Zonal Reduced-Order Modeling of Unsteady Flow Field

Author(s):  
Takashi Misaka
2019 ◽  
Author(s):  
Sandeep B. Reddy ◽  
Allan Ross Magee ◽  
Rajeev K. Jaiman ◽  
J. Liu ◽  
W. Xu ◽  
...  

Abstract In this paper, we present a data-driven approach to construct a reduced-order model (ROM) for the unsteady flow field and fluid-structure interaction. This proposed approach relies on (i) a projection of the high-dimensional data from the Navier-Stokes equations to a low-dimensional subspace using the proper orthogonal decomposition (POD) and (ii) integration of the low-dimensional model with the recurrent neural networks. For the hybrid ROM formulation, we consider long short term memory networks with encoder-decoder architecture, which is a special variant of recurrent neural networks. The mathematical structure of recurrent neural networks embodies a non-linear state space form of the underlying dynamical behavior. This particular attribute of an RNN makes it suitable for non-linear unsteady flow problems. In the proposed hybrid RNN method, the spatial and temporal features of the unsteady flow system are captured separately. Time-invariant modes obtained by low-order projection embodies the spatial features of the flow field, while the temporal behavior of the corresponding modal coefficients is learned via recurrent neural networks. The effectiveness of the proposed method is first demonstrated on a canonical problem of flow past a cylinder at low Reynolds number. With regard to a practical marine/offshore engineering demonstration, we have applied and examined the reliability of the proposed data-driven framework for the predictions of vortex-induced vibrations of a flexible offshore riser at high Reynolds number.


2019 ◽  
Vol 35 (2) ◽  
pp. 277-288
Author(s):  
Kyle J. Woolwine ◽  
Kenneth E. Jansen ◽  
George Kopasakis ◽  
Joseph W. Connolly

1997 ◽  
Vol 50 (6) ◽  
pp. 371-386 ◽  
Author(s):  
Earl H. Dowell ◽  
Kenneth C. Hall ◽  
Michael C. Romanowski

In this article, we review the status of reduced order modeling of unsteady aerodynamic systems. Reduced order modeling is a conceptually novel and computationally efficient technique for computing unsteady flow about isolated airfoils, wings, and turbomachinery cascades. Starting with either a time domain or frequency domain computational fluid dynamics (CFD) analysis of unsteady aerodynamic or aeroacoustic flows, a large, sparse eigenvalue problem is solved using the Lanczos algorithm. Then, using just a few of the resulting eigenmodes, a Reduced Order Model of the unsteady flow is constructed. With this model, one can rapidly and accurately predict the unsteady aerodynamic response of the system over a wide range of reduced frequencies. Moreover, the eigenmode information provides important insights into the physics of unsteady flows. Finally, the method is particularly well suited for use in the active control of aeroelastic and aeroacoustic phenomena as well as in standard aeroelastic analysis for flutter or gust response. Numerical results presented include: 1) comparison of the reduced order model to classical unsteady incompressible aerodynamic theory, 2) reduced order calculations of compressible unsteady aerodynamics based on the full potential equation, 3) reduced order calculations of unsteady flow about an isolated airfoil based on the Euler equations, and 4) reduced order calculations of unsteady viscous flows associated with cascade stall flutter, 5) flutter analysis using the Reduced Order Model. This review article includes 25 references.


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