Showing papers by "Volker Schmidt published in 2014"
TL;DR: In this article, a flat compression punch was employed to study the fiber geometry at different degrees of compression, which had a direct impact on the transport conditions for gas and fluid flow.
105 citations
TL;DR: In this paper, a large database is generated which is used to test expressions describing different micro-macro relationships such as Archie's law, tortuosity, and constrictivity equations.
Abstract: The microstructure influence on conductive transport processes is described in terms of volume fraction e, tortuosity τ, and constrictivity β. Virtual microstructures with different parameter constellations are produced using methods from stochastic geometry. Effective conductivities σeff are obtained from solving the diffusion equation in a finite element model. In this way, a large database is generated which is used to test expressions describing different micro–macro relationships such as Archie's law, tortuosity, and constrictivity equations. It turns out that the constrictivity equation has the highest accuracy indicating that all three parameters (e,τ,β) are necessary to capture the microstructure influence correctly. The predictive capability of the constrictivity equation is improved by introducing modifications of it and using error-minimization, which leads to the following expression: σeff =σ02.03e1.57β0.72/τ2 with intrinsic conductivity σ0. The equation is important for future studies in, for example, batteries, fuel cells, and for transport processes in porous materials. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1983–1999, 2014
89 citations
TL;DR: In this article, a parametrized model that describes the 3D microstructure of compressed fiber-based materials is introduced, which allows to virtually generate the microstructures of realistically compressed gas-diffusion layers (GDL).
62 citations
TL;DR: In this paper, a new algorithmic approach to segmentation of highly porous 3D image data gained by focused ion beam tomography is described which extends the key-principle of local threshold backpropagation described in Salzer et al.
36 citations
TL;DR: The data suggest that actin is able to form filamentous structures inside the nucleus, which share architectural and functional similarities with the cytoplasmic F-actin.
Abstract: Compared to the cytoplasmic F-actin abundance in cells, nuclear F-actin levels are generally quite low. However, nuclear actin is present in certain cell types including oocytes and under certain cellular conditions including stress or serum stimulation. Currently, the architecture and polymerization status of nuclear actin networks has not been analyzed in great detail. In this study, we investigated the architecture and functions of such nuclear actin networks. We generated nuclear actin polymers by overexpression of actin proteins fused to a nuclear localization signal (NLS). Raising nuclear abundance of a NLS wild-type actin, we observed phalloidin- and LifeAct-positive actin bundles forming a nuclear cytoskeletal network consisting of curved F-actin. In contrast, a polymer-stabilizing actin mutant (NLS-G15S-actin) deficient in interacting with the actin-binding protein cofilin generated a nuclear actin network reminiscent of straight stress fiber-like microfilaments in the cytoplasm. We provide a first electron microscopic description of such nuclear actin polymers suggesting bundling of actin filaments. Employing different cell types from various species including neurons, we show that the morphology of and potential to generate nuclear actin are conserved. Finally, we demonstrate that nuclear actin affects cell function including morphology, serum response factor-mediated gene expression, and herpes simplex virus infection. Our data suggest that actin is able to form filamentous structures inside the nucleus, which share architectural and functional similarities with the cytoplasmic F-actin.
32 citations
TL;DR: This work demonstrates how a stochastic network model, parametrized on atomistic simulations, can be performed by using atomic-scale (microscopic) simulations and retains the link to the material morphology and the underlying chemistry.
Abstract: Simulations of organic semiconducting devices using drift-diffusion equations are vital for the understanding of their functionality as well as for the optimization of their performance. Input parameters for these equations are usually determined from experiments and do not provide a direct link to the chemical structures and material morphology. Here we demonstrate how such a parametrization can be performed by using atomic-scale (microscopic) simulations. To do this, a stochastic network model, parametrized on atomistic simulations, is used to tabulate charge mobility in a wide density range. After accounting for finite-size effects at small charge densities, the data is fitted to the uncorrelated and correlated extended Gaussian disorder models. Surprisingly, the uncorrelated model reproduces the results of microscopic simulations better than the correlated one, compensating for spatial correlations present in a microscopic system by a large lattice constant. The proposed method retains the link to the material morphology and the underlying chemistry and can be used to formulate structure-property relationships or optimize devices prior to compound synthesis.
26 citations
TL;DR: This paper proposes an alternative to the Laguerre approach, representing grain ensembles with convex cells parametrized by orthogonal regression with respect to 3D image data, and demonstrates that the new approach represents statistical features of the underlying data—like distributions of grain sizes and coordination numbers—as well as or better than a recently introduced approximation method.
