Acoustic Waveform Research Articles

Acoustic source characterization for chemical explosions in air

Chemical explosions in the air generate large pressure disturbances. These pressure waves propagate as non-linear shock waves near the source and transition into acoustic waves in the far-field. Since low-frequency acoustic waves propagate long distances without significant loss of energy, acoustic signals induced by explosions are often used to determine explosion energies of the events in terms of an explosion yield. In order to estimate the explosion energy accurately, the relationship between acoustic energy and explosion yield must be understood. However, explosion yields are often measured by non-linear shock waves in the near-field, and it is not clear how much acoustic energy accounts for the explosion energy. In addition, acoustic signals typically have lower-frequency contents than shock waves in the near-field, and hence frequency-dependent explosion energy should be understood to accurately infer explosion yields based on acoustic observations. In this study, we investigate the relationship between acoustic energy and explosion yield based on ground-truth explosion data. A standard acoustic source waveform will be determined by acoustic observations, and frequency-dependent energy will be explored for yield estimation. We will demonstrate that this frequency-dependent acoustic source characterization can improve the accuracy and confidence of explosion yield estimation.

Characterization of the acoustic pressure waveforms in Rijke tubes with spatially varying heat sources and temperature distributions

In this work, we implement an asymptotic expansion technique that leverages a small perturbation parameter that arises in the context of one-dimensional tubes with open-open end point configurations and spatially varying heat sources. This approach, when paired with a spectral collocation eigensolver, enables us to produce accurate predictions of the pressure mode shapes and frequencies over a wide range of parameters. These include the temperature gain across the heat source, the heat source length and location, and the overall thermal profile. The latter is intended to reproduce different flow heating configurations that emulate Rijke tube characteristics. Specifically, this investigation begins by considering three piecewise representations of the heat source by juxtaposing constant–constant temperatures before and after a heating element whose temperature is prescribed locally using three analytical functions: linear, exponential, and power-law profiles. This is followed by a logistic distribution that can be globally applied to provide a uniformly valid, continuous, and differentiable thermal profile spanning the entire tube, including the heat source element. Our fundamental formulation relies on Green's functions and an integral formulation that enables us to extract all acoustic frequencies analytically. These are found to increase monotonically with successive elevations in the temperature gain across the heat source, retractions of the heat source, length reductions in the heat source, and smoothing of the temperature gain. Along similar lines, the pressure mode shapes are found to exhibit blunter and often linear variations for higher temperature gains, longer heat sources, and upstream displacements of the heat source toward the inlet.

Further development of high-amplitude underwater sound sources at The University of Texas Applied Research Laboratories

Following the early research on the Combustive Sound Source (CSS), described in the previous talk, additional development occurred in the 2000’s and beyond, which included work on both the CSS and a new source, the Rupture Induced Underwater Sound Source (RIUSS). Development of the CSS in this era included maximizing the output of the source, investigating the use of CSS in arrays, and towing the source. The CSS was also deployed in surveys conducted during Shallow Water ’06 and the Seabed Characterization Experiment. In part due to lessons learned during those surveys, the RIUSS was conceived and developed. The RIUSS functions by placing a rupture disk over an evacuated chamber and mechanically breaking the disk (either by striking on demand or via hydrostatic pressure) at a specified depth to initiate a volume collapse that produces an impulsive acoustic waveform. The development and use of each sound source will be provided along with high-speed underwater video, examples of signatures, and examples of the efficacy of the sources in ocean acoustics research. [Work Supported by ONR and NAVO].

Mapping Acoustic Frictional Properties of Self-Lubricating Epoxy-Coated Bearing Steel with Acoustic Emissions during Friction Test

This work investigates the stick–slip phenomenon during sliding motion between solid lubricant-impregnated epoxy polymer-coated steel bars and AISI 52,100 steel balls. An acoustic sensor detected the stick–slip phenomenon during the tribo-pair interaction. The wear characteristics of the workpiece coated with different epoxy coatings were observed and scrutinized. The RMS values of the acoustic sensor were correlated with the frictional coefficient to develop a standard based on the acoustic sensor, leading to the detection of the stick–slip phenomenon. As per the findings, the acoustic waveform remained relatively similar to the friction coefficient observed during the study and can be used effectively in detecting the stick–slip phenomenon between steel and polymer interaction. This work will be highly beneficial in industrial and automotive applications with a significant interaction of polymer and steel surfaces.

