Emitted Acoustic Signals Research Articles

Method for adapting radioacoustic sounding systems of the atmosphere

Stations of radioacoustic sounding (RAS) of the atmosphere are a promising means of obtaining information about the altitudinal distribution of meteorological parameters in the Earth's atmosphere used in the process of solving current scientific and applied tasks to ensure aircraft flights, weather forecasting, etc. However, the effectiveness of the existing radio-acoustic means is insufficient, and there are practical needs for the development of appropriate prospective structures and algorithms, which will be implemented during the construction of specific stations designed to solve actual applied tasks. The article presents the synthesis of the RAS systems algorithm adaptation performed by changing the frequency of the sounding radio signal to fulfill the Bragg condition when the emitted acoustic pulse signal moves along the sounding path from the standpoint of optimal control theory. Since in the problem of synthesizing the algorithm for controlling the frequency of a radio signal, the disturbing and leading to violations of the Bragg condition during the propagation of an acoustic packet along the sounding path, as well as the process causing measurement errors, are considered as random, this problem is considered as a problem of stochastic optimal control. It is shown that in accordance with the separation theorem, known from the theory of optimal stochastic control, the method of controlling the frequency of a sounding radio signal should include sequentially peformed operations of forming estimates of the information parameter of the scattered radio signal, optimal linear filtering of the obtained estimates, and deterministic control of the frequency of the sounding radio signal.

Monitoring of Joint Gap Formation in Laser Beam Butt Welding using Neural Network-Based Acoustic Emission Analysis

This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible-to-ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for the low-latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model’s performance further. This suggests that there is room for future enhancements in the study.

A CFD-FFT approach to hemoacoustics that enables degree of stenosis prediction from stethoscopic signals

In this paper, we identify a new (acoustic) frequency-stenosis relation whose frequencies lie within the recommended auscultation threshold of stethoscopy (< 120 Hz). We show that this relation can be used to extend the application of phonoangiography (quantifying the degree of stenosis from bruits) to widely accessible stethoscopes. The relation is successfully identified from an analysis restricted to the acoustic signature of the von Karman vortex street, which we automatically single out by means of a metric we propose that is based on an area-weighted average of the Q-criterion for the post-stenotic region. Specifically, we perform CFD simulations on internal flow geometries that represent stenotic blood vessels of different severities. We then extract their emitted acoustic signals using the Ffowcs Williams-Hawkings equation, which we subtract from a clean signal (stenosis free) at the same heart rate. Next, we transform this differential signal to the frequency domain and carefully classify its acoustic signatures per six (stenosis-)invariant flow phases of a cardiac cycle that are newly identified in this paper. We then automatically restrict our acoustic analysis to the sounds emitted by the von Karman vortex street (phase 4) by means of our Q-criterion-based metric. Our analysis of its acoustic signature reveals a strong linear relationship between the degree of stenosis and its dominant frequency, which differs considerably from the break frequency and the heart rate (known dominant frequencies in the literature). Applying our new relation to available stethoscopic data, we find that its predictions are consistent with clinical assessment. Our finding of this linear correlation is also unlike prevalent scaling laws in the literature, which feature a small exponent (i.e., low stenosis percentage sensitivity over much of the clinical range). They hence can only distinguish mild, moderate, and severe cases. Conversely, our linear law can identify variations in the degree of stenosis sensitively and accurately for the full clinical range, thus significantly improving the utility of the relevant scaling laws... Future research will investigate incorporating the vibroacoustic role of adjacent organs to expand the clinical applicability of our findings. Extending our approach to more complex 3D stenotic morphologies and including the vibroacoustic role of surrounding organs will be explored in future research to advance the clinical reach of our findings.

Acoustic emission monitoring for necking in sheet metal forming

There is extensive evidence in the literature that plastic deformation of metals is associated with an increase in Acoustic Emission (AE) activity. Thus, AE measurement techniques have the potential to monitor a forming process in real time and provide a signal for feedback control, to exploit optimum formability. In this work, custom-made AE sensors employing piezoelectric crystals are implemented to measure the emitted acoustic signal during uniaxial tension and cup drawing tests of an AA6013-T4 aluminum sheet (1.5 mm thick). The uniaxial tension tests are conducted with two AE sensors clamped to each end of the specimen gage section, along with full-field surface strain measurement using Digital Image Correlation (DIC) techniques. The AE signals along with the interrogation of the DIC images reveal that the maximum AE amplitude corresponds to the onset of diffuse necking, i.e., when the strain field starts to become spatially inhomogeneous. Interestingly, this onset occurs before the maximum force is attained. Comparing these observations to a model of dislocation activity supports the notion that dislocation is the main driver of AE activity. With these findings, AE measurements are performed in a cup drawing process where a custom-made Marciniak-type punch incorporates three AE sensors. These sensors are used to triangulate and determine the location of necking and eminent fracture based on the time difference of arriving signals to each sensor. The results from the cup drawing tests show that AE signals can identify the onset of necking and accurately predict the location of necking and fracture.

