Mechanical properties and acoustic emission characteristics of soft rock with different water contents under dynamic disturbance

Uniaxial compression tests and cyclic loading acoustic emission tests were conducted on 20%,40%,60%,80%,dry and saturated muddy sandstone by using a creep impact loading system to investigate the mechanical properties and acoustic emission characteristics of soft rocks with different water contents under dynamic disturbance.The mechanical properties and acoustic emission characteristics of muddy sandstones at different water contents were analysed.Results of experimental studies show that water is a key factor in the mechanical properties of rocks,softening them,increasing their porosity,reducing their brittleness and increasing their plasticity.Under uniaxial compression,the macroscopic damage characteristics of the muddy sandstone change from mono-bevel shear damage and‘X’type conjugate bevel shear damage to a roadway bottom-drum type damage as the water content increases.Dynamic perturbation has a strengthening effect on the mechanical properties of samples with 60%and less water content,and a weakening effect on samples with 80%and more water content,but the weakening effect is not obvious.Macroscopic damage characteristics of dry samples remain unchanged,water samples from shear damage and tensile–shear composite damage gradually transformed into cleavage damage,until saturation transformation monoclinic shear damage.The evolution of acoustic emission energy and event number is mainly divided into four stages:loading stage(Ⅰ),dynamic loading stage(Ⅱ),yield failure stage(Ⅲ),and post-peak stage(Ⅳ),the acoustic emission characteristics of the stages were different for different water contents.The characteristic value of acoustic emission key point frequency gradually decreases,and the damage degree of the specimen increases,corresponding to low water content—high main frequency—low damage and high water content—low main frequency—high damage.

Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion:A study from Poseidon,NW shelf Australia

Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.

Acoustic radiation force on a cylindrical composite particle with an elastic thin shell and an internal eccentric liquid column in a plane ultrasonic wave field

A model with three-layer structure is introduced to explore the acoustic radiation force(ARF)on composite particles with an elastic thin shell.Combing acoustic scattering of cylinder and the thin-shell theorem,the ARF expression was derived,and the longitudinal and transverse components of the force and axial torque for an eccentric liquid-filled composite particle was obtained.It was found that many factors,such as medium properties,acoustic parameters,eccentricity,and radius ratio of the inner liquid column,affect the acoustic scattering field of the particle,which in turn changes the forces and torque.The acoustic response varies with the particle structures,so the resonance peaks of the force function and torque shift with the eccentricity and radii ratio of particle.The acoustic response of the particle is enhanced and exhibits higher force values due to the presence of the elastic thin shell and the coupling effect with the eccentricity of the internal liquid column.The decrease of the inner liquid density may suppress the high-order resonance peaks,and internal fluid column has less effects on the change in force on composite particle at ka>3,while limited differences exist at ka<3.The axial torque on particles due to geometric asymmetry is closely related to ka and the eccentricity.The distribution of positive and negative force and torque along the axis ka exhibits that composite particle can be manipulated or separated by ultrasound.Our theoretical analysis can provide support for the acoustic manipulation,sorting,and targeting of inhomogeneous particles.

The viscous strip approach to simplify the calculation of the surface acoustic wave generated streaming

In recent decades,the importance of surface acoustic waves,as a biocompatible tool to integrate with microfluidics,has been proven in various medical and biological applications.The numerical modeling of acoustic streaming caused by surface acoustic waves in microchannels requires the effect of viscosity to be considered in the equations which complicates the solution.In this paper,it is shown that the major contribution of viscosity and the horizontal component of actuation is concentrated in a narrow region alongside the actuation boundary.Since the inviscid equations are considerably easier to solve,a division into the viscous and inviscid domains would alleviate the computational load significantly.The particles'traces calculated by this approximation are excellently alongside their counterparts from the completely viscous model.It is also shown that the optimum thickness for the viscous strip is about 9-fold the acoustic boundary layer thickness for various flow patterns and amplitudes of actuation.

