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Lecture

Rayleigh-Benard Eigenvalue Problem

In course

ME-466: Instability

This course focuses on the physical mechanisms at the origin of the transition of a flow from laminar to turbulent using the hydrodynamic instability theory. ...

Description

This lecture covers the solution of the Rayleigh-Benard eigenvalue problem, focusing on the finite difference schemes for first and second derivatives, as well as rigid boundary conditions. The lecture also discusses the spatial growth and nonlinear lubrication equations, emphasizing the temporal dispersion relation and the steady flow purely spatial dispersion relation. The presentation concludes with a detailed analysis of typical structures observed in Marangoni convection, including the transition to square patterns. MATLAB scripts and PowerPoint slides are used to illustrate the concepts.

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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).

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Related concepts (32)

Finite difference method

In numerical analysis, finite-difference methods (FDM) are a class of numerical techniques for solving differential equations by approximating derivatives with finite differences. Both the spatial domain and time interval (if applicable) are discretized, or broken into a finite number of steps, and the value of the solution at these discrete points is approximated by solving algebraic equations containing finite differences and values from nearby points.

Finite difference

A finite difference is a mathematical expression of the form f (x + b) − f (x + a). If a finite difference is divided by b − a, one gets a difference quotient. The approximation of derivatives by finite differences plays a central role in finite difference methods for the numerical solution of differential equations, especially boundary value problems. The difference operator, commonly denoted is the operator that maps a function f to the function defined by A difference equation is a functional equation that involves the finite difference operator in the same way as a differential equation involves derivatives.

Eigenvalues and eigenvectors

In linear algebra, an eigenvector (ˈaɪgənˌvɛktər) or characteristic vector of a linear transformation is a nonzero vector that changes at most by a constant factor when that linear transformation is applied to it. The corresponding eigenvalue, often represented by , is the multiplying factor. Geometrically, a transformation matrix rotates, stretches, or shears the vectors it acts upon. The eigenvectors for a linear transformation matrix are the set of vectors that are only stretched, with no rotation or shear.

Eigenvalue algorithm

In numerical analysis, one of the most important problems is designing efficient and stable algorithms for finding the eigenvalues of a matrix. These eigenvalue algorithms may also find eigenvectors. Eigenvalues and eigenvectors and Generalized eigenvector Given an n × n square matrix A of real or complex numbers, an eigenvalue λ and its associated generalized eigenvector v are a pair obeying the relation where v is a nonzero n × 1 column vector, I is the n × n identity matrix, k is a positive integer, and both λ and v are allowed to be complex even when A is real.

Eigendecomposition of a matrix

In linear algebra, eigendecomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. Only diagonalizable matrices can be factorized in this way. When the matrix being factorized is a normal or real symmetric matrix, the decomposition is called "spectral decomposition", derived from the spectral theorem. Eigenvalue, eigenvector and eigenspace A (nonzero) vector v of dimension N is an eigenvector of a square N × N matrix A if it satisfies a linear equation of the form for some scalar λ.

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