An Automated Method for Hot-to-Cold Geometry Mapping.

This project presents the development and implementation of a GPU-accelerated meshless two-phase incompressible fluid flow solver. The solver uses a variant of the Generalized Finite Difference Meshless Method presented by Gerace et al. [1]. The Level Set Method [2] is used for capturing the fluid interface. The Compute Unified Device Architecture (CUDA) language for general-purpose computing on the graphics-processing-unit is used to implement the GPU-accelerated portions of the solver. CUDA allows the programmer to take advantage of the massive parallelism offered by the GPU at a cost that is significantly lower than other parallel computing options. Through the combined use of GPU-acceleration and a radial-basis function (RBF) collocation meshless method, this project seeks to address the issue of speed in computational fluid dynamics. Traditional mesh-based methods require a large amount of user input in the generation and verification of a computational mesh, which is quite time consuming. The RBF meshless method seeks to rectify this issue through the use of a grid of data centers that need not meet stringent geometric requirements like those required by finite-volume and finite-element methods. Further, the use of the GPU to accelerate the method has been shown to provide a 16-fold increase in speed for the solver subroutines that have been accelerated.

No description available.

No description available.

No description available.

No description available.

No description available.

The thesis compares several non-linear regression methods on synthetic data sets gen- erated using standard benchmarks for a continuous black-box optimization. For that com- parison, we have chosen the following regression methods: radial basis function networks, Gaussian processes, support vector regression and random forests. We have also included polynomial regression which we use to explain the basic principles of regression. The com- parison of these methods is discussed in the context of black-box optimization problems where the selected methods can be applied as surrogate models. The methods are evalu- ated based on their mean-squared error and on the Kendall's rank correlation coefficient between the ordering of function values according to the model and according to the function used to generate the data. 1

Le CEA développe et utilise des logiciels de calcul, également appelés codes de calcul, dans différentes disciplines physiques pour optimiser les coûts de ses installations et de ses expérimentations. Lors d'une étude, plusieurs phénomènes physiques interagissent. Un couplage et des échanges de données entre plusieurs codes sont nécessaires.Chaque code réalise ses calculs sur une géométrie, généralement représentée sous forme d'un maillage contenant des milliers voire des millions de mailles. Cette thèse se focalise sur le transfert de déformations géométriques entre les maillages spécifiques de chacun des codes de calcul couplés. Pour cela, elle présente une méthode de couplage de plusieurs codes, dont le calcul des déformations est réalisé par l'un d'entre eux. Elle traite également de la mise en place d'un modèle commun aux différents codes de l'étude regroupant l'ensemble des données partagées. Enfin, elle porte sur les transferts de déformations entre des maillages représentant une même géométrie ou des géométries adjacentes. Les modifications géométriques sont de nature discrète car elles s'appuient sur un maillage. Afin de les rendre accessible à l'ensemble des codes de l'étude et pour permettre leur transfert, une représentation continue est calculée. Pour cela, deux fonctions sont développées : l'une à support global, l'autre à support local. Toutes deux combinent une méthode de simplification et un réseau de fonctions de base radiale. Un cas d'application complet est traité dans le cadre du réacteur Jules Horowitz. L'effet des dilatations différentielles sur le refroidissement d'un dispositif expérimental est étudié. / The CEA develops and uses scientific software, called physical codes, in various physical disciplines to optimize installation and experimentation costs. During a study, several physical phenomena interact, so a code coupling and some data exchanges between different physical codes are required.Each physical code computes on a particular geometry, usually represented by a mesh composed of thousands to millions of elements. This PhD Thesis focuses on the geometrical modification transfer between specific meshes of each coupled physical code. First, it presents a physical code coupling method where deformations are computed by one of these codes. Next, it discusses the establishment of a model, common to different physical codes, grouping all the shared data. Finally, it covers the deformation transfers between meshes of the same geometry or adjacent geometries. Geometrical modifications are discrete data because they are based on a mesh. In order to permit every code to access deformations and to transfer them, a continuous representation is computed. Two functions are developed, one with a global support, and the other with a local support. Both functions combine a simplification method and a radial basis function network. A whole use case is dedicated to the Jules Horowitz reactor. The effect of differential dilatations on experimental device cooling is studied.

Student Number : 8315331 - MSc dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / In this dissertation an efficient algorithm to calculate the differential of the network output with respect to its inputs is derived for axis orthogonal Local Model (LMN) and Radial Basis Function (RBF) Networks. A new recursive Singular Value Decomposition (SVD) adaptation algorithm, which attempts to circumvent many of the problems found in existing recursive adaptation algorithms, is also derived. Code listings and simulations are presented to demonstrate how the algorithms may be used in on-line adaptive neurocontrol systems. Specifically, the control techniques known as series inverse neural control and instantaneous linearization are highlighted. The presented material illustrates how the approach enhances the flexibility of LMN networks making them suitable for use in both direct and indirect adaptive control methods. By incorporating this ability into LMN networks an important characteristic of Multi Layer Perceptron (MLP) networks is obtained whilst retaining the desirable properties of the RBF and LMN approach.