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Publication# Algorithmes d'adaptation de maillages anisotropes et application à l'aérodynamique

Abstract

The goal of this thesis is to study an anisotropic adaptive algorithm for transonic compressible viscous flow around an airwing. A convection-diffusion model problem is considered, an anisotropic a posteriori error estimator for the H1 semi-norm of the error is derived. The equivalence between the error and the estimator is proved, which provides the efficiency and the reliability of this estimator. A goal oriented anisotropic a posteriori error estimator is introduced. The equivalence with the error is not proved but lower and upper bounds are obtained. Based on this error estimator, an anisotropic mesh algorithm is proposed and applied to the transonic compressible flow around an airwing. The mesh is structured close to the boundary layer and is kept as is, while the mesh outside the boundary layer is adapted according to the anisotropic error estimator. This anisotropic adaptive algorithm allows shocks to be captured accurately, while keeping the number of vertices as low as possible.

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Estimator

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Upper and lower bounds

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Genetic algorithm

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We developed a space and time adaptive method to simulate electroosmosis and mass transport of a sample concentration within a network of microchannels. The space adaptive criteria is based on an error estimator derived using anisotropic interpolation estimates and a post-processing procedure. In order to improve the accuracy of the numerical solution and to reduce even further the computational cost of the numerical simulation, a time adaptive procedure is combined with the one in space. To do so, a time error estimator is derived for a first model problem, the linear heat equation discretized in time with the Crank-Nicolson scheme. The main difficulty is then to obtain an optimal second order error estimator. Applying standard energy techniques with a continuous, piecewise linear approximation in time fail in recovering the optimal order. To restore the appropriate rate of convergence, a continuous piecewise quadratic polynomial function in time is needed. For this purpose, two different quadratic functions are introduced and two different time error estimators are then derived. It turns out that the second error estimator is more efficient than the first one when considering our adaptive algorithm. Thus, using the second quadratic polynomial, an upper bound for the error is derived for a second model problem, the time-dependent convection-diffusion problem discretized in time with the Crank-Nicolson scheme. The corresponding space and time error estimators are finally used for the numerical simulation of mass transport of a sample concentration within a complex network of microchannels driven by an electroosmotic flow and/or by a pressure-driven flow. Numerical results presented show the efficiency and the robustness of this approach.

Marco Picasso, Virabouth Prachittham

In this paper we derive two a posteriori upper bounds for the heat equation. A continuous, piecewise linear finite element discretization in space and the Crank-Nicolson method for the time discretization are used. The error due to the space discretization is derived using anisotropic interpolation estimates and a postprocessing procedure. The error due to the time discretization is obtained using two different continuous, piecewise quadratic time reconstructions. The first reconstruction is developed following G. Akrivis, C. Makridakis, and R. H. Nochetto [Math. Comp., 75 (2006), pp. 511-531], while the second one is new. Moreover, in the case of isotropic meshes only, upper and lower bounds are provided as in [R. Verfurth, Calcolo, 40 (2003), pp. 195 212]. An adaptive algorithm is developed. Numerical studies are reported for several test cases and show that the second error estimator is more efficient than the first one. In particular, the second error indicator is of optimal order with respect to both the mesh size and the time step when using our adaptive algorithm.

2009