Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data

Carter, Douglas; Voogt, Francis De; Soares, Renan Francisco; Ganapathisubramani, Bharathram

doi:10.1017/dce.2021.5

Cited by 28 publications

(8 citation statements)

References 46 publications

(66 reference statements)

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“…As mentioned above, fully connected network-based reconstruction is prohibitively expensive for global flow field reconstruction due to the very large number of parameters in the network [121]. To address this issue, there are also some efforts to estimate low-order representations such as coefficients obtained through proper orthogonal decomposition (POD) from sparse sensor measurements [122][123][124]. For instance, Nair and Goza [67] proposed a fully connected model-based estimator of POD coefficients and applied it to a laminar wake around a flat plate.…”

Section: Supervised Learningmentioning

confidence: 99%

Super-resolution analysis via machine learning: a survey for fluid flows

Fukami

Fukagata

Taira

2023

Theor. Comput. Fluid Dyn.

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This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data. Graphical abstract

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Section: Supervised Learningmentioning

confidence: 99%

Super-resolution analysis via machine learning: a survey for fluid flows

Fukami

Fukagata

Taira

2023

Theor. Comput. Fluid Dyn.

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“…Collecting information from sensor measurements is often the only viable approach when estimating the internal state or hidden physical quantities. The optimization of sensor positions was intensively discussed in order to determine the most representative sensors and to reduce the resulting estimation error, such as when monitoring sensor networks [1][2][3][4], fluid flows around objects [5][6][7][8][9][10][11][12][13][14][15], plants and factories [16][17][18], infrastructures [19][20][21], circuits [22], and biological systems [23], estimating physical field [24][25][26][27], and localizing sources [28,29]. Recent advances in data science techniques have enabled us to extract reduced-order models from vastly large-scale measurements of complex phenomena [30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Efficient Sensor Node Selection for Observability Gramian Optimization

Yamada

Sasaki

Nagata

et al. 2023

Sensors

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Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results.

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“…In addition, sensor optimization techniques are another important topic for this framework. Various methods have been proposed, and there are methods based on the greedy algorithm (Manohar et al, 2018a(Manohar et al, ,b, 2019Clark et al, 2018Clark et al, , 2020aJiang et al, 2019;Saito et al, 2020Saito et al, , 2021aYamada et al, 2021;Nakai et al, 2021;Carter et al, 2021;? ;Inoue et al, 2021;Li et al, 2021b,a;Nakai et al, 2022;Nagata et al, 2022a), the convex relaxation (Joshi & Boyd, 2009;Liu et al, 2016;Nonomura et al, 2021), and the proximal optimization (Fardad et al, 2011;Lin et al, 2013;Dhingra et al, 2014;Zare & Jovanović, 2018;Nagata et al, 2021Nagata et al, , 2022b.…”

Section: Introductionmentioning

confidence: 99%

Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model

Nagata,

Nakai,

Yamada

et al. 2022

Preprint

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Reconstruction of the distributions of ground motion due to an earthquake is one of the key technologies for the prediction of seismic damage to infrastructure. Particularly, immediate reconstruction of spatially continuous wavefield is valuable for decision-making of disaster response decisions in the initial phase. For a fast and accurate reconstruction, utilization of prior information and effective reduction of observation is essential. In fluid mechanics, full-state recovery, which recovers the full state from sparse observation using a data-driven model reduced-order model, is actively used. In the present study, the framework developed in the field of fluid mechanics is applied to seismic wavefield reconstruction. A seismic wavefield reconstruction framework based on compressed sensing using the data-driven reduced-order model (ROM) is proposed and its characteristics are investigated through numerical experiments. The data-driven ROM is generated from the dataset of the wavefield using the singular value decomposition. The spatially continuous seismic wavefield is reconstructed from the sparse and discrete observation and the datadriven ROM. The observation sites used for reconstruction are effectively selected by the sensor optimization method for linear inverse problems based on a greedy algorithm. The proposed framework was applied to synthetic waveforms without and with observation noise. The validity of the proposed method was confirmed by the reconstruction based on the noise-free observation. Since the ROM of the wavefield is used as prior information, the reconstruction error is reduced to an approximately lower error bound of the present framework, even though the number of the sensors used for reconstruction is limited and randomly selected. In addition, the reconstruction error obtained by the proposed framework is much smaller than that obtained by the Gaussian process regression. For the numerical experiment with noise-contaminated observation, the reconstructed wavefield is degraded due to the observation noise, but the reconstruction error obtained by the present framework with all available observation sites is close to a lower error bound, even though the reconstructed wavefield using the Gaussian process regression is fully collapsed. Although the reconstruction error is larger than that obtained using all observation sites, the number of observation sites used for reconstruction can be reduced while minimizing the deterioration and scatter of the reconstructed data by combining it with the sensor optimization method. Hence, the subset of the optimized observation sites selected by the greedy method provides a better and more stable reconstruction of the wavefield than randomly selected observation sites, even if the reconstruction is carried out with a smaller number of observations with observation noise.

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Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data

Cited by 28 publications

References 46 publications

Super-resolution analysis via machine learning: a survey for fluid flows

Super-resolution analysis via machine learning: a survey for fluid flows

Efficient Sensor Node Selection for Observability Gramian Optimization

Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model

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