Wave–structure interaction: simulation driven by quantitative imaging

Sirisup, Sirod; Karniadakis, George; Rockwell, D.

doi:10.1098/rspa.2003.1187

Cited by 19 publications

(10 citation statements)

References 44 publications

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“…Figure 20 compares such vorticity fields, similarly demonstrating the capability of the Kalman-filtered simulation. Some of the previous techniques, such as the POD-based methods (Ma et al 2003;Sirisup et al 2004), are capable of degenerating the degrees of freedom and compensating the measurement deficiency. In contrast, the philosophy of the hybrid unsteady-flow simulation is to retain as many degrees of freedom as possible but still suppress the measurement noise.…”

Section: Experimental Databasementioning

confidence: 99%

Reduced-order Kalman-filtered hybrid simulation combining particle tracking velocimetry and direct numerical simulation

Suzuki

2012

J. Fluid Mech.

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The capability of state-of-the-art techniques integrating experimental and computational fluid dynamics has been expanding recently. In our previous study, we have developed a hybrid unsteady-flow simulation technique combining particle tracking velocimetry (PTV) and direct numerical simulation (DNS) and demonstrated its capability at low Reynolds numbers. Similar approaches have also been proposed by a few groups; however, applying algorithms of this type generally becomes more challenging with increasing Reynolds number because the time interval of the frame rate for particle image velocimetry (PIV) becomes much greater than the required computational time step, and the PIV/PTV resolution tends to be lower than that necessary for computational fluid dynamics. To extend the applicability to noisy timeresolved PIV/PTV data, the proposed algorithm optimizes the data input temporally and spatially by introducing a reduced-order Kalman filter. This study establishes a framework of the Kalman-filtered hybrid simulation and proves the concept by tackling a planar-jet flow at Re ≈ 2000 as an example. We evaluate the filtering functions as well as convergence of the proposed algorithm by comparing with the existing PTV-DNS hybrid simulation, and show some techniques available to hybrid velocity fields by analysing vortical motion in the shear layers of the jet.

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Section: Experimental Databasementioning

confidence: 99%

Reduced-order Kalman-filtered hybrid simulation combining particle tracking velocimetry and direct numerical simulation

Suzuki

2012

J. Fluid Mech.

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“…There do exist exceptions -for nonautonomous problems with strong external sources, such as periodically driven inflow, long-time drifting from asymptotically stable states was not observed in [84,113]. The drifting of ROM trajectories in the general case is however a well-known problem and many attempts have been made to remedy it.…”

Section: Stabilization Of Roms For Unsteady Navier-stokes Equationsmentioning

confidence: 99%

Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

Lassila

Manzoni

Quarteroni

et al. 2014

Reduced Order Methods for Modeling and Computational Reduction

161

179

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This chapter reviews techniques of model reduction of fluid dynamics systems. Fluid systems are known to be difficult to reduce efficiently due to several reasons. First of all, they exhibit strong nonlinearities -which are mainly related either to nonlinear convection terms and/or some geometric variability -that often cannot be treated by simple linearization. Additional difficulties arise when attempting model reduction of unsteady flows, especially when long-term transient behavior needs to be accurately predicted using reduced order models and more complex features, such as turbulence or multiphysics phenomena, have to be taken into consideration. We first discuss some general principles that apply to many parametric model order reduction problems, then we apply them on steady and unsteady viscous flows modelled by the incompressible Navier-Stokes equations. We address questions of inf-sup stability, certification through error estimation, computational issues and -in the unsteady case -long-time stability of the reduced model. Moreover, we provide an extensive list of literature references.

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“…4 and Ma et al, 5 who extracted POD modes from ͑digital͒ PIV experiments and obtained an "experiment-based" POD simulation in an effort to implement a numerical algorithm in which simulation and experiment serve a "symbiotic feedback" system. In this way, experimental data are used to construct "realistic" simulation models to accurately predict the evolution of a given experimental state.…”

Section: Introductionmentioning

confidence: 99%

Spatial resolution enhancement/smoothing of stereo–particle-image-velocimetry data using proper-orthogonal-decomposition–based and Kriging interpolation methods

Güneş

Rist

2007

Physics of Fluids

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Methods for data reconstruction and spatial enhancement of experimental data for a transitional boundary layer with laminar separation bubble are investigated. Particularly, proper orthogonal decomposition (POD) is applied to direct numerical simulation (DNS) data to extract the DNS-based POD modes, which are projected onto the experimental data (via a least-squares procedure) in order to obtain model coefficients. These model coefficients are then used to reconstruct, “interpolate,” and smooth the experimental data based on the DNS modes. In addition, in order to compare and assess the effectiveness of the present DNS-based procedure, Kriging interpolation is performed on the experimental (as well as numerical) data. These procedures are applied to time periodic (experimental) instantaneous spanwise vorticity (ωz) at a constant spanwise location. We have demonstrated that particle-image-velocimetry (PIV)-based POD modes can be smoothed by Kriging interpolation, thus a noise-free reconstruction of PIV data can be achieved. It is also found that for very low resolution experimental data, DNS-based interpolation is superior over Kriging interpolation. On the other hand, Kriging interpolation based on the Gaussian correlation model works very well for sufficiently high resolution experimental data. The correlation parameter can be used to control the degree of smoothness in the data reconstruction. Both procedures effectively eliminate the unwanted noise in the experimental data. One important difference between the two procedures is that, with quite some confidence, the DNS-based procedure can also be used for “extrapolation” since the model coefficients do not depend on spatial variation. In fact, we show that near-wall spanwise vorticity, which is not available from experimental data, can be recovered faithfully. Moreover, the enhancement (interpolation and smoothing) of full three-dimensional PIV data has been performed by Kriging interpolation employing a Gaussian correlation model.

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Wave–structure interaction: simulation driven by quantitative imaging

Cited by 19 publications

References 44 publications

Reduced-order Kalman-filtered hybrid simulation combining particle tracking velocimetry and direct numerical simulation

Reduced-order Kalman-filtered hybrid simulation combining particle tracking velocimetry and direct numerical simulation

Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

Spatial resolution enhancement/smoothing of stereo–particle-image-velocimetry data using proper-orthogonal-decomposition–based and Kriging interpolation methods

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