2,047 research outputs found

    Multiscale simulations of porous media flows in flow-based coordinate system

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    In this paper, we propose a multiscale technique for the simulation of porous media flows in a flow-based coordinate system. A flow-based coordinate system allows us to simplify the scale interaction and derive the upscaled equations for purely hyperbolic transport equations. We discuss the applications of the method to two-phase flows in heterogeneous porous media. For two-phase flow simulations, the use of a flow-based coordinate system requires limited global information, such as the solution of single-phase flow. Numerical results show that one can achieve accurate upscaling results using a flow-based coordinate system

    Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

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    A variety of methods is available to quantify uncertainties arising with\-in the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by theoretical statistical distributions since only very limited data is available. Hence, exact information on distribution shapes for all uncertain parameters is very rare in realistic applications. We discuss and compare four different methods tested for data-driven uncertainty quantification based on a benchmark scenario of carbon dioxide storage. In the benchmark, for which we provide data and code, carbon dioxide is injected into a saline aquifer modeled by the nonlinear capillarity-free fractional flow formulation for two incompressible fluid phases, namely carbon dioxide and brine. To cover different aspects of uncertainty quantification, we incorporate various sources of uncertainty such as uncertainty of boundary conditions, of conceptual model definitions and of material properties. We consider recent versions of the following non-intrusive and intrusive uncertainty quantification methods: arbitary polynomial chaos, spatially adaptive sparse grids, kernel-based greedy interpolation and hybrid stochastic Galerkin. The performance of each approach is demonstrated assessing expectation value and standard deviation of the carbon dioxide saturation against a reference statistic based on Monte Carlo sampling. We compare the convergence of all methods reporting on accuracy with respect to the number of model runs and resolution. Finally we offer suggestions about the methods' advantages and disadvantages that can guide the modeler for uncertainty quantification in carbon dioxide storage and beyond

    A Framework for Modeling Subgrid Effects for Two-Phase Flows in Porous Media

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    In this paper, we study upscaling for two-phase flows in strongly heterogeneous porous media. Upscaling a hyperbolic convection equation is known to be very difficult due to the presence of nonlocal memory effects. Even for a linear hyperbolic equation with a shear velocity field, the upscaled equation involves a nonlocal history dependent diffusion term, which is not amenable to computation. By performing a systematic multiscale analysis, we derive coupled equations for the average and the fluctuations for the two-phase flow. The homogenized equations for the coupled system are obtained by projecting the fluctuations onto a suitable subspace. This projection corresponds exactly to averaging along streamlines of the flow. Convergence of the multiscale analysis is verified numerically. Moreover, we show how to apply this multiscale analysis to upscale two-phase flows in practical applications

    Coupling of cytoplasm and adhesion dynamics determines cell polarization and locomotion

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    Observations of single epidermal cells on flat adhesive substrates have revealed two distinct morphological and functional states, namely a non-migrating symmetric unpolarized state and a migrating asymmetric polarized state. These states are characterized by different spatial distributions and dynamics of important biochemical cell components: F-actin and myosin-II form the contractile part of the cytoskeleton, and integrin receptors in the plasma membrane connect F-actin filaments to the substratum. In this way, focal adhesion complexes are assembled, which determine cytoskeletal force transduction and subsequent cell locomotion. So far, physical models have reduced this phenomenon either to gradients in regulatory control molecules or to different mechanics of the actin filament system in different regions of the cell. Here we offer an alternative and self-organizational model incorporating polymerization, pushing and sliding of filaments, as well as formation of adhesion sites and their force dependent kinetics. All these phenomena can be combined into a non-linearly coupled system of hyperbolic, parabolic and elliptic differential equations. Aim of this article is to show how relatively simple relations for the small-scale mechanics and kinetics of participating molecules may reproduce the emergent behavior of polarization and migration on the large-scale cell level.Comment: v2 (updates from proof): add TOC, clarify Fig. 4, fix several typo

    Global gbest guided-artificial bee colony algorithm for numerical function optimization

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    Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing

    A locally adaptive time-stepping algorithm for\ud petroleum reservoir simulations

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    An algorithm for locally adapting the step-size for large scale finite volume simulations of multi-phase flow in petroleum reservoirs is suggested which allows for an “all-in-one” implicit calculation of behaviour over a very large time scale. Some numerical results for simple two-phase flow in one space dimension illustrate the promise of the algorithm, which has also been applied to very simple 3D cases. A description of the algorithm is presented here along with early results. Further development of the technique is hoped to facilitate useful scaling properties
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