Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

Manohar, Krithika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.

doi:10.1109/mcs.2018.2810460

Cited by 338 publications

(208 citation statements)

References 130 publications

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“…which terms in b are non-zero). Here, following the work of[13], we show that favourable The first four POD and DMD modes are shown in an idealized aneurysm model with the shown geometric dimensions. The pulsatile waveform used as inlet boundary condition (BC) is shown.…”

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confidence: 59%

Data-driven cardiovascular flow modelling: examples and opportunities

Arzani

Dawson

2021

J. R. Soc. Interface.

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High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

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confidence: 59%

Data-driven cardiovascular flow modelling: examples and opportunities

Arzani

Dawson

2021

J. R. Soc. Interface.

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“…The method of performing PIV using particle images at a limited number of observation points and estimating the flow field from the sparse velocity vectors obtained is called SPPIV. This is one of the applications of data-driven field reconstruction using the sparse sensors of previous studies, [3][4][5] but the application to PIV is an advanced point in the present study. Figure 1 shows a schematic diagram of creating train-ing data used for SPPIV, and Fig.…”

Section: Sppivmentioning

confidence: 99%

Feasibility Study on Real-time Observation of Flow Velocity Field using Sparse Processing Particle Image Velocimetry

Kanda

Nakai

Saito

et al. 2021

Trans. Japan Soc. Aero. S Sci.

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“…In this section, we seek to build a dynamical system (with the structure given by Eq. (12)) that estimates the evolution of Y(n) from local measurements. We consider a sensor measuring the streamwise and cross-stream components of the oscillatory velocity s(n) = [u(n), v(n)] at the point (x s , y s ) = (1.82, −0.04).…”

Section: B System Identification and Validationmentioning

confidence: 99%

“…A time-segment of the simulation, known as learning dataset, is used together with the identification algorithm N4SID [25] to estimate the unknown matrices of system (12). The reduced-order model is determined with k = 4 POD modes and represents the evolution of the vortex-shedding mode and its first harmonic.…”

Section: B System Identification and Validationmentioning

confidence: 99%

Data-based, reduced-order, dynamic estimator for reconstruction of nonlinear flows exhibiting limit-cycle oscillations

2019

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We apply a data-based, linear dynamic estimator to reconstruct the velocity field from measurements at a single sensor point in the wake of an aerofoil. In particular, we consider a NACA0012 airfoil at Re = 600 and 16 • angle of attack. Under these conditions, the flow exhibits a vortex shedding limit cycle. A reduced order model (ROM) of the flow field is extracted using proper orthogonal decomposition (POD). Subsequently, a subspace system identification algorithm (N4SID) is applied to extract directly the estimator matrices from the reduced output of the system (the POD coefficients). We explore systematically the effect of the number of states of the estimator, the sensor location, the type of sensor measurements (one or both velocity components), and the number of POD modes to be recovered. When the signal of a single velocity component (in the stream wise or cross stream directions) is measured, the reconstruction of the first two dominant POD modes strongly depends on the sensor location. We explore this behaviour and provide a physical explanation based on the non-linear mode interaction and the spatial distribution of the modes. When however, both components are measured, the performance is very robust, and is almost independent of the sensor location when the optimal number of estimator states is used. Reconstruction of the less energetic modes is more difficult, but still possible.

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Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

Cited by 338 publications

References 130 publications

Data-driven cardiovascular flow modelling: examples and opportunities

Data-driven cardiovascular flow modelling: examples and opportunities

Feasibility Study on Real-time Observation of Flow Velocity Field using Sparse Processing Particle Image Velocimetry

Data-based, reduced-order, dynamic estimator for reconstruction of nonlinear flows exhibiting limit-cycle oscillations

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