Behavicker: Eavesdropping Computer-Usage Activities through Acoustic Side Channel

Computers are widely used for business and entertainment purposes throughout our modern lives. Computer kits provide a variety of services such as text processing, programming, shopping, and gaming. Computers have greatly enhanced the quality of our lives; however, we discover an often-overlooked fact that engaging in computer-related activities may be eavesdropped upon by an attacker by sniffing the emitted acoustic signals from keyboard and mouse. The activity of eavesdropping via acoustic side channel has lower requirements in terms of hardware instrumentation and is easier to implement in real-world applications than other side channel attacks that have been presented in previous work. In this paper, we design and implement a system, namely, Behavicker, to validate the feasibility of this kind of attack. Unlike conventional activity recognition, Behavicker infers high-level computer-usage activities with a semantics-preserving multiscale learning scheme, based on the recognition of basic keyboard and mouse events including left click, right click, middle click, scrolling up, and scrolling down. Real-world experiments show that Behavicker can recognize six interaction events with an accuracy of 88.3% and infer computer-usage activities with an accuracy of 82.7% in an indoor environment.

A Novel Method for Smart Fire Detection Using Acoustic Measurements and Machine Learning: Proof of Concept

Fires are a major hazard resulting in high monetary costs, personal suffering, and irreplaceable losses. The consequences of a fire can be mitigated by early detection systems which increase the potential for successful intervention. The number of false alarms in current systems can for some applications be very high, but could be reduced by increasing the reliability of the detection system by using complementary signals from multiple sensors. The current study investigates the novel use of machine learning for fire event detection based on acoustic sensor measurements. Many materials exposed to heat give rise to acoustic emissions during heating, pyrolysis and burning phases. Further, sound is generated by the heat flow associated with the flame itself. The acoustic data collected in this study is used to define an acoustic sound event detection task, and the proposed machine learning method is trained to detect the presence of a fire event based on the emitted acoustic signal. The method is able to detect the presence of fire events from the examined material types with an overall F-score of 98.4%. The method has been developed using laboratory scale tests as a proof of concept and needs further development using realistic scenarios in the future.

A laser powder bed fusion system for operando synchrotron x-ray imaging and correlative diagnostic experiments at the Stanford Synchrotron Radiation Lightsource.

Laser powder bed fusion (LPBF) is a highly dynamic multi-physics process used for the additive manufacturing (AM) of metal components. Improving process understanding and validating predictive computational models require high-fidelity diagnostics capable of capturing data in challenging environments. Synchrotron x-ray techniques play a vital role in the validation process as they are the only in situ diagnostic capable of imaging sub-surface melt pool dynamics and microstructure evolution during LPBF-AM. In this article, a laboratory scale system designed to mimic LPBF process conditions while operating at a synchrotron facility is described. The system is implemented with process accurate atmospheric conditions, including an air knife for active vapor plume removal. Significantly, the chamber also incorporates a diagnostic sensor suite that monitors emitted optical, acoustic, and electronic signals during laser processing with coincident x-ray imaging. The addition of the sensor suite enables validation of these industrially compatible single point sensors by detecting pore formation and spatter events and directly correlating the events with changes in the detected signal. Experiments in the Ti-6Al-4V alloy performed at the Stanford Synchrotron Radiation Lightsource using the system are detailed with sufficient sampling rates to probe melt pool dynamics. X-ray imaging captures melt pool dynamics at frame rates of 20 kHz with a 2 µm pixel resolution, and the coincident diagnostic sensor data are recorded at 470 kHz. This work shows that the current system enables the in situ detection of defects during the LPBF process and permits direct correlation of diagnostic signatures at the exact time of defect formation.