Acoustic Emission Waveform Characterization of Crack Origin In Self-Healing of Mortar Due to Internal Carbonation

In this study, self-healing of mortar was achieved by “built-in” carbonation of soluble Na2CO3 and Ca(OH)2. The effect of carbonate and calcium ions, available either internally or externally by conditioning the specimens with Ca(OH)2 and Na2CO3 solution, on the formation of calcite in cracks was investigated. The acoustic events were monitored and compared in the loading process before and after healing. Furthermore, a calibration test was carried out to distinguish the characteristic acoustic emission events of the fracture of the matrix and of healing products. It was found that the distribution of acoustic energy with FMA (frequency at maximum amplitude) and the hits with duration show a consistent trend with that of calibration. The change of tensile to shear cracking ratio in reloading illustrates a self-healing effect of cracks. Meanwhile, X-ray diffraction analysis indicates more calcite formed in the crack of self-healing specimens. The pretreated ceramsite-containing specimens exhibit the predominate self-healing effect due to the internally available CO32– and sufficient Ca2+ as compared to the others.

A Novel Feature Representation Method Based on Distances Between Statistical Matrices of Acoustic Emission Waveforms

A Novel Feature Representation Method Based on Distances Between Statistical Matrices of Acoustic Emission Waveforms

Monitoring of Laser Powder Bed Fusion process by bridging dissimilar process maps using deep learning-based domain adaptation on acoustic emissions

Advances in sensorization and identification of information embedded inside sensor signatures during manufacturing processes using Machine Learning (ML) algorithms for better decision-making have become critical enablers in building data-driven monitoring systems. In the Laser Powder Bed Fusion (LPBF) process, data-driven-based process monitoring is gaining popularity since it allows for real-time component quality verification. Real-time qualification of the additively manufactured parts has a significant advantage as the cost of conventional post-manufacturing inspection methods can be reduced. Also, corrective actions or build termination could be done to save machine time and resources. However, despite the successful development in addressing monitoring needs in LPBF processes, less research has been paid to the ML model's robustness in decision-making when dealing with variations in data distribution from the laser-material interaction owing to different process spaces. Inspired by the idea of domain adaptation in ML, in this work, we propose a deep learning-based unsupervised domain adaptation technique to tackle shifts in data distribution owing to different process parameter spaces. The temporal waveforms of acoustic emissions from the LPBF process zone corresponding to three regimes, namely Lack of Fusion, conduction, and keyhole, were acquired on two different 316 L stainless steel powder distributions (> 45 µm and < 45 µm) with two different parameter sets. Temporal and spectral analysis of the acoustic waveforms corresponding to the powder distributions treated with different laser parameters showed the presence of offset in the data distribution, which was subsequently treated with the proposed unsupervised domain adaptation technique to have an ML model that could be generalized. Furthermore, the prediction accuracy of the proposed methodology between the two distributions showed the feasibility of adapting to the newer environment unsupervisedly and improving the ML model's generalizability.

Challenges and solutions pertinent to machine learning-based audio recognition

The problem of transcribing data from an acoustic waveform has yielded multiple approaches over the last decades. The current preferred approach involves a three-stage model that breaks the problem into its constituent stages, each with an equivalent model. The first stage divides pure acoustics and language study using a Bayesian model. The second stage focuses on the acoustics model; this model makes the most sufficient and efficient division. The third stage focuses on the acoustics model; this model provides complete instruction on how to compute the probability required by the first stage, given the division specification declared within the second stage.

Influence of MHz-order acoustic waves on bacterial suspensions

The development of alternative techniques to efficiently inactivate bacterial suspensions is crucial to prevent transmission of waterborne illness, particularly when commonly used techniques such as heating, filtration, chlorination, or ultraviolet treatment are not practical or feasible. We examine the effect of MHz-order acoustic wave irradiation in the form of surface acoustic waves (SAWs) on Gram-positive (Escherichia coli) and Gram-negative (Brevibacillus borstelensis and Staphylococcus aureus) bacteria suspended in water droplets. A significant increase in the relative bacterial load reduction of colony-forming units (up to 74%) can be achieved by either increasing (1) the excitation power, or, (2) the acoustic treatment duration, which we attributed to the effect of the acoustic radiation force exerted on the bacteria. Consequently, by increasing the maximum pressure amplitude via a hybrid modulation scheme involving a combination of amplitude and pulse-width modulation, we observe that the bacterial inactivation efficiency can be further increased by approximately 14%. By combining this scalable acoustic-based bacterial inactivation platform with plasma-activated water, a 100% reduction in E. coli is observed in less than 10 mins, therefore demonstrating the potential of the synergistic effects of MHz-order acoustic irradiation and plasma-activated water as an efficient strategy for water decontamination.

Ultrasonically induced nanofatigue monitoring during nanoindentation

The ultrasonic nanoindentation tip integrated into a nanoindenter operates at hundreds of kHz, inducing millions of load cycles within seconds at the localized mechanical contact point. Then operating on brittle submicron thick films such as SiC, the resulting accumulated nanofatigue exhibits different contact fracture modes. During the process of quasi-static loading with ultrasonic excitation, the ultrasonic nanoindentation tip monitors associated acoustic waveforms, which can provide additional insights into the nanofatigue process dynamics via advanced acoustic waveform analysis and deep learning classifications.

