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In recent years, the field of additive manufacturing (AM), often referred to as 3D printing, has seen tremendous growth and radically changed the means by which we describe valid 3D models for production. In particular, it is now conceivable to produce composite structures consisting of smoothly varying oriented anisotropic constitutive materials. In the present work, we propose a sensitivity driven method for the generation of transverse isotropic fiber reinforced structures having smooth spatially varying orientations. Our approach builds upon finite element analysis (FEA) and density-based topology optimization (TO). The local material orientations are formulated as design variables in a stiffness maximization problem, and solved with a non-convex gradient-based optimization scheme. Length-scale control is achieved through the use of filters for regularization. We demonstrate the ability of the proposed approach to handle large-scale 3D problems with synchronous optimization of material densities and orientations yielding millions of design variables on multiple load case scenarios. The method is shown to be compatible with compliant mechanism optimization as well as local volume constraints. Finally, the approach is extended with an additional design variable dictating the ratio of anisotropy for each element, thereby delegating the choice of material type to the optimization scheme.

The deployment of Industry 4.0 is based on data collection and data treatment technologies. Sensors, Internet of Things, Fog computing and Cloud computing constitute the essential infrastructure for Industry 4.0. In fact, production resources, such as machines, tools, and mobile transportation systems need a permanent connection to the monitoring system to ensure their control. Fog Computing represents an interesting solution to ensure that connection and, therefore, the possibility of a distributed control of smart devices. The installation of these facilities determines the cost of data collection and analysis and the quality of service of the whole information system. The challenge is thus to optimize the number and the location of fog computing devices, while making a compromise between the total cost and the quality of service. The problem is formulated with an Integer Linear Programming model and solved using a commercial solver. Results show the possibility to solve the problem and open interesting perspectives to this work.

In recent years, the field of additive manufacturing (AM), often referred to as 3D printing, has seen tremendous growth and radically changed the way we describe valid 3D models for fabrication. While not free of constraints, AM offers an unprecedented level of freedom in geometrical complexity for manufacturable feasible designs. One example of such design freedom is the creation of intricate, robust, and lightweight internal structures. Our approach builds upon and extends the recent works on topology optimization for the so-called infill structures. In order to have more design control over these infill structures, we present a new formulation allowing the generation of mixed design patterns containing bulk and porous regions using a guiding constraint parameterized by non-uniform constraining fields. Secondly, we demonstrate multiple methods of generating such non-uniform fields to leverage the present formulation and analyze their effect on the geometrical and physical properties of the obtained designs.

We study a spline-based approximation of vector fields in the conservative case. Its motivation comes from for instance when approximating current or wind velocity fields, the data deriving in those cases from a potential (pressure for the wind, etc‥). To model the problem, we introduce a minimization problem on a Hilbert space for which the existence and uniqueness of the solution are provided. A convergence result in Sobolev metric spaces is established using norm equivalences and compactness arguments, as well as an approximation error estimate of the involved smoothing $D^{m}$ splines. Possible applications include estimation of wind energy potential and prediction of wind power or marine current simulation.

In this work, the main goal is to study the evolution of the coastline at a local scale, and to get a better understanding of the erosion process. We propose a methodology based on a new software (PyMERI) in order to build a 3D mesh of the subsoil from a Digital Elevation Model (DEM) on which a Partial Differential Equation-direct problem-allowing to recover the voltage (electrical field) in the ground is solved yielding numerical simulations of Electrical Resistivity Imaging (ERI). This study is complemented by the resolution of an inverse problem of ERI for which, unlike the direct problem, the conductivity is now the searched quantity, and by the introduction of a segmentation approach to segment faults on images.

Communication pour le congrès ISPRS du 4 au 10 Juillet 2021; International audience; In this paper, we present a methodology to fusion 3D visible and thermal infrared (TIR) information on a coastal landslide area located in Normandy, France. A reflex and TIR camera on-board an Unmanned Aerial Vehicle are utilized to generate a 3D visible and a thermal model using Photogrammetry. A Python-written algorithm is then used to associate the thermal scalar on the TIR model to the closest point on the visible point cloud, before applying α-blending to ease the visualization of both data sets. This methodology leads to the generation of an integrated 3D thermo-visible model, allowing the direct analysis of the surface temperatures, visible data and geometric configuration of the landslide.

In this paper, we perform a numerical study for the solution of optimal constrained optimization problems for linear convection–diffusion PDEs by local and global radial basis function techniques. To the best of our knowledge, these control problems have not been treated in the literature by RBFs methods. It is well-known that the algebraic system of RBFs methods presents a larger condition number and a higher numerical complexity as the number of nodes (or shape parameter), increases. In this work, and in the context of optimal constrained optimization problems, we explore a possible answer to both problems. Specifically, we introduce a local RBF method (denoted as LAM-DQ), based on the combination of an asymmetric RBFs local method (LAM), inspired in local Hermite interpolation (LHI), combined with the differential quadrature method (DQ). We also propose a preconditioning technique that in combination with extended arithmetic precision let us treat the ill-conditioning problem. We numerically prove that as the number of nodes increases, then for errors of the same order, the condition number remains tractable, in quad-precision, and the numerical complexity of the local method remains bounded.