Mathematical and numerical results on the parametric sensitivity of a ROM-POD of the Burgers equation
Keywords:
ROM, POD, sensitivity, parametric evolution, error estimate, Burgers equationAbstract
We are interested in the mathematical study of the sensitivity of a reduced order model (ROM) of a particular single-parameterised quasi-linear equation, via the parametric evolution. More precisely, the ROM of interest is obtained in two different ways: First, we reduce the complete parametric equation using a proper orthogonal decomposition (POD) basis computed at a given reference value of the parameter, and second the parametric ROM is obtained by an expanded POD basis associated this time to a reference solution and its parametric derivative. The second case of our study was considered in a nearly similar way in Ito and Ravindran (1998), but in the context of the reduced basis (RB) method of the Navier–Stokes equations reduction. Indeed, the authors, Ito and Ravindran (1998) proposed to use an expanded set of basis functions, including solution flows for different values of the Reynolds number and their associated first-order derivatives with respect to this parameter. Beside this work, our second strategy for the parametric ROM-POD construction is to consider a temporal snapshots set including a reference solution and its first-order derivative with respect to the corresponding parameter reference value. We give in both proposed cases of the POD basis construction, an a priori estimate of the parametric squared L2-error between the ROM’s solution and the one associated to the full semi-discrete problem. We will show that this estimate will be depending on the distance between two distinct parameters and the evolution of the ROM’s dimension. Moreover, we show that an a priori upper bound of the squared L2-ROM-POD error is much better in the case of an expanded POD basis functions. In particular, we apply our theoretical study to the one-dimensional Burgers equation. Numerical tests are done for the one-dimensional Burgers equation, only in the case of a POD basis associated with a reference solution at a fixed value of the viscosity.
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Akkari, N., Hamdouni, A., Liberge, E., & Jazar, M. (in press). On the sensitivity of the pod technique
for a parameterized quasi-nonlinear parabolic equation. Submitted to Journal of
Advanced Modeling and Simulation in Engineering Sciences, 270, 522–530.
Akkari, N., Hamdouni, A., Liberge, E., & Jazar, M. (2013). A mathematical and numerical study
of the sensitivity of a reduced order model by POD ROM-POD, for a 2D incompressible
fluid flow. Journal of Computational and Applied Mathematics.
Allery, C., Béghein, C., & Hamdouni, A. (2005). Applying proper orthogonal decomposition to
the computation of particle dispersion in a two-dimensional ventilated cavity. Communications
in Nonlinear Science and Numerical Simulation, 10, 907–920.
Allery, C., Béghein, C., & Hamdouni, A. (2008). Investigation of particle dispersion by a ROM
POD approach. International Apllied Mechanics, 44, 133–142.
Ammar, A., Chinesta, F., Diez, P., & Huerta, A. (2010). An error estimator for separated representations
of highly multidimensional models. Computer Methods in Applied Mechanics and
Engineering, 199, 1872–1880.
Ammar, A., Mokdad, B., Chinesta, F., & Keunings, R. (2006). A new family of solvers for some
classes of multidimensional partial differential equations encountered in kinetic theory modeling
of complex fluids. Journal of Non-Newtonian Fluid Mechanics, 139, 153–176.
Ammar, A., Normandin, M., & Chinesta, F. (2010). Solving parametric complex fluids models in
rheometric flows. Journal of Non-Newtonian Fluid Mechanics, 165, 1588–1601.
Amsallem, D., Cortial, J., Carlberg, K., & Farhat, C. (2009). A method for interpolating on manifolds
structural dynamics reduced-order models. International Journal for Numerical Methods
in Engineering, 80, 1241–1258.
Amsallem, D., Cortial, J., & Farhat, C. (2009). On-demand CFD-based aeroelastic predictions
using a database of reduced-order bases and models. 47th AIAA Aerospace Sciences Meeting
Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, 5–8 January
AIAA Paper 2009-800.
Amsallem, D., & Farhat, C. (2008). Interpolation method for adapting reduced-order models and
application to aeroelasticity. AIAA Journal, 46, 1803–1813.
Buffa, A., Maday, Y., Patera, A. T., Prud’homme, C., & Turinici, G. (2012). A priori convergence
theory of the greedy algorithm for the parametrized reduced basis. ESAIM Mathematical
Modelling and Numerical Analysis, 46, 595–603.
Chen, Y., Hesthaven, J. S., Maday, Y., Rodriguez, J., & Zhu, X. (2012). Certified reduced basis
method for electromagnetic scattering and radar cross section estimation. Computer Methods
in Applied Mechanics and Engineering, 92, 233–236.
