Reconstruction Of Acoustic Field Research Articles

Temperature and Acoustic Field Reconstruction in Fiber Optic DAS System Based on Variational Mode Decomposition

Temperature and Acoustic Field Reconstruction in Fiber Optic DAS System Based on Variational Mode Decomposition

Research on the effects of resonance tube bending in loop thermoacoustic refrigeration system

A high-power-density heat-driven loop thermoacoustic refrigeration system has the potential to meet miniaturization requirements of vehicle waste heat recovery systems. Given the strict space constraints in vehicle power compartments and the need to reduce equipment footprint, the resonance tubes need to be bent and coiled. However, the introduction of curvature may increase losses in resonance tubes. In order to evaluate the minor losses caused by bending on resonance tubes, a double-acoustic-source driven acoustic field modulation platform was constructed to experimentally investigate bent resonance tubes with different arc lengths under two common bending angles (90° and 180°) under high working pressure. Through the acoustic field reconstruction method, the acoustic field distributions of each resonance tube were obtained, and the effects of changes in bending angle and arc length on acoustic field and acoustic power dissipation were analyzed. The results indicated that the introduction of curvature leads to an increase in traveling-standing wave ratio, acoustic power, and acoustic power dissipation, with the degree of increase influenced by both curvature and arc length. Under the same bending angle, the minor loss is directly proportional to curvature, and there is an ExpAssoc function relationship between minor loss coefficient and curvature. Under the same curvature, the minor loss is not proportional to arc length, and minor loss coefficient slightly increases with the arc length doubles. The findings fill the research gap in resonance tube bending in loop thermoacoustic systems and provide guidance for the selection of resonance tube bending parameters.

Laser interferometer measurement and reconstruction of supersonic projectile acoustic pressure fields

Sensing acoustic fields with a laser interferometer offers several advantages over conventional techniques: the probe beam does not disturb the medium in which it operates, the interferometer optics can be placed far from the source, and interferometry typically has a wider bandwidth than microphones. These advantages are particularly useful in situations where precise measurements of high-amplitude shock-generating sources are required. In this work we use a laser interferometer to study the generation and propagation of shock wave signatures of ballistic projectiles. A laboratory experiment was conducted to collect interferometer data from a variety of supersonic projectiles over a range of standoff distances. Simultaneously-captured microphone data is compared to the interferometer measurements, and techniques for inversion and reconstruction of the sound pressure field are discussed.

Mode-informed complex-valued neural processes for matched field processing

Matched field processing (MFP) is a key technique for passive underwater source localization, estimating the source position by matching array measurements with acoustic model replicas. Its effectiveness relies on matching environmental parameters with the actual oceanic environment, but performance declines with environmental mismatches and lower signal-to-noise ratios. This paper proposes a novel approach that integrates neural networks (NNs) and complex Gaussian processes with modal depth functions for acoustic field reconstruction that is more accurate and efficient compared to Gaussian process regression. A meta-learning strategy is used to optimize parameters of the NN. The reconstructed data are denoised and interpolated, generating densely populated acoustic fields at virtual arrays, which are then used as data in MFP. Replicas are also computed at the virtual receivers. This mode-informed complex-valued neural processes enhance MFP performance, particularly in low SNR and mismatched environments. It captures the propagation properties of underwater acoustic fields more comprehensively, showing superior localization performance in both simulated and real-world data from the SWellEx-96 Event S5 environment.

Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction

Acoustic holography (AH) provides a promising technique for arbitrary acoustic field reconstruction, supporting many applications like robotic micro-nano manipulation, neuromodulation, volumetric imaging, and virtual reality. In AH, three-dimensional (3D) acoustic fields quantified with complex-valued acoustic pressures are reconstructed by virtue of two-dimensional (2D) acoustic holograms. Phase-only hologram (POH) is recently regarded as an energy-efficient way for AH, which is typically implemented by a dynamically programmable phased array of transducers (PATs). As a result, spatiotemporal precise acoustic field reconstruction is enabled by precise, dynamic, and individual actuation of PAT. Thus, 2D POH is required per arbitrary acoustic fields, which can be viewed as a physical inverse problem. However, solving the aforementioned physical inverse problem in numerical manners poses challenges due to its non-linear, high-dimensional, and complex coupling natures. The existing iterative algorithms like the iterative angular spectrum approach (IASA) and iterative backpropagation (IB) still suffer from speed-accuracy trade-offs. Hence, this paper explores a novel physics-iterative-reinforced deep learning method, in which frequency-argument contrastive learning is proposed facilitated by the inherent physical nature of AH, and the energy conservation law is under consideration. The experimental results demonstrate the effectiveness of the proposed method for acoustic field reconstruction, highlighting its significant potential in the domain of acoustics, and pushing forward the combination of physics into deep learning.

