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Lecture

Finite Element Method: Quadrangular Elements

In course

ME-372: Finite element method

L'étudiant acquiert une initiation théorique à la méthode des éléments finis qui constitue la technique la plus courante pour la résolution de problèmes elliptiques en mécanique. Il apprend à appliquer cette méthode à des cas simples et à l'exploiter pour ...

Description

This lecture covers the product of two functions, basic functions of linear and quadratic rectangular elements, criteria of differentiability, continuity, and support functions in the context of quadrangular finite elements. It also discusses the Lagrangian family of regular rectangular finite elements and the index form of elementary quantities.

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Instructor

François Gallaire

Né le 26 février 1975, François Gallaire obtient, en 1998, le diplôme dingénieur de lEcole Polytechnique à Paris et, en 1999, un master en « Physique des liquides » à lUniversité Pierre et Marie Curie, toujours à Paris. Il rejoint ensuite le Laboratoire dhydrodynamique (LadHyX) à lEcole polytechnique où il soutient, en 2003, une thèse sur le thème des instabilités des jets tournants et sur le contrôle de léclatement tourbillonnaire sous la direction de Jean-Marrc Chomaz. En 2003, il est nommé chargé de recherche au CNRS au Laboratoire J.A. Dieudonné de lUniversité de Nice Sophia-Antipolis.En 2009, il rejoint l'EPFL pour y fonder le laboratoire des mécanique des fluides et instabilités (LFMI). Ses recherches se concentrent sur létude des propriétés fondamentales de stabilité des écoulements de fluides et sont guidées par les applications réelles, en particulier le contrôle des écoulements. Récemment, il a réalisé dimportantes contributions dans les domaines de la micro-fluidique (lanalyse de la manipulation par laser dune goutte dans un micro-canal) et la dynamique des bio-fluides (le descriptif mécanique de lanévrisme de laorte abdominale).

Official source

Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Related concepts (32)

Linear algebra

Linear algebra is the branch of mathematics concerning linear equations such as: linear maps such as: and their representations in vector spaces and through matrices. Linear algebra is central to almost all areas of mathematics. For instance, linear algebra is fundamental in modern presentations of geometry, including for defining basic objects such as lines, planes and rotations. Also, functional analysis, a branch of mathematical analysis, may be viewed as the application of linear algebra to spaces of functions.

Linear map

In mathematics, and more specifically in linear algebra, a linear map (also called a linear mapping, linear transformation, vector space homomorphism, or in some contexts linear function) is a mapping between two vector spaces that preserves the operations of vector addition and scalar multiplication. The same names and the same definition are also used for the more general case of modules over a ring; see Module homomorphism. If a linear map is a bijection then it is called a .

Linear combination

In mathematics, a linear combination is an expression constructed from a set of terms by multiplying each term by a constant and adding the results (e.g. a linear combination of x and y would be any expression of the form ax + by, where a and b are constants). The concept of linear combinations is central to linear algebra and related fields of mathematics. Most of this article deals with linear combinations in the context of a vector space over a field, with some generalizations given at the end of the article.

Linear span

In mathematics, the linear span (also called the linear hull or just span) of a set S of vectors (from a vector space), denoted span(S), is defined as the set of all linear combinations of the vectors in S. For example, two linearly independent vectors span a plane. The linear span can be characterized either as the intersection of all linear subspaces that contain S, or as the smallest subspace containing S. The linear span of a set of vectors is therefore a vector space itself. Spans can be generalized to matroids and modules.

Linear independence

In the theory of vector spaces, a set of vectors is said to be if there exists no nontrivial linear combination of the vectors that equals the zero vector. If such a linear combination exists, then the vectors are said to be . These concepts are central to the definition of dimension. A vector space can be of finite dimension or infinite dimension depending on the maximum number of linearly independent vectors. The definition of linear dependence and the ability to determine whether a subset of vectors in a vector space is linearly dependent are central to determining the dimension of a vector space.

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