Chinesta, F., Ammar, A., & Cueto, E. (2010). Recent advances and new challenges in the use of
the proper generalized decomposition for solving multidimensional models. Archives of
Computational Methods in Engineering, 17, 327–350.
Chinesta, F., Leygue, A., Bordeu, F., Aguado, J. V., Cueto, E., Gonzalez, D., … Huerta, A.
(2013). Pgd-based computational vademecum for efficient design, optimization and control.
Archives of Computational Methods in Engineering, 20, 31–59.
Gonzalez, D., Masson, F., Poulhaon, F., Leygue, A., Cueto, E., & Chinesta, F. (2012). Proper
generalized decomposition based dynamic data driven inverse identification. Mathematics and
Computers in Simulation, 82, 1677–1695.Grepl, M. A., Maday, Y., Nguyen, N. C., & Patera, A. T. (2007). Efficient reduced-basis
treatment of nonaffine and nonlinear partial differential equations. ESAIM: Mathematical
Modelling and Numerical Analysis, 41, 575–605.
Grepl, M. A., & Patera, A. T. (2005). A posteriori error bounds for reduced-basis approximations
of parametrized parabolic partial differential equations. ESAIM: Mathematical Modelling and
Numerical Analysis, 39, 157–181.
Hay, A., Akhtar, I., & Borggaard, J. T. (2012). On the use of sensitivity analysis in model reduction
to predict flows for varying inflow conditions. International Journal for Numerical
Methods in Fluids, 68, 122–134.
Hay, A., Borggaard, J., Akhtar, I., & Pelletier, D. (2010). Reduced-order models for parameter
dependent geometries based on shape sensitivity analysis. Journal of Computational Physics,
, 1327–1352.
Hay, A., Borggaard, J., & Pelletier, D. (2009). Local improvements to reduced-order models
using sensitivity analysis of the proper orthogonal decomposition. Journal of Fluid
Mechanics, 629, 41–72.
Ito, K., & Ravindran, S. S. (1998). A reduced-order method for simulation and control of fluid
flows. Journal of Computational Physics, 143, 403–425.
Kunisch, K., & Volkwein, S. (1999). Control of the Burgers equation by a reduced-order
approach using proper orthogonal decomposition. Journal of Optimization Theory and
Applications, 102, 345–371.
Ladeveze, P. (1999). New approaches and non-incremental methods of calculation nonlinear
computational structural mechanics. Berlin: Springer Verlag.
Ladeveze, P., & Nouy, A. (2003). On a multiscale computational strategy with time and space
homogenization for structural mechanics. Computer Methods in Applied Mechanics and
Engineering, 192, 3061–3087.
Lumley, J. (1967). The structure of inhomogeneous turbulent flows. In A. M. Yaglom &
V. I. Tararsky (Eds.), Atmospheric turbulence and radio wave propagation (pp. 166–178).
Nauka: Moscow.
Ly, H. V., & Tran, H. T. (1998). Proper orthogonal decomposition for flow calculations and optimal
control in a horizontal CVD reactor. Center for Research in Scientific Computation,
North Carolina State University: Raleigh, USA.
Machiels, L., Maday, Y., & Patera, A. T. (2001). Output bounds for reduced-order approximations
of elliptic partial differential equations. Computer Methods in Applied Mechanics and
Engineering, 190, 3413–3426.
Maday, Y., Patera, A. T., & Turinici, G. (2002). Global a priori convergence theory for
reduced-basis approximations of single-parameter symmetric coercive elliptic partial
differential equations. Comptes Rendus Mathematique, 335, 289–294.
Nguyen, N.-C., Rozza, G., & Patera, A. T. (2009). Reduced basis approximation and a posteriori
error estimation for the time-dependent viscous Burgers’ equation. Calcolo, 46, 157–185.
Sirovitch, L. (1987). Turbulence and the dynamics of coherent structures, part I: Coherent
strucures, part II: Symmetries and transformations, part III: Dynamics and scaling. Quarterly
of Applied Mathematics, 45, 561–590.
Terragni, F., Valero, E., & Vega, J. M. (2011). Local POD plus galerkin projection in the
unsteady lid-driven cavity problem. SIAM Journal on Scientific Computing, 33, 3538–3561.
Terragni, F., & Vega, J. M. (2012). On the use of POD-based ROMs to analyze bifurcations in
some dissipative systems. Physica D: Nonlinear Phenomena, 241, 1393–1405.
Veroy, K., Prud’homme, C., & Patera, A. T. (2003). Reduced-basis approximation of the viscous
burgers equation: Rigorous a posteriori error bounds. Comptes Rendus Mathematique, 337,
–624.