Deep learning-empowered moving cascaded acoustic holography for high-fidelity and high-capacity acoustic holographic reconstruction

Acoustic holograms offer a promising platform for reconstructing complex acoustic fields with high resolution in three dimensions. However, traditional design algorithms based on single acoustic holograms have limitations in terms of their information capacity. To address this challenge, our study proposes a novel strategy called deep learning-empowered moving cascaded acoustic holography. This strategy enables high-capacity target patterns to be encoded onto two moving cascaded acoustic phase plates at a set of movable distances along the forward diffractive path of the incident acoustic wave. To achieve the high-fidelity and high-capacity acoustic holographic reconstruction, we develop a physics-enhanced moving cascaded acoustic hologram deep neural network (PhysNet_MCAH) method, which employs a physics-driven neural network to inversely design the phase offset distributions of two phase plates. Our results demonstrate that the PhysNet_MCAH method effectively generates high-fidelity and high-capacity moving cascaded acoustic holograms that are capable of rendering different complex target patterns at different design movable distances. Additionally, we show that moving cascaded acoustic holograms significantly improve the holographic storage capacity and enhance acoustic field reconstruction accuracy, surpassing the capabilities of moving single acoustic holograms. Remarkably, we further demonstrate that the proposed PhysNet_MCAH method achieves a superior performance in terms of the trade-off between reconstruction quality and holographic storage capacity when compared to existing unsupervised deep learning methods and multiplexing strategies for designing the multi-plane or cascaded holograms. Our method offers a new paradigm for high-capacity acoustic volumetric display, dynamic particle manipulation, and other applications.

Discrete Multi-Step Phase Hologram for High Frequency Acoustic Modulation

Abstract Acoustic holograms can recover wavefront stored acoustic field information and produce high-fidelity complex acoustic fields. Benefiting from the advantages of the huge spatial information content unmatched by traditional acoustic components, acoustic holograms pursue the realization of high-resolution complex acoustic fields and gradually tend to high-frequency ultrasound applications. However, conventional continuous phase holograms are limited by 3D printing size and the presence of unavoidable small printing errors make it difficult to achieve acoustic field reconstruction at high frequency accuracy. Here, we present an optimized discrete multi-step phase hologram. It can guarantee the reconstruction quality of image with high robustness, and properly lower the requirement of the 3D printing accuracy. Meanwhile, the concept of reconstruction similarity is proposed to refine a measure of acoustic field quality. In addition, the realized complex acoustic field at 20 MHz promotes the application of acoustic holograms in high frequencies and provides a new way to generate high-fidelity acoustic fields.

Reconstruction of transient acoustic field using sparse real-time near-field acoustic holography

A sparse real-time near-field acoustic holography method is proposed to precisely and stably reconstruct a transient sound field. By a time-domain impulse response function, a time domain convolution equation between the pressure time-wavenumber spectra on the hologram and reconstruction planes is first established. Then, a smoothed ℓ0-norm optimization algorithm is applied to solve the serious ill-conditioned problem of the inverse process to get the pressure time-wavenumber spectrum of the reconstruction plane. The key part of solving this problem is to approximate replace the discontinuous ℓ0-norm by using a suitable continuous Gaussian function family, and a steepest ascent algorithm is introduced to minimize the continuous function for obtaining the optimal solution. Finally, the pressure time-wavenumber spectra of the reconstruction plane for all wavenumbers at all times are solved, and the corresponding time-dependent pressures are gained by the two-dimensional space inverse Fourier transform. A numerical simulation is conducted to observe the reconstruction capacity of the proposed method. The simulation effect prove that the proposed method can accurately reconstruct the transient sound field, and its reconstruction results are compared to those of the real-time near-field acoustic holography (RTNAH) with Tikhonov regularization and YALL1 modal for verifying the superiority of the proposed method. The YALL1 module utilizes an alternating direction algorithm for solving the ℓ1 minimization problems. Several parameters including the noise, position of the holograph plane and two initialization values are discussed in the simulation. The comparison and discussion results show that the reconstruction effect of the proposed method is much better than those of RTNAH with Tikhonov regularization and YALL1 modal under different parameter configurations. An experiment with a percussive steel plate is implemented for further validating the effectivity of the proposed method.

