A reduced-order model for compressible flows with buffeting condition using higher order dynamic mode decomposition with a mode selection criterion

This study proposes an improvement in the performance of reduced-order models (ROMs) based on dynamic mode decomposition to model the flow dynamics of the attractor from a transient solution. By combining higher order dynamic mode decomposition (HODMD) with an efficient mode selection criterion, the HODMD with criterion (HODMDc) ROM is able to identify dominant flow patterns with high accuracy. This helps us to develop a more parsimonious ROM structure, allowing better predictions of the attractor dynamics. The method is tested in the solution of a NACA0012 airfoil buffeting in a transonic flow, and its good performance in both the reconstruction of the original solution and the prediction of the permanent dynamics is shown. In addition, the robustness of the method has been successfully tested using different types of parameters, indicating that the proposed ROM approach is a tool promising for using in both numerical simulations and experimental data.

[1]  Peter J. Schmid,et al.  Sparsity-promoting dynamic mode decomposition , 2012, 1309.4165.

[2]  Peter J. Schmid,et al.  Application of the dynamic mode decomposition to experimental data , 2011 .

[3]  Christopher C. Pain,et al.  Non-intrusive reduced order modelling of fluid–structure interactions , 2016 .

[4]  P. Schmid,et al.  Dynamic mode decomposition of numerical and experimental data , 2008, Journal of Fluid Mechanics.

[5]  P. Goulart,et al.  Optimal mode decomposition for unsteady flows , 2013, Journal of Fluid Mechanics.

[6]  Weiwei Zhang,et al.  Reduced-order thrust modeling for an efficiently flapping airfoil using system identification method , 2017 .

[7]  Scott T. M. Dawson,et al.  Model Reduction for Flow Analysis and Control , 2017 .

[8]  A. Jirásek,et al.  Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics , 2014 .

[9]  F. Guéniat,et al.  A dynamic mode decomposition approach for large and arbitrarily sampled systems , 2015 .

[10]  Clarence W. Rowley,et al.  Spectral analysis of fluid flows using sub-Nyquist-rate PIV data , 2014, Experiments in Fluids.

[11]  Weiwei Zhang,et al.  Mechanism of frequency lock-in in vortex-induced vibrations at low Reynolds numbers , 2015, Journal of Fluid Mechanics.

[12]  Clarence W. Rowley,et al.  Model Reduction for fluids, Using Balanced Proper Orthogonal Decomposition , 2005, Int. J. Bifurc. Chaos.

[13]  Weiwei Zhang,et al.  The interaction between flutter and buffet in transonic flow , 2015 .

[14]  Bryan Glaz,et al.  Reduced-Order Nonlinear Unsteady Aerodynamic Modeling Using a Surrogate-Based Recurrence Framework , 2010 .

[15]  Clarence W. Rowley,et al.  Dynamic mode decomposition for large and streaming datasets , 2014, 1406.7187.

[16]  E. Ferrer,et al.  Low cost 3D global instability analysis and flow sensitivity based on dynamic mode decomposition and high‐order numerical tools , 2014 .

[17]  Walter A. Silva,et al.  Application of nonlinear systems theory to transonic unsteady aerodynamic responses , 1993 .

[18]  D. A. Bistrian,et al.  Randomized dynamic mode decomposition for nonintrusive reduced order modelling , 2016, 1611.04884.

[19]  Weiwei Zhang,et al.  An improved criterion to select dominant modes from dynamic mode decomposition , 2017 .

[20]  Shervin Bagheri,et al.  Koopman-mode decomposition of the cylinder wake , 2013, Journal of Fluid Mechanics.

[21]  Peter J. Schmid,et al.  Recursive dynamic mode decomposition of transient and post-transient wake flows , 2016, Journal of Fluid Mechanics.

[22]  Peter J. Schmid,et al.  Parametrized data-driven decomposition for bifurcation analysis, with application to thermo-acoustically unstable systems , 2015 .