Abstract: As a straightforward generalization of the well-known Voronoi construction, Laguerre tessellations have long found application in the modelling, analysis and simulation of polycrystalline microstructures. The application of Laguerre tessellations to real (as opposed to computed) microstructures—such as those obtained by modern 3D characterization techniques like X-ray microtomography or focused-ion-beam serial sectioning—is hindered by the mathematical difficulty of determining the correct seed location and weighting factor for each of the grains in the measured volume. In this paper, we propose an alternative to the Laguerre approach, representing grain ensembles with convex cells parametrized by orthogonal regression with respect to 3D image data. Applying our algorithm to artificial microstructures and to microtomographic data sets of an Al-5 wt% Cu alloy, we demonstrate that the new approach represents statistical features of the underlying data—like distributions of grain sizes and coordination numbers—as well as or better than a recently introduced approximation method based on the Laguerre tessellation; furthermore, our method reproduces the local arrangement of grains (i.e., grain shapes and connectivities) much more accurately. The additional computational cost associated with orthogonal regression is marginal.
24 citations
TL;DR: The proposed stacking method improves the efficiency of synchrotron tomography by measuring up to ten layers in parallel, without the loss of image resolution nor quality, resulting in a maximization of acquired data.
Abstract: We present an approach for multi-layer preparation to perform microstructure analysis of a Li-ion cell anode active material using synchrotron tomography. All necessary steps, from the disassembly of differently-housed cells (pouch and cylindrical), via selection of interesting layer regions, to the separation of the graphite-compound and current collector, are described in detail. The proposed stacking method improves the efficiency of synchrotron tomography by measuring up to ten layers in parallel, without the loss of image resolution nor quality, resulting in a maximization of acquired data. Additionally, we perform an analysis of the obtained 3D volumes by calculating microstructural characteristics, like porosity, tortuosity and specific surface area. Due to a large amount of measurable layers within one stacked sample, differences between aged and pristine material (e.g., significant differences in tortuosity and specific surface area, while porosity remains constant), as well as the homogeneity of the material within one cell could be recognized.
24 citations
TL;DR: In this article, a stochastic model describing the microstructure of paper-type GDL is combined with the Lattice-Boltzmann method (LBM) to simulate gas transport within the GDL micro-structure.
Abstract: The relationship between the 3D morphology of gas-diffusion layers (GDL) of HT-PEFCs and their functionality is analyzed. A stochastic model describing the microstructure of paper-type GDL is combined with the Lattice-Boltzmann method (LBM) to simulate gas transport within the GDL microstructure. Virtual 3D microstructures representing paper-type GDL are generated by a stochastic model, where the binder morphology is systematically modified. On these structures, single phase single component gas flow is computed by the LBM. Quality criteria evaluating the spatial homogeneity of gas supply are introduced and related to the binder morphology. The spatial homogeneity of the gas supply is analyzed by a parametrized stochastic model describing the gas flow at the exit of the GDL. This approach gives insight into the spatial structure of the gas flow at the GDL exit. The quality of gas supply is quantified by characterizing size and arrangement of regions with high gas supply. This stochastic gas flow model predicts the quality of gas supply for further binder morphologies. Analyzing the quality criteria and the stochastic evaluation of the spatial structure of the gas flow field at the GDL exit, it is found that the binder morphology has an essential influence on the gas supply.
22 citations
TL;DR: This paper forms the inversion problem as a multimodal optimization problem and applies the cross-entropy method to solve it, and shows that, unlike the Voronoi case, the inverse problem is in general non-unique: different weighted generator points can create the same tessellation.
Abstract: A Laguerre tessellation is a generalization of a Voronoi tessellation where the proximity between points is measured via a power distance rather than the Euclidean distance. Laguerre tessellations have found significant applications in materials science, providing improved modeling of (poly)crystalline microstructures and grain growth. There exist efficient algorithms to construct Laguerre tessellations from given sets of weighted generator points, similar to methods used for Voronoi tessellations. The purpose of this paper is to provide theory and methodology for the inverse construction; that is, to recover the weighted generator points from a given Laguerre tessellation. We show that, unlike the Voronoi case, the inverse problem is in general non-unique: different weighted generator points can create the same tessellation. To recover pertinent generator points, we formulate the inversion problem as a multimodal optimization problem and apply the cross-entropy method to solve it.
