ground station model. The derived ground station model and controls are implemented with Python, and incorporated in the kitepower software in order to make simulations of a full pumping cycle. In the result chapter 6 simulations are being done with and without using the ground station model. Both the proposed velocity and the force control are used for the simulations. The simulations are compared on physi- cally realistic behavior and validated with experimental data.

If secondary hydrocarbon recovery methods, like water flooding, fail because of the occurrence of viscous fingering one can turn to an enhanced oil recovery method (EOR) like the injection of foam. The generation of foam in a porousmedium can be described by a set of partial differential equations with strongly non-linear functions, which impose challenges for the numerical modeling. Former studies [1–3] show the occurrence of strongly temporally oscillating solutions when using forward simulation models, that are entirely due to discretization artifacts. We describe the foam process by an immiscible two-phase flow model where gas is injected in a porousmedium filled with a mixture of water and surfactants. The change from pure gas into foam is incorporated in the model through a reduction in the gas mobility. Hence, the two-phase description of the flow stays intact. Since the total pressure drop in the reservoir is small, both fluids can be considered incompressible [3]. However, whereas the fractional flow function for a gas-flooding process is a smooth function of water saturation, the generation of foam will cause a rapid increase of the flux function over a very small saturation scale. Consequently, the derivatives of the flux function can become extremely large and impose a severe constraint on the time step. We address the stability issues of the foam model, by numerous numerical approaches that improve the accuracy of the solutions. First, we study several averaging schemes and introduce a novel way of approximating the foam mobility functions on the grid interfaces in a finite volume framework. This will lead to solutions that are significantly smoother than can be achieved with standard averaging schemes. Next, we discuss several novel discretization schemes where the discontinuity is incorporated in the numerical fluxes for a simplified compressible flow model. These include the indirect addition of an extra grid interface at the location of the discontinuity, to preserve monotonicity of the solutions in time. Variations on this method, are the addition of an extra grid cell around the highly non-linear phase transition and the adaption of the flux terms based on the location of the discontinuity or non-linearity in the grid. As a practical example to demonstrate these techniques we study a simplified model for foam flow in porous media. The model is then extended to a two-dimensional reservoir, where the accuracy of the solutions is a main concern. The two-dimensional simulator that is used for this, was build and tested for the foam model. It includes higher-order hyperbolic Riemann solvers, and flux correction schemes to compute the saturation of the different fluid phases in the model. The elliptic solver for the pressure equation is also adapted to the stiffness of the problem. With this simulator we perform a quantitative study of the stability characteristics of the flow, to gain more insight in the important wave-lengths and scales of the foam model. This insight forms an essential step towards the design of a suitable computational solver that captures all the appropriate scales, while retaining computational efficiency. In addition, we present a qualitative analysis of the effect of different reservoir and fluid properties on the foam fingering behavior. In particular, we consider the effect of heterogeneity of the reservoir, injection rates, and foam quality. This leads to interesting observations about the influence of the different foam parameters on the stability of the solutions, and we are able to predict the flow stability for different foam qualities. Finally, we discuss several other approaches that were addressed during this PhD-project to increase the understanding of solving highly non-linear flow problems in a porous medium. @en