Morphological characterization of a polymeric microfiltration membrane by synchrotron radiation computed microtomography

Most commercial polymeric membranes are prepared by phase inversion. The performance of the membranes depends greatly on the morphology of the porous structure formed during the different steps of this process. Researchers in this field have found it extremely difficult to foresee how a change in the composition of the polymer solution will affect pore formation without a set of methods designed to yield detailed knowledge of the morphological structure. This paper reports the new potential associated with X-Ray synchrotron microtomography to characterize the 3D structure of a PvDF hollow fibre microfiltration membrane prepared by phase inversion. 3D morphological data obtained from the ID19 line at the ESRF are presented. The membrane actually appears as a complex three-dimensional bi-continuum of interconnected pores. Within the hollow fibre structure, different regions with various thicknesses and pore size distributions have been identified and well characterized. Transversal views show the anisotropic finger-like structure of pores, while longitudinal sections reveal a honeycomb structure which resembles the structure of highly concentrated water in oil emulsion or dispersion. This typical structure might be obtained during the phase inversion process. How the phase inversion process may result in these morphologies is finally discussed

Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of computing resources. The pressure to maintain reasonable levels of performance and portability forces application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical manycore architectures. In this paper, we study the benefits and limits of replacing the highly specialized internal scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them the opportunity to process and optimize its traversal in order to maximize the algorithm efficiency for the targeted hardware platform. A comparative study of the performance of the PaStiX solver on top of its native internal scheduler, PaRSEC, and StarPU frameworks, on different execution environments, is performed. The analysis highlights that these generic task-based runtimes achieve comparable results to the application-optimized embedded scheduler on homogeneous platforms. Furthermore, they are able to significantly speed up the solver on heterogeneous environments by taking advantage of the accelerators while hiding the complexity of their efficient manipulation from the programmer.Comment: Heterogeneity in Computing Workshop (2014

Accurate method for calculating currents in wires in the vicinity of curved geometries

International audiencePrecise methods to calculate currents are required for low frequency EMC simulations dealing with vehicles struck by lightning. The current model used resolves Maxwell’s equations combined with a Line model based on Holland’s thin wire formalism [1]. The challenge is related to the approximation of the source fields obtained with Yee’s scheme [2]. These sources are then used for the thin wire equations. In the vicinity of structures, the errors due to the staircase meshes representing surfaces corrupt the fields’ values. In order to bypass this issue, it was suggested to apply non structured meshes such as Finite Volume (FV) [3]. Difficulties are encountered when introducing thin oblique wires [4] in this last approach, in particular for the calculation of the local self inductance L, a numerical parameter required by the line model equations.In choosing a FV solver, difficulties will arise in terms of calculation resources due to the calculation procedure of the latter and to the unstructuredness of the meshes. To overcome this obstacle, a hybrid Non Structured-Structured (NST-ST) FV scheme which can also incorporate oblique Line models is proposed.To illustrate the advantage of this new approach, an open cylindrical structure with wires running along its walls will be taken into account. It will be illuminated by a plane wave and we shall compare the obtained results in terms of current and field values retrieved inside and also in the vicinity of the cables

Iron-catalyzed hydrosilylation of CO2: CO2 conversion to formamides and methylamines

International audienceno abstrac

The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation

Comparing metric measure spaces (i.e. a metric space endowed with aprobability distribution) is at the heart of many machine learning problems. The most popular distance between such metric measure spaces is theGromov-Wasserstein (GW) distance, which is the solution of a quadratic assignment problem. The GW distance is however limited to the comparison of metric measure spaces endowed with a probability distribution.To alleviate this issue, we introduce two Unbalanced Gromov-Wasserstein formulations: a distance and a more tractable upper-bounding relaxation.They both allow the comparison of metric spaces equipped with arbitrary positive measures up to isometries. The first formulation is a positive and definite divergence based on a relaxation of the mass conservation constraint using a novel type of quadratically-homogeneous divergence. This divergence works hand in hand with the entropic regularization approach which is popular to solve large scale optimal transport problems. We show that the underlying non-convex optimization problem can be efficiently tackled using a highly parallelizable and GPU-friendly iterative scheme. The second formulation is a distance between mm-spaces up to isometries based on a conic lifting. Lastly, we provide numerical experiments onsynthetic examples and domain adaptation data with a Positive-Unlabeled learning task to highlight the salient features of the unbalanced divergence and its potential applications in ML