Scheme of negative acoustic radiation force based on a multiple-layered spherical structure

Acoustic radiation force(ARF), as an important particle manipulation method, has been extensively studied in recent years. With the introduction of the concept of “acoustic tweezers”, negative acoustic radiation has become a research hotspot. In this paper, a scheme of realizing negative ARF based on the multiple-layered spherical structure design is proposed. The specific structure and design idea are presented. Detailed theoretical calculation analysis is carried out.Numerical simulations have been performed to verify the correctness of this prediction. The conjecture that the suppression of backscattering can achieve negative ARF is verified concretely, which greatly expands the application prospect and design ideas of the ARF. This work has laid a theoretical foundation for realizing precise control of the structure.

Development and prospect of acoustic reflection imaging logging processing and interpretation method

Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies,such as fractures,caves and faults,within a range of tens of meters from the wellbore,greatly expanding the application scope of well logging technology.This article reviews the development history of the technology and focuses on introducing key methods,software,and on-site applications of acoustic reflection imaging logging technology.Based on the analyses of major challenges faced by existing technologies,and in conjunction with the practical production requirements of oilfields,the further development directions of acoustic reflection imaging logging are proposed.Following the current approach that utilizes the reflection coefficients,derived from the computation of acoustic slowness and density,to perform seismic inversion constrained by well logging,the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves.It is essential to advance research in imaging of fractures within shale reservoirs,the assessment of hydraulic fracturing effectiveness,the study of geosteering while drilling,and the innovation in instruments of acoustic reflection imaging logging technology.

Distribution and influencing factors of acoustic characteristics of seafloor sediment in the Sunda Shelf

To understand the acoustic and physical properties of piston core samples collected from the Sunda continental shelf and analyze their distribution patterns,the samples were analyzed in laboratory,from which three provinces were divided in sound speed,sound speed ratio,porosity,wet bulk density,and maximum shear strength.ProvinceⅠhad lower sound speed and sound speed ratio(<1.04),high porosity,and low wet bulk density.ProvinceⅡhad higher sound speed and sound speed ratio(>1.04),low porosity,and high wet bulk density.ProvinceⅢhad the lowest sound speed and sound speed ratio(0.99),highest porosity(81%),and lowest wet bulk density(1.34 g/cm^(3)).The distribution pattern indicates that sediment movement,sediment source,topography,and hydrodynamic conditions influenced the distribution of acoustic and physical properties.Furthermore,we investigated the relationship of the maximum shear strength to the porosity and wet bulk density,and found that the maximum shear strength was proportional to both the porosity and wet bulk density.This finding has significant implications for ocean engineering applications.

A Self-Healing Optoacoustic Patch with High Damage Threshold and Conversion Efficiency for Biomedical Applications

Compared with traditional piezoelectric ultrasonic devices,optoacoustic devices have unique advantages such as a simple preparation process,anti-electromagnetic interference,and wireless long-distance power supply.However,current optoacoustic devices remain limited due to a low damage threshold and energy conversion efficiency,which seriously hinder their widespread applications.In this study,using a self-healing polydimethylsiloxane(PDMS,Fe-Hpdca-PDMS)and carbon nanotube composite,a flexible optoacoustic patch is developed,which possesses the self-healing capability at room temperature,and can even recover from damage induced by cutting or laser irradiation.Moreover,this patch can generate high-intensity ultrasound(>25 MPa)without the focusing structure.The laser damage threshold is greater than 183.44 mJ cm^(-2),and the optoacoustic energy conversion efficiency reaches a major achievement at 10.66×10^(-3),compared with other carbon-based nanomaterials and PDMS composites.This patch is also been successfully examined in the application of acoustic flow,thrombolysis,and wireless energy harvesting.All findings in this study provides new insight into designing and fabricating of novel ultrasound devices for biomedical applications.

Predicting microseismic,acoustic emission and electromagnetic radiation data using neural networks

Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

Orbital angular momentum conversion of acoustic vortex beams via planar lattice coupling

Orbital angular momentum(OAM)conversion is critical in understanding interactions between a structural sound field and a planar lattice.Herein,we explore the evolution of a monochromatic acoustic vortex beam(AVB)that is scattered by a phononic crystal(PnC)or a correlated random lattice.The phenomenon is ascribed to the enhanced orbit–orbit angular momentum coupling induced by the band structure.By modifying the coupling condition,accurate and continuous micromanipulation of AVBs can be achieved,including the transverse/lateral gravity shift,the dynamics of the phase singularities,and the spatial distribution of acoustic pressure,etc.This research provides insight to the inhomogeneous coupling of AVBs with both propagating Bloch waves and localized Anderson modes,and may facilitate development of novel OAM-based acoustic devices for active sound field manipulation.