Investigation of an Active Measurement Method to Detect Inertial Cavitation and the Presence of Potential Drug Carriers

Abstract Drug carriers, such as nanoparticles, are capable of releasing pharmaceutically active ingredients, which can be initiated by focused ultrasound via the effect of inertial cavitation. This effect of inertial cavitation is often verified by passive measurement methods that rely on the analysis of emitted acoustic signals caused by the implosion of bubbles. However, a major issue of such methods is their inability to detect the presence of potential drugs in human vessels, complicating the implementation of a closed loop control for future medical therapies. Therefore, this contribution introduces an active measurement method to determine both inertial cavitation and the presence of potential drug carriers in a tissue mimicking phantom

Synthesis of group compressed-air sources for marine seismics

The improvement of geological efficiency of marine seismic survey implies continuous improvement of the entire technological complex used in water areas. This is typically accomplished by using complex linear or spatial non-uniform compressed-air groups. In the development of such sources, in addition to solving a number of purely technical problems related to their placement on board the ship and safe operation, one of the most difficult tasks is to determine the initial requirements for selection of the main parameters of the group, such as the volumes of working chambers sources and geometry of their grouping, as well as other factors determining amplitude, time and frequency of emitted acoustic signals. This study considers the main principles of grouping the compressed-air sources for marine seismic survey. It is shown that the most important factors determining the choice of source parameters and specific research technology include the nature of geological tasks and seismic conditions, which primarily depend on the sea depth in the study area. A simple method of calculating the configuration of linear subgroups with fluctuation suppression is described, as well as examples of building linear and spatial groups based on them with pronounced directional properties are considered.

Identification of object on an emitted signal based on matrixes of a bispectrum and indicator characteristics of Fourier spectrum

The article considers the urgent problem of identifying the state of an object in order to improve the quality of management. To identify the state of the object, a new method is proposed for analyzing the characteristics of the spectral power of a signal recorded during monitoring, which is characterized by the application of convergent decision production rules of a classification type. It is proposed to carry out convergence based on the analysis of the bispectrum of the acoustic signal emitted by the object and the correlation matrix of the characteristics of the amplitude spectral function. The first approach allows to investigate autocorrelation functions of a signal. The second approach allows to study structure of the organization of a signal. The following are considered: the mathematical apparatus and algorithms for determining specific correlation matrices and the methodology of the decision rules system. Results of synthesis of convergent decisive rules for diagnostics of statuses of an organism at standard forms of bronchitis are given. It is noted that the convergence of decision rules allows to increase the accuracy of identification of the state of the observed object by an average of 5-6% compared to private decision rules and by 10% compared with the classification by an expert person. A distinctive feature is the analysis of not only spectral characteristics, but also the functional relationship between them, which allows us to identify new properties and take into account the states that characterize the change in the functioning of the internal control system as a reaction to external influence as a whole. The obtained research results can be used in the construction of smart expert systems that are part of facility management systems whose state is characterized by emitted acoustic signals.

On-Line non-intrusive Monitoring of Particulate Solid Materials in Gas Flowlines Using Acoustic Sensor and ML Techniques

This paper describes initial steps towards developing a real-time quantitative particulate solids’ (sand) monitoring system forMultiphase flowlines based on acoustic monitoring and machine learning techniques. The concentration and the velocity of thesolids were varied during experimental trials. A conventional contact microphone mounted externally to a production flowline bend was used for recording the emitted acoustic signal. Features extracted from the signal were used as input to Time DelayNeural Network (TDNN) with solids concentration and velocity label to a training set. The TDNN achieved low values ofnormalised root mean square error (NRMSE) for all the models compared to the classical neural network.

Recent advances in remote sensing technologies for hydrocarbon exploration and environmental evaluation

Remote sensing detects and monitors the physical and spatial characteristics of the earth's oceans, surface, and atmosphere by measuring the reflected or scattered downwelling or emitted upwelling electromagnetic radiation or acoustic signal using passive or active sensors at a distance. It plays an important role in today's energy and environmental sustainability efforts. Remote sensing from spaceborne, airborne, terrestrial, and marine platforms has long been used in hydrocarbon exploration to map surface geology, topography, and hydrocarbon seepages, as well as to evaluate environments that relate to petroleum industry activities. Since the mid-2000s, remote sensing technologies have undergone substantial advances in data acquisition, processing, and interpretation. In the last decade, rapid advances in satellite systems, unmanned autonomous vehicles (UAVs), sensors, and scale of surveys have further expanded applications.

Experimental investigations of monolithic IGBT transistor acoustic emission phenomena

Many recent researches are focused on the diagnostic methods allowing determination of the condition of working semiconductor components (power transistors, diodes etc.). As any solid material under stresses (mechanical, temperature and electrical) the semiconductors are generating elastic waves in the case of changing conductivity state. Based on the results of experimental research, a correlation can be observed between the transition between the on and off states of a single IGBT transistor and the emitted acoustic signal. Acquisition of acoustic emission signals was obtained with use of a wide band acoustic emission sensor. The received signal initially was prepared by AD input card with amplifier and then were recorded by means of a computer software. Acquired data were exported the to a file, which enabled further processing of the measured signals. The aim of the study was to determine the acoustic emission background signal coming from switching IGBT transistor and preparation of frequency response. The results of analysis will be used as a reference point for a further investigation of early-stage faults of semiconductors elements.