Towards reconstruction of acoustic fields via physics-informed neural networks

Acoustic measurements, obtained by microphones positioned at strategic places, are of great utility for the monitoring of a given acoustic system and for its protection in case large pressure fluctuations are measured. Such strategies are reliable as long as the microphones are properly positioned, which is not evident: in some cases the excited acoustic modes are not known beforehand. In this work, we proposed a method based on physics informed neural networks (PINN) in order to reconstruct the entire acoustic field of a given acoustic element, provided some acoustic measurements at some few locations. Such a method makes use of a feed forward neural network, where the cost function is taken as the residual of the acoustic wave equation. Such a residual is computed exploiting the automatic differentiation property of neural networks, in order to obtain the corresponding spatial and time derivatives. Additionally, the measurements of the aforementioned microphones are gathered and used also for the calculation of additional terms in the PINN cost function. By doing so, the most adequate acoustic state is obtained, which satisfy both measurements and the acoustic wave equation. In other words, the acoustic field within the system is reconstructed.

On-grid compressive sampling for spherical field measurements in acoustics.

We derive a compressive sampling method for acoustic field reconstruction using field measurements on a pre-defined spherical grid that has theoretically guaranteed relations between signal sparsity, measurement number, and reconstruction accuracy. This method can be used to reconstruct band limited spherical harmonic or Wigner D-function series (spherical harmonic series are a special case) with sparse coefficients. Contrasting typical compressive sampling methods for Wigner D-function series that use arbitrary random measurements, the new method samples randomly on an equiangular grid, a practical and commonly used sampling pattern. Using its periodic extension, we transform the reconstruction of a Wigner D-function series into a multi-dimensional Fourier domain reconstruction problem. We establish that this transformation has a bounded effect on sparsity level and provide numerical studies of this effect. We also compare the reconstruction performance of the new approach to classical Nyquist sampling and existing compressive sampling methods. In our tests, the new compressive sampling approach performs comparably to other guaranteed compressive sampling approaches and needs a fraction of the measurements dictated by the Nyquist sampling theorem. Moreover, using one-third of the measurements or less, the new compressive sampling method can provide over 20 dB better de-noising capability than oversampling with classical Fourier theory.

Multifunctional acoustic holography based on compact acoustic geometric-phase meta-array

Optical geometric-phase metasurfaces provide a robust and efficient means for light wave control by simply manipulating the spatial orientations of the in-plane anisotropic meta-atoms, where polarization conversion plays a vital role. However, the concept of acoustic geometric-phase modulation for acoustic field control remains unexplored because airborne acoustic waves lack a similar optical polarization conversion process. In this work, a new type of acoustic meta-atom with deep-subwavelength feature size is theoretically investigated and further applied to acoustic field engineering based on the emerged concept of acoustic geometric phase. Herein, tunable acoustic geometric-phase modulation of designated order is obtained via the near-field coupled orbital angular momentum transfer process, and the topological charge-multiplexed acoustic geometric phase endows our meta-arrays with multiple functionalities. Our work extends the capacity of the acoustic geometric-phase meta-arrays in high-quality acoustic field reconstruction and offers new possibilities in multifunctional acoustic meta-holograms.

Krylov subspace iterative methods for time domain equivalent sources method based nearfield acoustical holography

This paper studies the acoustic field reconstruction on an arbitrarily shaped vibrating surface using the time-domain equivalent source method (ESM) based near-field acoustic holography (NAH) from measurements of the pressure field on a nearby conformal surface. For the classical frequency-dependent ESM, we represent the acoustic field by a set of frequency-dependent monopole sources strategically distributed over a close and conformal surface to the vibrating structure. We demonstrated that in this setup, the ESM representation approximates the classical integral representations, which justifies the ESM as an extension to the classical integral theory. For time-domain ESM in a similar manner, we demonstrate that a set of time-dependent monopole sources approximates the single layer retarded potential and then characterize the acoustic field. The discretization of time-domain ESM produces a matrix system with a particular sparse structure that can be efficiently solved using Krylov subspace iterative methods. The iterative methods that will be of our interest are Conjugate Gradients Normal equation Residual, Conjugate Gradients Normal equation Error and Least Squares QR. To reduce the amplification of measurement in the numerical reconstructions, we combine the proposed iterative methods with heuristic stopping rules like L-curve, Quasi-Optimality, Hanke–Raus, Brezinski–Rodriguez–Seatzu and a posteriori stopping rule. To determine the best combination of reconstruction technique, iteration and stopping rule, we develop a statistical study that uses multiple samples of numerically generated data by Gaussian pulses over a sphere. We produce these samples using a random source position and different noise levels. Finally, we validate our proposed methodology using scattering data from an elastic spherical shell.

