Publication

Design et optimisation d'un actionneur linéaire miniature

2012
Student Projects

Abstract

Dans le cadre d'une collaboration avec une entreprise suisse, le LAI développe un mécanisme d'échappement linéaire permettant de réguler le déplacement relatif entre deux pièces. Un soin particulier doit être apporté au design d'un actionneur linéaire miniature à faible course. Au cours d'un précédent projet de semestre, le choix d'une topologie particulière et la modélisation de la force obtenue en fonction de paramètres géométriques ont été effectués. Dans ce projet, il est demandé d'adapter cette topologie aux contraintes géométriques strictes imposées par le cahier des charges, tout en respectant les contraintes de force du système. Pour ce faire, la modélisation analytique ou la méthode des éléments finis sera utilisée pour la détermination des forces en jeu dans le système. Différentes méthodes d'optimisation peuvent être utilisées et testées par l'étudiant.

Official source

https://infoscience.epfl.ch/entities/publication/fb1bbc2c-08fd-4718-9217-a5ae35ab229e

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2012
Student Projects

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/fb1bbc2c-08fd-4718-9217-a5ae35ab229e

About this result

Related concepts (5)

Multigrid method

In numerical analysis, a multigrid method (MG method) is an algorithm for solving differential equations using a hierarchy of discretizations. They are an example of a class of techniques called multiresolution methods, very useful in problems exhibiting multiple scales of behavior. For example, many basic relaxation methods exhibit different rates of convergence for short- and long-wavelength components, suggesting these different scales be treated differently, as in a Fourier analysis approach to multigrid.

Runge–Kutta methods

In numerical analysis, the Runge–Kutta methods (ˈrʊŋəˈkʊtɑː ) are a family of implicit and explicit iterative methods, which include the Euler method, used in temporal discretization for the approximate solutions of simultaneous nonlinear equations. These methods were developed around 1900 by the German mathematicians Carl Runge and Wilhelm Kutta. The most widely known member of the Runge–Kutta family is generally referred to as "RK4", the "classic Runge–Kutta method" or simply as "the Runge–Kutta method".

Galerkin method

In mathematics, in the area of numerical analysis, Galerkin methods are named after the Soviet mathematician Boris Galerkin. They convert a continuous operator problem, such as a differential equation, commonly in a weak formulation, to a discrete problem by applying linear constraints determined by finite sets of basis functions.