Multifunctional comb-like acoustic metasurface for transmissive wavefront manipulation

Precise and highly efficient wavefront modulation is greatly appreciated in acoustic engineering. Here, a comb-like acoustic metasurface is developed to efficiently manipulate the transmitted wavefront. The comb-like unit consists of an array of rigid rectangular partitions supported by a base. By adjusting the height of these rectangular elements, the entire 2π transmission phase shift can be achieved. The controllability of the phase shifts is demonstrated through theoretical and numerical analysis. A particle swarm optimization algorithm is employed to determine the height of the rectangular elements to achieve accurate target phase shift while maintaining high transmission efficiency. As a case study, highly efficient acoustic focusing and waveform conversion are achieved by comb-like metasurfaces, and the effect of unit deficiency on focusing performance is discussed. Furthermore, the application of comb-like metasurfaces to generate a self-bending beam and Airy beam is verified, and the acoustic beam of self-repair performance and robustness are demonstrated. In addition, acoustic focusing is realized using two symmetric Airy beams generated from the comb-like metasurface. Experiments are conducted to verify the effectiveness of the designed metasurfaces. This work presents an effective and feasible design solution for flexible wavefront modulation.

Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged.

Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle the effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise-vocoded, ~20 s duration) are presented twice, but the second presentation is preceded by the original (nondegraded) version of the speech. This intermediate priming, which generates a "pop-out" percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affect acoustic and linguistic neural representations using multivariate temporal response functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. mTRFs analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming but only by the acoustics of the stimuli (bottom-up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later (~400 ms latency) word processing stage, in prefrontal cortex, in line with engagement of top-down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension.

Fractional derivative-based approximation of acoustic waveform dispersion measured in bubbly water beyond resonance frequency.

Acoustic pulses transmitted across air bubbles in water are usually analyzed in terms of attenuation coefficient and phase velocity in the frequency domain. The present work expresses an analytical approximation of the acoustic waveform in the time domain. It is introduced by experiments performed with a Gaussian derivative source wavelet, S0(t), with a derivative order, β0 = 4, and a peak frequency, νp0, much larger than the bubble resonance frequency. The measurements highlight a significant shape variability of the waveform Bx(t), measured at x≤ 0.74 m and characterized by a peak frequency νpx≃νp0. The results are in good agreement with the approximation Bx(t)∝(dγx/dtγx)S0(δxt - T), where γx is an additional fractional-derivative order determined by an optimization procedure and T is related to the travel time. The time-scale parameter, δx=β0/(β0+γx), becomes a free parameter for more general source signals. The correlation coefficient between Bx(t) and the approximated waveform is used to identify the applicability of the method for a wide range of bubbly waters. The results may be of potential interest in characterizing gas bubbles in the ocean water column and, more generally, in modeling wave propagation in dispersive media with fractional-derivative orders in the time domain.

Effect of trapping and reflection on dust acoustic solitary waves in nonthermal opposite polarity dust plasmas

The study of resonant wave-particle interactions (WPIs) is crucial in plasma systems where charged plasma particles interact via long-range electromagnetic waves. Our research focuses on exploring the impact of trapping and reflection, along with the superthermality of Kappa resonance electrons and ions, on the characteristics of dust acoustic waves (DAWs) in opposite polarity dust plasma (OPDP). Both linear and non-linear analyses were conducted. Two distinct types of dust acoustic modes, namely fast and slow, have been observed in the linear regime of two different instances of WPIs. Moving on to the non-linear regime, the Schamel KdV (SKdV) equation has been derived using the reductive perturbation technique. In both cases, a stationary solution in the form of a dust acoustic double-layer wave (DADLW) has been successfully obtained. Our findings are highly relevant to astrophysical plasma environments with non-thermal trapped and reflected particles.

Statistics of acoustic emission waveforms in characterizing the fracture process zone in fibre-reinforced cementitious materials under mode I fracture

This article reports on the characteristics of fracture process zone in steel fibre-reinforced concrete (SFRC) under the mode I fracture process using acoustic emission (AE) testing. The generated AE waveforms during mode I fracture process in SFRC were recorded in the laboratory. Using a statistical analysis of AE waveforms, it was observed that as the loading increases, a damage zone consisting of numerous microcracks develops ahead of the predefined notch tip. The location of the generated AE events related to the numerous microcracks were classified into three zones namely (i) major damage, (ii) moderate damage and (iii) low damage. The areas of these regions were evaluated from the distribution of the AE events around the pre-notch. The number of AE events reduced with the increase in the steel fibre content under the same experimental conditions. The major damage zone was located ahead of the notch tip very closely and it comprised of AE events with (i) high peak amplitude, (ii) low information entropy and (iii) longer AE waveform duration.

