Data-Driven Estimation of the Unsteady Flowfield Near an Actuated Airfoil

Gomez, Daniel F.; Lagor, Francis D.; Kirk, Phillip B.; Lind, Andrew H.; Jones, Anya R.; Paley, Derek A.

doi:10.2514/1.g004339

Cited by 30 publications

(9 citation statements)

References 28 publications

(51 reference statements)

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“…For comparison purposes, the state estimation by using the Kalman Filter [21,22] is considered. The combination of DMDc models with Kalman Filters has recently been shown to yield quite convincing results [3,[17][18][19][20]. One big potential of the Kalman Filter relies on the fact that it provides a minimum estimation error covariance based on a priori statistics of the model and measurement uncertainties, which are considered as white noise processes with covariances Q ∈ R r 2 ×r 2 and r ∈ R in the present context, i.e.,…”

Section: Kalman Filtermentioning

confidence: 96%

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Autoencoder-Based Reduced Order Observer Design for a Class of Diffusion-Convection-Reaction Systems

Schaum¹

2021

Algorithms

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The application of autoencoders in combination with Dynamic Mode Decomposition for control (DMDc) and reduced order observer design as well as Kalman Filter design is discussed for low order state reconstruction of a class of scalar linear diffusion-convection-reaction systems. The general idea and conceptual approaches are developed following recent results on machine-learning based identification of the Koopman operator using autoencoders and DMDc for finite-dimensional discrete-time system identification. The resulting linear reduced order model is combined with a classical Kalman Filter for state reconstruction with minimum error covariance as well as a reduced order observer with very low computational and memory demands. The performance of the two schemes is evaluated and compared in terms of the approximated L2 error norm in a numerical simulation study. It turns out, that for the evaluated case study the reduced-order scheme achieves comparable performance with significantly less computational load.

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Section: Kalman Filtermentioning

confidence: 96%

“…Early lumping based on the combination of DMDc with state estimation has already been shown in several application scenarios to yield satisfactory performance [3,[17][18][19][20]. In these studies the Kalman Filter [21,22] has been used on the basis of the obtained finite-dimensional linear discrete-time model equations.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder-Based Reduced Order Observer Design for a Class of Diffusion-Convection-Reaction Systems

Schaum¹

2021

Algorithms

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“…This estimation approach is actually identical to DMD-model-based estimations. One of them was conducted by Gomez et al (2019) in which a DMD model is used with a Kalman filter for flow estimation with the pressure sensors as observation. Equation 14 is actually identical to the equation of the exact DMD matrix in the projected POD subspace, which is the standard practice for deriving the F matrix in exact DMD.…”

Section: Data-driven Construction Of Modelmentioning

confidence: 99%

Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation

Kanda,

Abe,

Goto

et al. 2022

Preprint

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In this paper, we overview, evaluate, and demonstrate the sparse processing particle image velocimetry (SPPIV) as a real-time flow field estimation method using the particle image velocimetry (PIV), whereas SPPIV was previously proposed with its feasibility study and its real-time demonstration is conducted for the first time in this study. In the wind tunnel test, the PIV measurement and real-time measurement using SPPIV were conducted for the flow velocity field around the NACA0015 airfoil model. The off-line analysis results of the test show that the flow velocity field can be estimated from a small number of processing points by applying SPPIV, and also illustrates the following characteristics of SPPIV. The estimation accuracy improves as the number of processing points increases, whereas the processing time per step increases in proportion to the number of processing points. Therefore, it is necessary to set an optimal number of processing points. Furthermore, the number of modes should be appropriately selected depending on the number of processing points. In addition, the application of the Kalman filter significantly improves the estimation accuracy with a small number of processing points while suppressing the processing time.When the flow velocity fields with different angles of attack are used as the training data with that of test data, the estimation using SPPIV is found to be reasonable if the difference in angle of attack between the training and test data is equal to or less than 2 deg and the flow phenomena of the training data are similar to that of the test data. For this reason, training data should be prepared at least every 4 deg. Finally, the demonstration of SPPIV as a real-time flow observation was conducted for the first time. In this demonstration, the real-time measurement is found to be possible at a sampling rate of 2000 Hz at 20 or less processing points in the top 10 modes estimation as expected by the off-line analyses.

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“…Nonomura et al [33] proposed a Kalman filter-based DMD method for parameter estimation or system identification in the case of observation noise. Unlike [33,34] which used KF to obtain precise DMD modes, Gomez et al [35,36] used the DMD method to define the linear dynamic system and then performed Kalman filtering for state estimation (referred to as DMD-KF), which was used for full flowfield estimates from distributed pressure sensors. Fathi et al [37] and Jiang and Liu [38] used KF and its variants to denoise observation data and proposed KF-DMD, EnKF-DMD, and DMD-KF-W methods to reconstruct noisy deterministic dynamic systems and random dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Song,

Chen,

Wang

et al. 2024

Meas. Sci. Technol.

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Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic Mode Decomposition with control (DMDc) is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k) is input into the k-nearest neighbor (kNN) classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and V k (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k, and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

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Data-Driven Estimation of the Unsteady Flowfield Near an Actuated Airfoil

Cited by 30 publications

References 28 publications

Autoencoder-Based Reduced Order Observer Design for a Class of Diffusion-Convection-Reaction Systems

Autoencoder-Based Reduced Order Observer Design for a Class of Diffusion-Convection-Reaction Systems

Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

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