Experimental and numerical investigations on acoustic damping of monoclinic crystalline wideband sound absorbing structures

In order to overcome the limitations of traditional microperforated plate with narrow sound absorption bandwidth and a single structure,two multi-cavity composite sound-absorbing materials were designed based on the shape of monoclinic crystals:uniaxial oblique structure(UOS)and biaxial oblique structure(BOS).Through finite element simulation and experimental research,the theoretical models of UOS and BOS were verified,and their sound absorption mechanisms were revealed.At the same time,the influence of multi-cavity composites on sound absorption performance was analyzed based on the theoretical model,and the influence of structural parameters on sound absorption performance was discussed.The research results show that,in the range of 100-2000 Hz,UOS has three sound absorption peaks and BOS has five sound absorption peaks.The frequency range of the half-absorption bandwidth(α>0.5)of UOS and BOS increases by 242% and 229%,respectively.Compared with traditional microperforated sound-absorbing structures,the series and parallel hybrid methods significantly increase the sound-absorbing bandwidth of the sound-absorbing structure.This research has guiding significance for noise control and has broad application prospects in the fields of transportation,construction,and mechanical design.

Bandgap adjustment of a sandwich-like acoustic metamaterial plate with a frequency-displacement feedback control method

Several types of acoustic metamaterials composed of resonant units have been developed to achieve low-frequency bandgaps.In most of these structures,bandgaps are determined by their geometric configurations and material properties.This paper presents a frequency-displacement feedback control method for vibration suppression in a sandwich-like acoustic metamaterial plate.The band structure is theoretically derived using the Hamilton principle and validated by comparing the theoretical calculation results with the finite element simulation results.In this method,the feedback voltage is related to the displacement of a resonator and the excitation frequency.By applying a feedback voltage on the piezoelectric fiber-reinforced composite(PFRC)layers attached to a cantilever-mass resonator,the natural frequency of the resonator can be adjusted.It ensures that the bandgap moves in a frequency-dependent manner to keep the excitation frequency within the bandgap.Based on this frequency-displacement feedback control strategy,the bandgap of the metamaterial plate can be effectively adjusted,and the vibration of the metamaterial plate can be significantly suppressed.

Ion acoustic solitary waves in an adiabatic dusty plasma:Roles of superthermal electrons,ion loss and ionization

We investigate propagation of dust ion acoustic solitary wave(DIASW)in a multicomponent dusty plasma with adiabatic ions,superthermal electrons,and stationary dust.The reductive perturbation method is employed to derive the damped Korteweg-de Vries(DKdV)equation which describes DIASW.The result reveals that the adiabaticity of ions significantly modifies the basic features of the DIASW.The ionization effect makes the solitary wave grow,while collisions reduce the growth rate and even lead to the damping.With the increases in ionization cross sectionΔσ/σ_(0),ion-to-electron density ratioδ_(ie)and superthermal electrons parameterκ,the effect of ionization on DIASW enhances.

2D DOA Estimation of Coherent Signals with a Separated Linear Acoustic Vector-Sensor Array

In this paper, a two-dimensional(2D) DOA estimation algorithm of coherent signals with a separated linear acoustic vector-sensor(AVS) array consisting of two sparse AVS arrays is proposed. Firstly,the partitioned spatial smoothing(PSS) technique is used to construct a block covariance matrix, so as to decorrelate the coherency of signals. Then a signal subspace can be obtained by singular value decomposition(SVD) of the covariance matrix. Using the signal subspace, two extended signal subspaces are constructed to compensate aperture loss caused by PSS.The elevation angles can be estimated by estimation of signal parameter via rotational invariance techniques(ESPRIT) algorithm. At last, the estimated elevation angles can be used to estimate automatically paired azimuth angles. Compared with some other ESPRIT algorithms, the proposed algorithm shows higher estimation accuracy, which can be proved through the simulation results.