Noise source identification of high-speed motion mechanism of textile equipment based on equivalent source method

In order to accurately identify the noise sources of high-end textile equipment and achieve noise control, this paper takes warp knitting machine as an example to accurately locate the main voice mechanism. Firstly, the stability and accuracy of traditional equivalent source method (TRESM) and Bayesian regularization criterion method for acoustic field reconstruction in different frequency bands are simulated and verified. Secondly, the accuracy of iterative reweighted least squares (IRLS) and steepest descent iteration equivalent source method (SDIESM) in medium- and high-frequency bands is verified. The results show that TRESM and Bayesian methods are suitable for the identification of medium- and low-frequency noise sources, and IRLS and SDIESM algorithms have better adaptability to complex sound fields. Bayesian method, IRLS and SDIESM algorithm can be used to identify the noise sources of broadband warp knitting machine. The main noise sources are spindle motor, pulling roller, spindle of loop forming mechanism and push rod of comb bar transverse mechanism, which provide theoretical support for active noise reduction of loom.

Robust reconstruction of equivalent source method based near-field acoustic holography using an alternative regularization parameter determination approach.

The core of equivalent source method based near-field acoustic holography is to solve the equivalent source strength with the measured pressure and the transfer matrix, which is a typical ill-posed inverse problem. In this work, an explicit formula is proposed in which the regularization parameter is proportional to the product of the hologram distance and the maximum singular value of the transfer matrix. Utilizing the proposed approach, robust acoustic field reconstruction can be achieved even when the hologram distance is 5 times larger than the average microphone spacing.

Acoustic Signature Analysis for Localization Estimation of Unmanned Aerial Vehicles Using Few Number of Microphones

In recent years, the current technological improvements of Unmanned Aerial Vehicles (UAV) have allowed more and more efficient use for applications ranging from simple amateur shooting to more professional tasks. Being handy, drones can easily fly near sensitive sites, such as power plants, airports or ministries. It is therefore necessary to develop systems able to keep watch such sites. However, the size and the composition of these devices make optical or electromagnetic systems inefficient for their detection. This study proposes an alternative exploiting the sound wave emitted by their motorization and / or their aerodynamic whistling. For this, an acoustic antenna with few microphones was sized to be sensitive to frequencies emitted by a drone. Firstly, an experimental analysis on the noise made by a drone was carried out. The results allowed us to settle the geometry of the antenna in order to process localization. Two methods of location are used. The first is based on an energy approach providing the acoustic field reconstruction in all directions of space. The second estimates the position of the source by inverting a system exploiting the arrival time differences of the acoustic wave between different pairs of microphones. Numerical simulations, supported by a measurement campaign, make it possible to highlight the performance of the methods used.

Acoustic Scattering Field Reconstruction Using Truncated Total Least Square Algorithm

Acoustic scattering-field reconstruction of structures with arbitrary shape is the research basis of the scattering characteristics for underwater targets. Firstly, using boundary element method (BEM) and acoustic radiation modes (ARMs) solution in fluid domain, it is proved that the scattering pressures can be expressed by ARMs. Secondly, the acoustic field distribution modes (AFDMs) are constructed by ARMs and a new acoustic transfer matrix (ATM) which is acquired by the simplification of the traditional ATM. At the same time, the scattering pressures can be expressed as the product of the AFDMs and the modal expansion coefficients. Thus, the scattering reconstruction problem is converted into the exact solution problem of the modal expansion coefficients. Aiming at the existing noise of both the pressures at measuring point and AFDMs, the total least square (TLS) algorithm is introduced to acquire the accurate solution. Further, considering the ill-conditioned AFDMs matrix, the truncated total least square (TTLS) algorithm is introduced to solve the modal expansion coefficients. Simulation results show that the capability of resisting noise contamination is limited for the reconstruction algorithm based on TLS and that the reconstruction algorithm based on TTLS has a better denoising performance than the TLS one. At the same time, for smaller wave numbers, the modal orders for reconstruction at different noise levels are approximately equal and the reconstruction errors are small. The simulation results also demonstrate that the reconstruction algorithm based on TTLS has a better denoising performance at smaller wave numbers than at higher wave numbers. For the higher wave numbers, the modal orders for reconstruction decrease and the reconstruction errors increase with the decrease of the signal-to-noise ratio (SNR). For the backward reconstruction at smaller wave numbers, the influence to reconstruction results, which arises from structure complexity ascending and evanescent waves existing, should be considered when the reconstruction surfaces are near the structures.