[23]  Christian Breitsamter,et al.  Efficient unsteady aerodynamic loads prediction based on nonlinear system identification and proper orthogonal decomposition , 2016 .

[24]  Soledad Le Clainche,et al.  Flow around a hemisphere-cylinder at high angle of attack and low Reynolds number. Part II: POD and DMD applied to reduced domains , 2015 .

[25]  Clarence W. Rowley,et al.  De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets , 2015, Theoretical and Computational Fluid Dynamics.

[26]  P. Seiler,et al.  A method to construct reduced‐order parameter‐varying models , 2017 .

[27]  Steven L. Brunton,et al.  Dynamic Mode Decomposition with Control , 2014, SIAM J. Appl. Dyn. Syst..

[28]  I. Mezić,et al.  Spectral analysis of nonlinear flows , 2009, Journal of Fluid Mechanics.

[29]  Clarence W. Rowley,et al.  Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses , 2012, J. Nonlinear Sci..

[30]  David R. Williams,et al.  Reduced-order unsteady aerodynamic models at low Reynolds numbers , 2013, Journal of Fluid Mechanics.

[31]  Clarence W. Rowley,et al.  Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition , 2014, Experiments in Fluids.

[32]  Soledad Le Clainche,et al.  Flow around a hemisphere-cylinder at high angle of attack and low Reynolds number. Part I: Experimental and numerical investigation , 2015 .

[33]  Weiwei Zhang,et al.  Numerical study on closed-loop control of transonic buffet suppression by trailing edge flap , 2016 .

[34]  P. Beran,et al.  Reduced-order modeling: new approaches for computational physics , 2004 .

[35]  Clarence W. Rowley,et al.  A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition , 2014, Journal of Nonlinear Science.

[36]  Vassilios Theofilis,et al.  Four Decades of Studying Global Linear Instability: Progress and Challenges , 2012 .

[37]  B. R. Noack,et al.  On the need of mode interpolation for data-driven Galerkin models of a transient flow around a sphere , 2017 .

[38]  J. Vega,et al.  Higher order dynamic mode decomposition to identify and extrapolate flow patterns , 2017 .

[39]  Soledad Le Clainche,et al.  Higher order dynamic mode decomposition of noisy experimental data: The flow structure of a zero-net-mass-flux jet , 2017 .

[40]  Matthew O. Williams,et al.  A kernel-based method for data-driven koopman spectral analysis , 2016 .

[41]  B. R. Noack,et al.  A hierarchy of low-dimensional models for the transient and post-transient cylinder wake , 2003, Journal of Fluid Mechanics.

[42]  Damon Honnery,et al.  An error analysis of the dynamic mode decomposition , 2011, Experiments in Fluids.

[43]  Weiwei Zhang,et al.  Mechanism of frequency lock-in in transonic buffeting flow , 2017, Journal of Fluid Mechanics.

[44]  Soledad Le Clainche Martínez,et al.  Higher Order Dynamic Mode Decomposition , 2017, SIAM J. Appl. Dyn. Syst..

[45]  Weiwei Zhang,et al.  Efficient Method for Limit Cycle Flutter Analysis Based on Nonlinear Aerodynamic Reduced-Order Models , 2012 .

[46]  Minglang Yin,et al.  Novel Wiener models with a time-delayed nonlinear block and their identification , 2016 .

[47]  Earl H. Dowell,et al.  Modeling of Fluid-Structure Interaction , 2001 .

[48]  Weiwei Zhang,et al.  Active control of transonic buffet flow , 2017, Journal of Fluid Mechanics.

[49]  V. Theofilis Advances in global linear instability analysis of nonparallel and three-dimensional flows , 2003 .

[50]  Maenghyo Cho,et al.  Reduced-Order Model with an Artificial Neural Network for Aerostructural Design Optimization , 2013 .

[51]  Soledad Le Clainche,et al.  Accelerating oil reservoir simulations using POD on the fly , 2017 .