A Closed-Loop PMSM Sensorless Control Based on the Machine Acoustic Noise

In this paper, a novel sensorless control for Permanent Magnet Synchronous Machine (PMSM) using its radiated acoustic noise is proposed in which a high-frequency voltage signal is superimposed upon the fundamental excitation and the machine response to such excitation is studied. Firstly, the analytical equations describing the field formation within the airgap leading to the generation of radial forces and acoustic noise are extensively investigated. In the next step, resulting harmonics in the acoustic noise waveform caused by the injected signals are predicted. Then it is experimentally verified that, depending on the machine structure, there will be some distinctive sidebands around the injected frequency directly related to the fundamental synchronous frequency. Acoustic noise waveform is highly distorted and sensitive to the environment, so a proposed 4-stage signal processing algorithm containing two self-adaptive Band-pass Filters (BPF) are used to safeguard the signal against unwanted distortions and flawlessly extract the rotor position information. Robustness and effectivity of this method is verified experimentally in various operating conditions.

Sonomechanobiology: High frequency cell mechanostimulation

It is well known that cells respond in many different ways to various forms of mechanical cues. To date, however, mechanostimulation has typically been carried out statically or at relatively low frequencies (several Hz), characteristic of the frequencies associated with the motion experienced by cells in their local environment, for example, in the human body (e.g., walking and running). Where higher frequency vibrational mechanotransduction pathways have been investigated, these have primarily been limited to kHz order, and it has generally been suggested that there is no significant advantage in utilising higher frequencies. In a similar manner to observations of new and often nonlinear discoveries when we couple high frequency (10–30 MHz) vibration in the form of surface and hybrid acoustic waves into fluids as well as crystalline materials, we however observe unique and novel phenomena when similar MHz-order vibrational stimuli are transmitted into cells. These include modulation of various ion and piezo channels, and transient, yet reversible, permeabilization of the cell membrane, which have implications for efficient intracellular cytosol delivery, exosome biogenesis, cytoskeletal reorganization and stem cell differentiation, at the same time maintaining very high levels (&amp;gt;95%) in cellular viability.

A Novel Feature Representation Method Based on Similarity Between Statistical Distributions of Acoustic Emission Waveforms

A Novel Feature Representation Method Based on Similarity Between Statistical Distributions of Acoustic Emission Waveforms

Accurate prediction of signal waveform in the convergence zone of the South China Sea based on frequency shift compensation

In a deep-sea environment in the South China Sea, a towed sound source is near the surface, and the acoustic signal is received by the vertical array of submersible buoys. The correlation values between the predicted and the measured signal waveform of the first two convergence zones (CZs) are poor, both below 0.5. The sea condition of the test area is good, the hydrological environment changes little in the horizontal direction. The analysis shows that when the underwater acoustic waveform is predicted, due to the relative motion of the transmitting and receiving elements, the channel dispersion caused by the Doppler effect leads to signal distortion. Therefore, the sampling frequency is broadened by 0.125 times to both ends, and the frequency shift corresponding to the maximum value of the waveform correlation peak is used as the optimal compensation for Doppler frequency offset. The results calculated by the RAM model show that the predicted and measured signal waveforms are in good agreement, and the correlation degree is significantly improved. Among them, the average normalized correlation values of the measured and predicted waveforms at different receiving depths in the first and second CZs are 0.84 and 0.82, respectively.

Recognition of shear and tension signals based on acoustic emission parameters and waveform using machine learning methods

Acoustic emission (AE) technology is widely used to monitor the damage evolution of rock. Identifying AE signals is crucial to reveal the rock cracking mechanism. The current tension and shear signal discrimination methods primarily rely on AE parameters. However, the differences between AE parameters and waveform images used as inputs require further exploration. Acoustic emission data from Brazilian splitting and direct shear tests of red sandstone were collected. A decision tree was applied to classify the signals based on the AE parameters: Rise time (RT), Counts, Amplitude, Peak frequency (PF), and absolute energy. Three typical CNN-based networks (InceptionV3, ResNet50, and VGG19) were used to classify the AE signals using images of waveforms, reconfigured waveforms, and spectrograms generated by wavelet packets. The results showed that using AE parameters as input to distinguish tensile and shear signals yields an accuracy of up to 88%, increasing recognition accuracy when using PF as input. In addition, increasing the number of input parameters to the decision tree can improve average recognition accuracy, with single, double, and three parameters being 65%, 78%, and 82%, respectively. However, when the number of parameters exceeds three, the accuracy improvement is minimal, with four and five parameters being 84% and 86%, respectively. The CNN-based networks outperform the decision tree in terms of recognition accuracy. It is recommended that the VGG19 network and waveform are adopted as input to identify the tension and shear AE signals. In contrast, using the spectrogram as input enhances the stability of the model, particularly for the validation set, although the improvement is limited.