Bubble dynamics in boiling histotripsy

Boiling histotripsy is a non-invasive, cavitation-based ultrasonic technique which uses a number of millisecond pulses to mechanically fractionate tissue. Though a number of studies have demonstrated the efficacy of boiling histotripsy for fractionation of solid tumours, treatment monitoring by cavitation measurement is not well studied because of the limited understanding of the dynamics of bubbles induced by boiling histotripsy. The main objectives of this work are to (a) extract qualitative and quantitative features of bubbles excited by shockwaves and (b) distinguish between the different types of cavitation activity for either a thermally or a mechanically induced lesion in the liver. A numerical bubble model based on the Gilmore equation accounting for heat and mass transfer (gas and water vapour) was developed to investigate the dynamics of a single bubble in tissue exposed to different High Intensity Focused Ultrasound fields as a function of temperature variation in the fluid. Furthermore, ex vivo liver experiments were performed with a passive cavitation detection system to obtain acoustic emissions. The numerical simulations showed that the asymmetry in a shockwave and water vapour transport are the key parameters which lead the bubble to undergo rectified growth at a boiling temperature of 100°C. The onset of rectified radial bubble motion manifested itself as (a) an increase in the radiated pressure and (b) the sudden appearance of higher order multiple harmonics in the corresponding spectrogram. Examining the frequency spectra produced by the thermal ablation and the boiling histotripsy exposures, it was observed that higher order multiple harmonics as well as higher levels of broadband emissions occurred during the boiling histotripsy insonation. These unique features in the emitted acoustic signals were consistent with the experimental measurements. These features can, therefore, be used to monitor (a) the different types of acoustic cavitation activity for either a thermal ablation or a mechanical fractionation process and (b) the onset of the formation of a boiling bubble at the focus in the course of HIFU exposure.

Overview of one year acoustical oceanography measurements on Chukchi shelf during Shallow Water Canada Basin Acoustic Propagation Experiment 2016–2017

A multi-institutional, acoustical oceanography experiment was conducted from October 2016 through November 2017 on the Chukchi continental shelf covering 100–700 m isobaths. Parallel to a deep-water experiment conducted during the same period, the Shallow Water Canada Basin Acoustic Propagation Experiment (SW CANAPE) was designed to assess basin scale acoustic signals on the shelf region while detailed oceanographic dynamic of the shelf break region, particularly the upwelling and other dynamic of the upper 500 m water column, was measured simultaneously. Multiple arrays of oceanographic sensors including upward looking ice profiler, current profiler, temperature, conductivity, and pressure profiles measured temporal and spatial dynamics of 500 m upper ocean in connection with acoustic measurements. Distributed in a 30 km2 area north of Barrow Alaska, vertical line arrays including an L-shaped array, covered upper 200 m of the water column. Two acoustic sources placed at 148 m and 193 m depths on the shelf emitted broadband acoustic signals in frequency bands (700–1100 Hz, and 1400–4000 Hz) along and across the shelf while the sound speed and current profile and surface ice were being measured continuously. Deep water low frequency signals were also recorded. This talk provides an overview of the SW CANAPE experiment and highlights some of the detailed measurements. [Work supported by ONR.]A multi-institutional, acoustical oceanography experiment was conducted from October 2016 through November 2017 on the Chukchi continental shelf covering 100–700 m isobaths. Parallel to a deep-water experiment conducted during the same period, the Shallow Water Canada Basin Acoustic Propagation Experiment (SW CANAPE) was designed to assess basin scale acoustic signals on the shelf region while detailed oceanographic dynamic of the shelf break region, particularly the upwelling and other dynamic of the upper 500 m water column, was measured simultaneously. Multiple arrays of oceanographic sensors including upward looking ice profiler, current profiler, temperature, conductivity, and pressure profiles measured temporal and spatial dynamics of 500 m upper ocean in connection with acoustic measurements. Distributed in a 30 km2 area north of Barrow Alaska, vertical line arrays including an L-shaped array, covered upper 200 m of the water column. Two acoustic sources placed at 148 m and 193 m depths on the shel...

