Nearfield acoustic holography in a moving medium based on particle velocity input using nonsingular propagator

Nearfield acoustic holography in a moving medium is a technique which is typically suitable for sound sources identification in a flow. In the process of sound field reconstruction, sound pressure is usually used as the input, but it may contain considerable background noise due to the interactions between microphones and flow moving at a high velocity. To avoid this problem, particle velocity is an alternative input, which can be obtained by using Laser Doppler Velocimetry in a non-intrusive way. However, there is a singular problem in the conventional propagator relating the particle velocity to the pressure, and it could lead to significant errors or even false results. In view of this, in this paper nonsingular propagators are deduced to realize accurate reconstruction in both cases that the hologram is parallel to and perpendicular to the flow direction. The advantages of the proposed method are analyzed, and simulations are conducted to verify the validation. The results show that the method can overcome the singular problem effectively, and the reconstruction errors are at a low level for different flow velocities, frequencies, and signal-to-noise ratios.

A block bayesian compressive sensing method based on the equivalent source method for near-field acoustic holography

The conventional equivalent source method for near-field acoustic holography is an effective noise diagnosis method using microphone array. However, its performance is limited by microphone spacing, so the effect is unsatisfied when the wave number is high. In this paper, to broaden the frequency suitability and improve the performance of sound source reconstruction with low signal-to-noise ratios, a block Bayesian compressive sensing method based on the equivalent source method is proposed. Numerical results show that this proposed method has a good reconstruction performance and makes wideband reconstruction possible. By changing the frequency, location, and signal-to-noise ratio of the sound source, the reconstruction performance of the proposed method can remain stable. Finally, the validity and practicability of the proposed method are verified by experiments.

A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography

In this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surface of the vibrating structure using the sampled pressure soundfield on the holographic plane as input. The loss function used for training the network is based on a combination of two components. The first component is the error in the reconstructed velocity. The second component is the error between the sound pressure on the holographic plane and its estimate obtained from forward propagating the pressure and velocity fields on the structure through the Kirchhoff–Helmholtz integral; thus, bringing some knowledge about the physics of the process under study into the estimation algorithm. Due to the explicit presence of the Kirchhoff–Helmholtz integral in the loss function, we name the proposed technique the Kirchhoff–Helmholtz-based convolutional neural network, KHCNN. KHCNN has been tested on two large datasets of rectangular plates and violin shells. Results show that it attains very good accuracy, with a gain in the NMSE of the estimated velocity field that can top 10 dB, with respect to state-of-the-art techniques. The same trend is observed if the normalized cross correlation is used as a metric.

Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization

For the sound field reconstruction of large conical surfaces, current statistical optimal near-field acoustic holography (SONAH) methods have relatively poor applicability and low accuracy. To overcome this problem, conical SONAH based on cylindrical SONAH is proposed in this paper. Firstly, elementary cylindrical waves are transformed into those suitable for the radiated sound field of the conical surface through cylinder-cone coordinates transformation, which forms the matrix of characteristic elementary waves in the conical spatial domain. Secondly, the sound pressure is expressed as the superposition of those characteristic elementary waves, and the superposition coefficients are solved according to the principle of superposition of wave field. Finally, the reconstructed conical pressure is expressed as a linear superposition of the holographic conical pressure. Furthermore, to overcome ill-posed problems, a regularization method combining truncated singular value decomposition (TSVD) and Tikhonov regularization is proposed. Large singular values before the truncation point of TSVD are not processed and remaining small singular values representing high-frequency noise are modified by Tikhonov regularization. Numerical and experimental case studies are carried out to validate the effectiveness of the proposed conical SONAH and the combined regularization method, which can provide reliable evidence for noise monitoring and control of mechanical systems.