A hybrid distributed optical fibre sensor for acoustic and temperature fields reconstruction

Abstract We have proposed and experimentally demonstrated a distributed optical fibre sensor (DOFS) based on the hybrid of phase-sensitive optical time domain reflectometry ( Φ -OTDR) and Raman optical time domain reflectometry (ROTDR), which could reconstruct the acoustic and temperature fields all along the sensing fibre at the same time. The Rayleigh back scattering (RBS), Raman Stokes (RS) and Raman anti-Stokes (RAS) generated by the probe pulse were separated by a WDM filter. Then the RBS was received by a heterodyne balanced photo detector (BPD) for quantitative vibration measurement and the dynamic acoustic field reconstruction, while the RS and RAS components were received directly by avalanche photodiodes (APDs) for temperature demodulation. The experimental results have shown that the proposed hybrid system could measure the temperature profile along a 12km long standard single-mode fibre with a maximal temperature uncertainty of 0.95 °C; and a linearity response of R 2 = 0 . 9986 , along with a periodical knocking event detected simultaneously. Meanwhile, a 100 Hz sinusoidal vibration at 12 km distance along the same fibre could be clearly identified. Distributed knocking vibration and temperature was achieved at the same time with high accuracy.

Time domain nearfield acoustical holography with three-dimensional linear deconvolution.

In this paper, a formulation is proposed to improve the time domain reconstruction of non-stationary acoustic fields with nearfield acoustical holography. The formulation involves applying three-dimensional (3D) linear deconvolution using a Green's function sampled in the time and spatial domains. Because linear deconvolution does not assume periodic signals, it more appropriately describes the decaying behavior of sources that begin and end at null amplitude and that radiate over a finite amount of time. The proposed method outperforms standard circular convolution-based nearfield acoustical holography by up to a factor three in relative root-mean-square error, when compared using a transient baffled piston model, and its reconstructions remain accurate over large back-propagation distances. Furthermore, it is shown that truncation errors in linear deconvolution can be reduced by applying a 3D patch extrapolation algorithm; however, convergence depends on the choice of an adequate Tikhonov's regularization parameter. Three methods for predicting the optimal parameter are compared: the L-curve, the generalized cross-validation, and the empirical Bayesian method. It is shown that with the proposed formulation applied to reconstructing the field radiated by a transient baffled piston, the generalized cross-validation gives the overall best prediction for the noise levels and back-propagation distances studied.

Reconstructing the acoustic far-field using a semi-circular sensor array

A method to reconstruct the acoustic far-field using a semi-circular sensor array is proposed. Firstly, the pressures in the acoustic far-field are measured by a semi-circular sensor array; then, the acoustic far-field is reconstructed based on the theory of acoustic radiation modes. The numerical simulations for reconstructing the acoustic far-field of a baffled rectangular plate are conducted. The results show that the acoustic far-field of the baffled rectangular plate can be reconstructed accurately by a few sensors on the semi-circular array. The influence of noise to the reconstruction of acoustic field is analyzed, and the robustness of the proposed method is proved. This paper provides a method for the reconstruction of acoustic far-field through a simple measuring array. The method can be applied to the acoustic field reconstruction of large structures, and has good engineering application prospects.

An Inverse Direct Time Domain Boundary Element Method for the Reconstruction of Transient Acoustic Field

An inverse direct time domain boundary element method (IDTBEM) is proposed for the reconstruction of transient acoustic field radiated by arbitrarily shaped sources. The method is based on the theory of direct time domain boundary element method (DTBEM), which is free from the calculation of hypersingular integrals, and thus, its reconstruction process is relatively simple and easy to implement. However, the formulations of DTBEM cannot be used directly for the reconstruction of transient acoustic field, and therefore, new formulations with a modified time axis are derived. With these new formulations, a linear system of equations is formed and the reconstruction is performed in a marching-on-time (MOT) way. Meanwhile, to deal with the ill-posedness involved in the inverse process, the truncated singular value decomposition (TSVD) is employed. Numerical simulations with three examples of a sphere, a cylinder, and a simplified car model are carried out to verify the validity of IDTBEM, and the results demonstrate that the IDTBEM is effective in reconstructing the transient acoustic fields radiated by arbitrarily shaped sources in both time and space domains.