The simulation of heat convection problems usually leads to very large matrix systems because both Navier–Stokes equations and the energy equations are to be taken into account and discretized. Of course, such large matrix systems cannot be used for on-line control algorithms due to memory limitations and computation time. On-line control algorithms should rather consider much smaller matrix systems. Bearing this in mind, model reduction techniques present a large interest to obtain a suitable low-order model that can further be used in control processes. In this paper, reduced models are obtained through the modal identification method. This method relies on the solution of an optimization problem of parameter estimation following the steps: (i) the structure of the state model is first defined, (ii) the related vectors and matrices are estimated through the minimization of a corresponding functional, (iii) the reduced order model then must be validated with input data different from those used within the identification process. These steps being completed, other control algorithms can “plug” such reduced models. Among linear control algorithms, the feedback control laws considered in this paper are based either on the reduced state or on the output. A Kalman filter evaluates the state through a limited number of measurements. The developed numerical application deals with a temperature field within a 2D stationary flow over a backward-facing step. The goal is to keep the outlet temperature as close as possible to a given temperature profile downstream from the step, whatever the pipe inlet temperature fluctuations. One thus searches some “optimal” heat fluxes upstream from the step that counteract the inlet temperature variations.
Accepté le :
Publié le :
Yassine Rouizi 1, 2 ; Yann Favennec 3 ; Yvon Jarny 3 ; Daniel Petit 2
@article{CRMECA_2013__341_11-12_776_0, author = {Yassine Rouizi and Yann Favennec and Yvon Jarny and Daniel Petit}, title = {Model reduction through identification {\textendash} {Application} to some diffusion{\textendash}convection problems in heat transfer, with an extension towards control strategies}, journal = {Comptes Rendus. M\'ecanique}, pages = {776--792}, publisher = {Elsevier}, volume = {341}, number = {11-12}, year = {2013}, doi = {10.1016/j.crme.2013.09.005}, language = {en}, }
TY - JOUR AU - Yassine Rouizi AU - Yann Favennec AU - Yvon Jarny AU - Daniel Petit TI - Model reduction through identification – Application to some diffusion–convection problems in heat transfer, with an extension towards control strategies JO - Comptes Rendus. Mécanique PY - 2013 SP - 776 EP - 792 VL - 341 IS - 11-12 PB - Elsevier DO - 10.1016/j.crme.2013.09.005 LA - en ID - CRMECA_2013__341_11-12_776_0 ER -
%0 Journal Article %A Yassine Rouizi %A Yann Favennec %A Yvon Jarny %A Daniel Petit %T Model reduction through identification – Application to some diffusion–convection problems in heat transfer, with an extension towards control strategies %J Comptes Rendus. Mécanique %D 2013 %P 776-792 %V 341 %N 11-12 %I Elsevier %R 10.1016/j.crme.2013.09.005 %G en %F CRMECA_2013__341_11-12_776_0
Yassine Rouizi; Yann Favennec; Yvon Jarny; Daniel Petit. Model reduction through identification – Application to some diffusion–convection problems in heat transfer, with an extension towards control strategies. Comptes Rendus. Mécanique, Volume 341 (2013) no. 11-12, pp. 776-792. doi : 10.1016/j.crme.2013.09.005. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2013.09.005/
[1] Model Order Reduction: Theory, Research Aspects and Applications, Mathematics in Industry, vol. 13, Springer, 2008
[2] Reduced-order modeling: new approaches for computational physics, Prog. Aerosp. Sci., Volume 40 (2004) no. 1–2, pp. 51-117
[3] An overview of approximation methods for large-scale dynamical systems, Annu. Rev. Control, Volume 29 (2005) no. 2, pp. 181-190
[4] Analyse modale dʼun processus de diffusion thermique : identification par thermographie infrarouge, Int. J. Heat Mass Transf., Volume 31 (1988) no. 3, pp. 487-496
[5] Identification methods in nonlinear heat conduction. Part I: Model reduction, Int. J. Heat Mass Transf., Volume 48 ( January 2005 ), pp. 105-118
[6] Model reduction for heat conduction with radiative boundary conditions using the modal identification method, Numer. Heat Transf., Part B, Volume 52 (2007), pp. 107-130
[7] Reduced modelling through identification on 2D incompressible laminar flows, Inverse Probl. Sci. Eng., Volume 17 (2009) no. 3, pp. 303-319
[8] Numerical model reduction of 2D steady incompressible laminar flows: Application on the flow over a backward-facing step, J. Comput. Phys., Volume 228 (2009) no. 6, pp. 2239-2255
[9] The adjoint method coupled with the modal identification method for nonlinear model reduction, Inverse Probl. Sci. Eng., Volume 14 (2006), pp. 153-170
[10] Commande et Optimisation des Processus, Editions Technip, 1990
[11] Using process tomography as a sensor for optimal control, Appl. Numer. Math., Volume 56 (2006), pp. 37-54
[12] On the use of reduced models obtained through identification for feedback optimal control problems in a heat convection–diffusion problem, Comput. Methods Appl. Mech. Eng., Volume 199 (2010) no. 17–20, pp. 1193-1201
[13] Experimental and theoretical investigation of backward-facing step flow, J. Fluid Mech., Volume 127 (1983), pp. 473-496
[15] Numerical solutions of 2D steady incompressible flow over a backward-facing step, Part I: High Reynolds number solutions, Comput. Fluids, Volume 37 (2008) no. 6, pp. 633-655
[16] A non-staggered grid, fractional step method for time-dependent incompressible Navier–Stokes equations in curvilinear coordinates, J. Comput. Phys., Volume 114 (1994), pp. 18-33
[17] Model reduction by the modal identification method in forced convection: Application to a heated flow over a backward-facing step, Int. J. Therm. Sci., Volume 49 (2010), pp. 1354-1368
[18] Comparison between the modal identification method and the POD-Galerkin method for model reduction in nonlinear diffusive systems, Int. J. Numer. Methods Eng., Volume 67 (2006), pp. 895-915
[19] Mathematical Programming, Theory and Applications, Wiley, 1986
[20] Optimal control of diffusion–convection–reaction processes using reduced-order models, Comput. Chem. Eng., Volume 32 (2008) no. 9, pp. 2123-2135
[21] Optimal heating strategies for a convection oven, J. Food Eng., Volume 48 (2001) no. 4, pp. 335-344
[22] Optimal control of the cylinder wake in the laminar regime by trust-region methods and pod reduced-order models, J. Comput. Phys., Volume 227 (2008) no. 16, pp. 7813-7840
[23] Thermal control via state feedback using a low order model built from experimental data by the modal identification method, Int. J. Heat Mass Transf., Volume 55 (2012), pp. 1679-1694
[24] Temperature regulation and tracking in a MIMO system with a mobile heat source by LQG control with a low order model, Control Eng. Pract., Volume 21 (2013), pp. 333-349
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