20 citations
TL;DR: It is found that the hierarchical titania nanostructure facilitates polymer infiltration, thus favoring intermixing of the two semiconducting phases, essential for charge separation.
Abstract: A quantitative method for the characterization of nanoscale 3D morphology is applied to the investigation of a hybrid solar cell based on a novel hierarchical nanostructured photoanode. A cross section of the solar cell device is prepared by focused ion beam milling in a micropillar geometry, which allows a detailed 3D reconstruction of the titania photoanode by electron tomography. It is found that the hierarchical titania nanostructure facilitates polymer infiltration, thus favoring intermixing of the two semiconducting phases, essential for charge separation. The 3D nanoparticle network is analyzed with tools from stochastic geometry to extract information related to the charge transport in the hierarchical solar cell. In particular, the experimental dataset allows direct visualization of the percolation pathways that contribute to the photocurrent.
TL;DR: A general framework is proposed for the study of the charge transport properties of materials via random walks in random environments (RWRE), which combines a model for the fast generation of random environments that realistically mimic materials morphology with an algorithm for efficient estimation of key properties of the resulting random walk.
Abstract: A general framework is proposed for the study of the charge transport properties of materials via random walks in random environments (RWRE). The material of interest is modeled by a random environment, and the charge carrier is modeled by a random walker. The framework combines a model for the fast generation of random environments that realistically mimic materials morphology with an algorithm for efficient estimation of key properties of the resulting random walk. The model of the environment makes use of tools from spatial statistics and the theory of random geometric graphs. More precisely, the disordered medium is represented by a random spatial graph with directed edge weights, where the edge weights represent the transition rates of a Markov jump process (MJP) modeling the motion of the random walker. This MJP is a multiscale stochastic process. In the long term, it explores all vertices of the random graph model. In the short term, however, it becomes trapped in small subsets of the state space a...
01 Mar 2014
TL;DR: A possible solution to this problem is to use linguistic distances rather than geographical distances in the estimation, which leads to maps which render geolinguistic distributions more faithfully, especially in areas that are deemed critical for the interpretation of the resulting maps and for subsequent statistical analyses of the results.
Abstract: Dialectometric intensity estimation as introduced in Rumpf etal. (2009) and Pickl and Rumpf (2011, 2012) is a method for the unsupervised generation of maps visualizing geolinguistic data on the level of linguistic variables. It also extracts spatial information for subsequent statistical analysis. However, as intensity estimation involves geographically conditioned smoothing, this method can lead to undesirable results. Geolinguistically relevant structures such as rivers, political borders or enclaves, for instance, are not taken into account and thus their manifestations in the distributions of linguistic variants are blurred. A possible solution to this problem, as suggested and put to the test in this paper, is to use linguistic distances rather than geographical (Euclidean) distances in the estimation. This methodological adjustment leads to maps which render geolinguistic distributions more faithfully, especially in areas that are deemed critical for the interpretation of the resulting maps and for subsequent statistical analyses of the results.
TL;DR: Aggregate Monte Carlo (AMC) as mentioned in this paper is a fast alternative to the standard CMC algorithm, which is suitable for efficient simulation of continuous-time Markov chains that are nearly-completely decomposable.
Abstract: A methodology is proposed that is suitable for efficient simulation of continuous-time Markov chains that are nearly-completely decomposable. For such Markov chains the effort to adequately explore the state space via Crude Monte Carlo (CMC) simulation can be extremely large. The purpose of this paper is to provide a fast alternative to the standard CMC algorithm, which we call Aggregate Monte Carlo (AMC). The idea of the AMC algorithm is to reduce the jumping back and forth of the Markov chain in small subregions of the state space. We accomplish this by aggregating such problem regions into single states. We discuss two methods to identify collections of states where the Markov chain may become ‘trapped’: the stochastic watershed segmentation from image analysis, and a graph-theoretic decomposition method. As a motivating application, we consider the problem of estimating the charge carrier mobility of disordered organic semiconductors, which contain low-energy regions in which the charge carrier can quickly become stuck. It is shown that the AMC estimator for the charge carrier mobility reduces computational costs by several orders of magnitude compared to the CMC estimator.
TL;DR: In this paper, a set of computationally generated granular packings of frictionless grains is statistically analyzed using tools from stochastic geometry, and the authors consider both the graph of the solid phase (formed using the particle mid-points) and the pore-phase.
