Unsteady DMD-Based Flow Field Estimation From Embedded Pressure Sensors in an Actuated Airfoil

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

doi:10.2514/6.2019-0346

Cited by 13 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fish extract such information about the source of a vortex field from the fluid flow using a grouping of mechanosensors, called the lateral line [7,[20][21][22][23][24][25][26] which are made up of hundreds of neuromasts spread over their body that can detect subtle water motion and pressure gradients [27]. Considerable research and engineering has been devoted to creating artificial lateral lines through a variety of electromechanical sensors such as miniature pressure sensors [28][29][30][31][32], ionic polymer-metal composite sensors [33,34], multi-layered silicon beams [35], and micro-fabricated hot-wire anemometry sensors [36]. While fish lateral lines and their artificial counterparts have received considerable attention, proprioceptive sensing, the ability to use the kinematics or motion of the body or parts of it to sense, has been unexplored in bioinspired swimming robots.…”

Section: Introductionmentioning

confidence: 99%

Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers

Pollard¹,

Tallapragada²

2021

Bioinspir. Biomim.

View full text Add to dashboard Cite

Objects moving in water or stationary objects in streams create a vortex wake. Such vortex wakes encode information about the objects and the flow conditions. Underwater robots that often function with constrained sensing capabilities can benefit from extracting this information from vortex wakes. Many species of fish do exactly this, by sensing flow features using their lateral lines as part of their multimodal sensing. To replicate such capabilities in robots, significant research has been devoted to developing artificial lateral line sensors that can be placed on the surface of a robot to detect pressure and velocity gradients. We advance an alternative view of embodied sensing in this paper; the kinematics of a swimmer’s body in response to the hydrodynamic forcing by the vortex wake can encode information about the vortex wake. Here we show that using artificial neural networks that take the angular velocity of the body as input, fish-like swimmers can be trained to label vortex wakes which are hydrodynamic signatures of other moving bodies and thus acquire a capability to ‘blindly’ identify them.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers

Pollard¹,

Tallapragada²

2021

Bioinspir. Biomim.

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…Researchers have long been captivated by the sensing capabilities of the lateral line and have sought to mimic these. Considerable research and engineering has been devoted to creating artificial lateral lines through a variety of electromechanical sensors such as miniature pressure sensors [22][23][24][25][26], ionic polymermetal composite sensors [27,28], multi-layered silicon beams [29], and micro-fabricated hot-wire anemometry sensors [30], and these sensors have been useful to perform state estimation [31] and improve the swimming efficiency of robots [24]. The whiskers of aquatic mammals have been shown to serve a similar role in flow detection by translating the fluid flow into detectable whisker vibrations [32].…”

Section: Introductionmentioning

confidence: 99%

Proprioceptive wake classification by a body with a passive tail

2023

View full text Add to dashboard Cite

The remarkable ability of some marine animals to identify flow structures and parameters using complex non-visual sensors, such as lateral lines of fish and the whiskers of seals, has been an area of investigation for researchers looking to apply this ability to artificial robotic swimmers, which could lead to improvements in autonomous navigation and efficiency. Several species of fish in particular have been known to school effectively even when blind. Beyond specialized sensors like the lateral lines, it is now known that some fish use purely proprioceptive sensing, using the kinematics of their fins or tails to sense their surroundings. In this paper we show that the kinematics of a body with a passive tail encode information about the ambient flow, which can be deciphered through machine learning. We demonstrate this with experimental data of the angular velocity of a hydrofoil with a passive tail that lies in the wake generated by an upstream oscillating body. Using convolutional neural networks we show that with the kinematic data from the downstream body with a tail, the wakes can be better classified than in the case of a body without a tail. In fact this superior sensing ability exists for a body with a tail, even if only the kinematics of the main body are used as input for the machine learning. This shows that beyond generating `additional inputs' passive tails modulate the response of the main body in manner that is useful for hydrodynamic sensing. These findings have clear application for improving the sensing abilities of bioinspired swimming robots.

show abstract

Unsteady DMD-Based Flow Field Estimation From Embedded Pressure Sensors in an Actuated Airfoil

Cited by 13 publications

References 21 publications

Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers

Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers

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

Proprioceptive wake classification by a body with a passive tail

Contact Info

Product

Resources

About