Simulation of induced acoustic emission in fractured porous media

Acoustic/microseismic Emissions (AE) in naturally fractured porous media are the result of local instability along internal interfaces and the sudden release of strain energy stored in the rock matrix. This rapid release of energy, stimulates high-frequency components of the dynamic response of the rock mass, inducing mechanical wave propagation. In this article an enriched finite element model is employed to concurrently simulate the interface instability and the induced wave propagation processes in a fractured porous media. Harmonic enrichment functions are used in the context of the Generalized Finite Element Method (GFEM) to suppress the spurious oscillations that can appear in wave propagation/dynamic modelings using regular finite elements. To model the fractures, the Phantom Node Method (PNM) is employed with the GFEM. The frictional contact condition at material interfaces is modeled using a stable augmented Lagrange multiplier approach. Through various parametric studies it’s shown that (i) decreasing the permeability leads to an increase in the frequency and a decrease in the amplitude of the acoustic signal; (ii) increasing viscous damping leads to narrower frequency spectrum and decreased magnitude of the emitted acoustic signal; (iii) increasing damping leads to a transition from transient wave propagation to diffusion-dominated AE response; (iv) increasing interface friction leads to more pronounced stick-slip behavior and higher amplitude AE-without interface friction there is no AE. Lastly, the numerical illustrations demonstrate the superior capability of the enriched model (in comparison with regular finite element models) in suppressing the spurious oscillations in AE solutions.

Investigation of acoustic emissions and pressure stimulated currents detected during bending of restored marble epistyles within the frame of log-periodic power-law models

The deformation of solid materials is a procedure which is accompanied by numerous effects like the emission of acoustic and electromagnetic signals or the generation of pressure stimulated currents. In the present work, we focus on the study of acoustic emissions and pressure stimulated currents detected during a recent bending experiment on an accurate copy of an authentic marble epistyle of the Parthenon Temple. The aim is to investigate the emitted acoustic signals and pressure stimulated currents within the frame of log-periodic power-law models. While acoustic and electromagnetic emissions detected during deformation experiments of well-defined and specially crafted specimens have already been successfully described by such models, it is interesting to investigate whether these models could also describe pressure stimulated currents as well as acoustic emission signals originating from complex structural setups.

Identification of jet noise source using causality method based on large-eddy simulation of a round jet flow

An acoustic analogy analysis based on a decomposition of the source term in Lighthill’s equation is discussed in light of a large-eddy simulation of a subsonic turbulent jet exhausting from a baseline round nozzle at Mach number M = 0.9 with Reynolds number Re = 105. The decomposed sub-terms show the nonlinear reciprocal interactions of density, velocity, vorticity, and dilatation fields. To understand the aerodynamic sound generation mechanism, intrinsic links between turbulent flow and emitted acoustic signals are made and applied to the large-eddy simulation data. Cross-correlation functions are used for the links between the far-field sound signals and the sub-terms as well as major flow variables in the jet flow domain. The spatial distributions of cross-correlations are examined to identify the sound source distribution throughout the domain and observe the mutual interactions and cancellations between the decomposed sub-terms. The contributions of sub-terms are also studied in frequency domain.

Acoustic signal characteristics of laser induced cavitation in DDFP droplet: Spectrum and time-frequency analysis

Cavitation has great application potential in microvessel damage and targeted drug delivery. Concerning cavitation, droplet vaporization has been widely investigated in vitro and in vivo with plasmonic nanoparticles. Droplets with a liquid dodecafluoropentane (DDFP) core enclosed in an albumin shell have a stable and simple structure with good characteristics of laser absorbing; thus, DDFP droplets could be an effective aim for laser-induced cavitation. The DDPF droplet was prepared and perfused in a mimic microvessel in the optical microscopic system with a passive acoustic detection module. Three patterns of laser-induced cavitation in the droplets were observed. The emitted acoustic signals showed specific spectrum components at specific time points. It was suggested that a nanosecond laser pulse could induce cavitation in DDPF droplets, and specific acoustic signals would be emitted. Analyzing its characteristics could aid in monitoring the laser-induced cavitation process in droplets, which is meaningful to theranostic application.

Multiscale modeling of sensory properties of Co–Ni–Al shape memory particles embedded in an Al metal matrix

The concept of utilizing ferromagnetic shape memory alloys as embedded sensory particles in aluminum alloys for damage detection is discussed. When embedded in a material, a shape memory particle can undergo an acoustically detectable solid-state phase transformation when the local strain reaches a critical value. The emitted acoustic signal can be used for real-time damage detection. To study the transition behavior of the sensory particle inside a metal matrix under load, a simulation approach based on a coupled atomistic-continuum model is used. The simulation results indicate a strong dependence of the particle’s pseudoelastic response on its crystallographic orientation with respect to the loading direction. These results serve as a basis for understanding the efficacy and variability in the sensory particle transformation to detect damage processes.