Abstract: A set of computationally generated granular packings of frictionless grains is statistically analyzed using tools from stochastic geometry. We consider both the graph of the solid phase (formed using the particle mid-points) and the pore-phase. Structural characteristics rooted in the analysis of random point processes are seen to yield valuable insights into the underlying structure of granular systems. The graph of the solid phase is analyzed using traditional measures such as edge length and coordination number, as well as more instructive measures of the overall transport properties such as geometric tortuosity, where significant differences are observed in the windedness of paths through the different particle graphs considered. In contrast, the distributions of pore-phase characteristics have a similar shape for all considered granular packings. Interestingly, it is found that prolate and oblate ellipsoid packings show a striking similarity between their solid-phase graphs as well as between their pore-phase graphs.
TL;DR: In this paper, a stochastic algorithm for acceptance and rejection of simulated cyclone tracks with landfall is proposed, which is based on the fact that the locations, translational speeds, and wind speeds of cyclones at landfall constitute three-dimensional Poisson point processes, and a well-known thinning property of Poisson processes can be applied.
Abstract: We consider a spatial stochastic model for the simulation of tropical cyclone tracks, which has recently been introduced. Cyclone tracks are represented as labeled polygonal lines, which are described by the movement directions, translational speeds, and wind speeds of the cyclones in regular 6-h intervals. In the present paper, we compare return levels for wind speeds of historically observed cyclone tracks with those generated by the simulator, where a mismatch is shown for most of the considered coastal regions. To adjust this discrepancy, we develop a stochastic algorithm for acceptance and rejection of simulated cyclone tracks with landfall. It is based on the fact that the locations, translational speeds, and wind speeds of cyclones at landfall constitute three-dimensional Poisson point processes, which are a basic model type in stochastic geometry. Due to that, a well-known thinning property of Poisson processes can be applied. This means that to each simulated cyclone, an acceptance probability is assigned, which is higher for cyclones with suitable landfall characteristics and lower for implausible ones. More intuitively, the algorithm comprises the simulation of a more comprehensive cyclone event set than needed and the random selection of those tracks that best match historical observations at landfall. A particular advantage of our algorithm is its applicability to multiple landfalls, i.e., to cyclones that successively make landfall at two geographically distinct coastlines, which is the most relevant case in applications. It turns out that the extended simulator provides a much better accordance between landfall characteristics of historical and simulated cyclone tracks.
TL;DR: In this paper, the authors consider spatially homogeneous marked point patterns in an unboundedly expanding convex sampling window and construct an asymptotic goodness-of-fit test to identify the distribution of the typical mark.
Abstract: We consider spatially homogeneous marked point patterns in an unboundedly expanding convex sampling window. Our main objective is to identify the distribution of the typical mark by constructing an asymptotic $\chi^2$-goodness-of-fit test. The corresponding test statistic is based on a natural empirical version of the Palm mark distribution and a smoothed covariance estimator which turns out to be mean square consistent. Our approach does not require independent marks and allows dependences between the mark field and the point pattern. Instead we impose a suitable $\beta$-mixing condition on the underlying stationary marked point process which can be checked for a number of Poisson-based models and, in particular, in the case of geostatistical marking. In order to study test performance, our test approach is applied to detect anisotropy of specific Boolean models.
TL;DR: An explicit description of the Palm version of Poisson–Delaunay tessellations (PDT) is developed, which provides a new direct simulation algorithm for the typical Cox–Voronoi cell based on PDT.
Abstract: Distributional properties and a simulation algorithm for the Palm version of stationary iterated tessellations are considered. In particular, we study the limit behaviour of functionals related to Cox–Voronoi cells (such as typical shortest-path lengths) if either the intensity γ0 of the initial tessellation or the intensity γ1 of the component tessellation converges to 0. We develop an explicit description of the Palm version of Poisson–Delaunay tessellations (PDT), which provides a new direct simulation algorithm for the typical Cox–Voronoi cell based on PDT. It allows us to simulate the Palm version of stationary iterated tessellations, where either the initial or component tessellation is a PDT and can furthermore be used in order to show numerically that the qualitative and quantitative behaviour of certain functionals related to Cox–Voronoi cells strongly depends on the type of the underlying iterated tessellation.
TL;DR: In this article, the authors consider spatially homogeneous marked point patterns in an unboundedly expanding convex sampling window and construct an asymptotic -2 -goodness-of-fit test.
