64 research outputs found

    Three-dimensional CFD simulations with large displacement of the geometries using a connectivity-change moving mesh approach

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    This paper deals with three-dimensional (3D) numerical simulations involving 3D moving geometries with large displacements on unstructured meshes. Such simulations are of great value to industry, but remain very time-consuming. A robust moving mesh algorithm coupling an elasticity-like mesh deformation solution and mesh optimizations was proposed in previous works, which removes the need for global remeshing when performing large displacements. The optimizations, and in particular generalized edge/face swapping, preserve the initial quality of the mesh throughout the simulation. We propose to integrate an Arbitrary Lagrangian Eulerian compressible flow solver into this process to demonstrate its capabilities in a full CFD computation context. This solver relies on a local enforcement of the discrete geometric conservation law to preserve the order of accuracy of the time integration. The displacement of the geometries is either imposed, or driven by fluid–structure interaction (FSI). In the latter case, the six degrees of freedom approach for rigid bodies is considered. Finally, several 3D imposed-motion and FSI examples are given to validate the proposed approach, both in academic and industrial configurations

    French database of children and adolescents with Prader-Willi syndrome

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    <p>Abstract</p> <p>Background</p> <p>Prader-Willi syndrome (PWS) is a rare multisystem genetic disease leading to severe complications mainly related to obesity. We strongly lack information on the natural history of this complex disease and on what factors are involved in its evolution and its outcome. One of the objectives of the French reference centre for Prader-Willi syndrome set-up in 2004 was to set-up a database in order to make the inventory of Prader-Willi syndrome cases and initiate a national cohort study in the area covered by the centre.</p> <p>Description</p> <p>the database includes medical data of children and adolescents with Prader-Willi syndrome, details about their management, socio-demographic data on their families, psychological data and quality of life of the parents. The tools and organisation used to ensure data collection and data quality in respect of good clinical practice procedures are discussed, and main characteristics of our Prader-Willi population at inclusion are presented.</p> <p>Conclusion</p> <p>this database covering all the aspects of PWS clinical, psychological and social profiles, including familial psychological and quality of life will be a powerful tool for retrospective studies concerning this complex and multi factorial disease and could be a basis for the design of future prospective multicentric studies. The complete database and the Stata.do files are available to any researcher wishing to use them for non-commercial purposes and can be provided upon request to the corresponding author.</p

    Finite element setting for fluid flow simulations with natural enforcement of the triple junction equilibrium

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    International audienceCapillary phenomena are involved in many industrial processes, especially those dealing with composite manufacturing. However, their modelling is still challenging. Therefore, a finite element setting is proposed to better investigate this complex issue. The variational formulation of a liquid-air Stokes' system is established, while the solid substrate is described through boundary conditions. Expressing the weak form of Laplace's law over liquid-air, liquid-solid and air-solid interfaces, leads to a natural enforcement of the mechanical equilibrium over the wetting line, without imposing explicitly the contact angle itself. The mechanical problem is discretised by using finite elements, linear both in velocity and pressure, stabilised with a variational multiscale method, including the possibility of enrichment of the pressure space. The moving interface is captured by a Level-Set methodology, combined with a mesh adaptation technique with respect to both pressure and level-set fields. Our methodology can simulate capillary-driven flows in 2D and 3D with the desired precision: droplet spreading, droplet coalescence, capillary rise. In each case, the equilibrium state expected in terms of velocity, pressure and contact angle is reached. (C) 2018 Elsevier Ltd. All rights reserved

    On the Use of Magnetic RAMs in Field-Programmable Gate Arrays

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    This paper describes the integration of field-induced magnetic switching (FIMS) and thermally assisted switching (TAS) magnetic random access memories in FPGA design. The nonvolatility of the latter is achieved through the use of magnetic tunneling junctions (MTJs) in the MRAM cell. A thermally assisted switching scheme helps to reduce power consumption during write operation in comparison to the writing scheme in the FIMS-MTJ device. Moreover, the nonvolatility of such a design based on either an FIMS or a TAS writing scheme should reduce both power consumption and configuration time required at each power up of the circuit in comparison to classical SRAM-based FPGAs. A real-time reconfigurable (RTR) micro-FPGA using FIMS-MRAM or TAS-MRAM allows dynamic reconfiguration mechanisms, while featuring simple design architecture

    Basic reinforcement learning techniques to control the intensity of a seeded free-electron laser

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    Optimal tuning of particle accelerators is a challenging task. Many different approaches have been proposed in the past to solve two main problems\u2014attainment of an optimal working point and performance recovery after machine drifts. The most classical model-free techniques (e.g., Gradient Ascent or Extremum Seeking algorithms) have some intrinsic limitations. To overcome those limitations, Machine Learning tools, in particular Reinforcement Learning (RL), are attracting more and more attention in the particle accelerator community. We investigate the feasibility of RL model-free approaches to align the seed laser, as well as other service lasers, at FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. We apply two different techniques\u2014the first, based on the episodic Q-learning with linear function approximation, for performance optimization; the second, based on the continuous Natural Policy Gradient REINFORCE algorithm, for performance recovery. Despite the simplicity of these approaches, we report satisfactory preliminary results, that represent the first step toward a new fully automatic procedure for the alignment of the seed laser to the electron beam. Such an alignment is, at present, performed manually

    Toward the Application of Reinforcement Learning to the Intensity Control of a Seeded Free-Electron Laser

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    The optimization of particle accelerators is a challenging task, and many different approaches have been proposed in years, to obtain an optimal tuning of the plant and to keep it optimally tuned despite drifts or disturbances. Indeed, the classical model-free approaches (such as Gradient Ascent or Extremum Seeking algorithms) have intrinsic limitations. To overcome those limitations, Machine Learning techniques, in particular, the Reinforcement Learning, are attracting more and more attention in the particle accelerator community. The purpose of this paper is to apply a Reinforcement Learning model-free approach to the alignment of a seed laser, based on a rather general target function depending on the laser trajectory. The study focuses on the alignment of the lasers at FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. In particular, we employ Q-learning with linear function approximation and report experimental results obtained in two setups, which are the actual setups where the final application has to be deployed. Despite the simplicity of the approach, we report satisfactory preliminary results, that represent the first step toward a fully automatic procedure for seed laser to the electron beam. Such a superimposition is, at present, performed manually
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