Abstract: We consider spatially homogeneous marked point patterns in an unboundedly expanding convex sampling window. Our main objective is to identify the distribution of the typical mark by constructing an asymptotic � 2 -goodness-of-fit test. The corresponding test statistic is based on a natural empirical version of the Palm mark distribution and a smoothed covariance estimator which turns out to be mean-square consistent. Our approach does not require independent marks and allows dependences between the mark field and the point pattern. Instead we impose a suitable �-mixing condition on the underlying stationary marked point process which can be checked for a number of Poissonbased models and, in particular, in the case of geostatistical marking. Our method needs a central limit theorem for �-mixing random fields which is proved by extending Bernstein’s blocking technique to non-cubic index sets and seems to be of interest in its own right. By large-scale model-based simulations the performance of our test is studied in dependence of the model parameters which determine the range of spatial correlations.
TL;DR: In this paper, the authors carried out Monte Carlo experiments to study the scaling behavior of shortest path lengths in continuum percolation and found that the critical exponent governing this scaling is the same for both continuum and lattice percolations.
Abstract: We carry out Monte Carlo experiments to study the scaling behavior of shortest path lengths in continuum percolation. These studies suggest that the critical exponent governing this scaling is the same for both continuum and lattice percolation. We use splitting, a technique that has not yet been fully exploited in the physics literature, to increase the speed of our simulations. This technique can also be applied to other models where clusters are grown sequentially.
07 Dec 2014
TL;DR: This paper presents a conditional Monte Carlo algorithm for the estimation of the probability that random graphs related to Bernoulli and continuum percolation are connected andumerical results are presented showing that the unconditional Monte Carlo estimators significantly outperform the crude simulation estimators.
Abstract: Spatial statistical models are of considerable practical and theoretical interest. However, there has been little work on rare-event probability estimation for such models. In this paper we present a conditional Monte Carlo algorithm for the estimation of the probability that random graphs related to Bernoulli and continuum percolation are connected. Numerical results are presented showing that the conditional Monte Carlo estimators significantly outperform the crude simulation estimators.
TL;DR: It is shown how regional prediction of car insurance risks can be improved for finer subregions by combining explanatory modeling with phenomenological models from industrial practice and a non-parametric random forest approach may be used to practically compute such predictors.
Abstract: We show how regional prediction of car insurance risks can be improved by combining explanatory modeling with phenomenological models from industrial practice. Motivated by the control-variates technique, we propose a suitable combined predictor. We provide explicit conditions which imply that the mean squared error of the combined predictor is smaller than the mean squared error of the standard predictor currently used in industry and smaller than predictors from explanatory modeling. We also discuss how a non-parametric random forest approach may be used to practically compute such predictors and consider an application to German car insurance data.
01 Jan 2014
TL;DR: In this paper, an approach for multi-layer preparation to perform microstructure analysis of a Li-ion cell anode active material using synchrotron tomography is presented, where all necessary steps from disassembly of differently-housed cells (pouch and cylindrical), via selectionof interesting layer regions, to the separation of the graphite-compound and current collector, are described in detail.
Abstract: Daimler AG, HPC H152, Mercedesstr. 137, Stuttgart 70367, Germany;E-Mail: andreas.hintennach@daimler.com (A.H.)* Author to whom correspondence should be addressed; E-Mail: tim.mitsch@daimler.com;Tel.: +49-176-3090-5697; Fax: +49-711-3052-123-291.Received: 31 March 2014; in revised form: 26 May 2014 / Accepted: 28 May 2014 /Published: 12 June 2014Abstract: We present an approach for multi-layer preparation to perform microstructureanalysis of a Li-ion cell anode active material using synchrotron tomography. All necessarysteps, from the disassembly of differently-housed cells (pouch and cylindrical), via selectionof interesting layer regions, to the separation of the graphite-compound and current collector,are described in detail. The proposed stacking method improves the efficiency of synchrotrontomography by measuring up to ten layers in parallel, without the loss of image resolutionnor quality, resulting in a maximization of acquired data. Additionally, we perform ananalysis of the obtained 3D volumes by calculating microstructural characteristics, likeporosity, tortuosity and specific surface area. Due to a large amount of measurable layerswithin one stacked sample, differences between aged and pristine material (e.g., significantdifferences in tortuosity and specific surface area, while porosity remains constant), as wellas the homogeneity of the material within one cell could be recognized.