Vibration Reduction by a Partitioned Dynamic Vibration Absorber with Acoustic Black Hole Features

Vibration quality is a vital indicator for assessing the progress of modern equipment.The dynamic vibration absorber(DVA)based on the acoustic black hole(ABH)feature is a new passive control method that manipulates waves.It offers efficient energy focalization and broad-spectrum vibration suppression,making it highly promising for applications in large equipment such as aircraft,trains,and ships.Despite previous advancements in ABH-DVA development,certain challenges remain,particularly in ensuring effective coupling with host structures during control.To address these issues,this study proposes a partitioned ABH-featured dynamic vibration absorber(PABH-DVA)with partitions in the radial direction of the disc.By employing a plate as the host structure,simulations and experiments were conducted,demonstrating that the PABH-DVA outperforms the original symmetric ABH-DVA in terms of damping performance.The study also calculated and compared the coupling coefficients of the two ABH-DVAs to uncover the mechanism behind the enhanced damping.Simulation results revealed that the PABH-DVA exhibits more coupled modes,occasionally with lower coupling coefficients than the symmetric ABH-DVA.The influence of frequency ratio and modal mass was further analyzed to explain the reasons behind the PABH-DVA's superior damping performance.Additionally,the study discussed the impact of the number of slits and their orientation.This research further explains the coupling mechanism between the ABH-DVA and the controlled structure,and provides new ideas for the further application of ABH in engineering.

Microstructure Features and the Macroscopic Acoustic Behavior of Gassy Silt in the Yellow River Delta

The morphological changes in isolated bubbles in gassy silt play a critical role in the microscopic structures between soil particles and bubbles and macroscopic physical properties.Based on X-ray CT scanning experiments under various vertical loads(four levels),self-designed acoustic macro experiments,and a series of formula revisions to the macro-air-bearing silt sound-velocity prediction model,this paper discusses the macro-and micro-scale features of gassy silts from the Yellow River Delta.The samples consisted of different proportions of silt from the Yellow River Delta and porous media,and they were used to form two types of aerosol silts with initial gas contents of 4.23%and 7.67%.The results show that the air bubble content and external load considerably affect the microstructural parameters and acoustic behavior of gassy silt in the Yellow River Delta.The macroscopic sound velocity showed a linear positive correlation with vertical load and relation to microstructural parameters in varying manners and degrees.Based on the traditional Biot-Stoll acoustic model,the gas-phase medium coefficient was introduced for the proper calculation and prediction of the sound velocity of air-bearing silt.The errors of the overall prediction varied between 5.6%and 9.6%.

Identifying the real fracture hidden in rock microcrack zone by acoustic emission energy

Identifying the real fracture of rock hidden in acoustic emission(AE)source clusters(AE-depicted microcrack zone)remains challenging and crucial.Here we revealed the AE energy(representing dissipated energy)distribution rule in the rock microcrack zone and proposed an AE-energy-based method for identifying the real fracture.(1)A set of fracture experiments were performed on granite using wedgeloading,and the fracture process was detected and recorded by AE.The microcrack zone associated with the energy dissipation was characterized by AE sources and energy distribution,utilizing our selfdeveloped AE analysis program(RockAE).(2)The accumulated AE energy,an index representing energy dissipation,across the AE-depicted microcrack zone followed the normal distribution model(the mean and variance relate to the real fracture path and the microcrack zone width).This result implies that the nucleation and coalescence of massive cracks(i.e.,real fracture generation process)are supposed to follow a normal distribution.(3)Then,we obtained the real fracture extension path by joining the peak positions of the AE energy normal distribution curve at different cross-sections of the microcrack zone.Consequently,we distinguished between the microcrack zone and the concealed real fracture within it.The deviation was validated as slight as 1–3 mm.

Simplified prediction models for acoustic installation effects of train-mounted equipment

Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test bench is combined with meas-ured or predicted transfer functions.It is important,however,to allow for installation effects due to shielding by fairings or the train body.In the current work,fast-running analytical models are developed to determine these installation effects.The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing,using a barrier model.For equipment mounted under the train,the acoustic propagation from the sides of the source is based on free-field Green’s functions.The bottom surfaces are assumed to radiate initially into a cavity under the train,which is modelled with a simple diffuse field approach.The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions.Results show good agreement with a 2.5D boundary element model and with measurements.Modelling uncertainty and parametric uncertainty are evaluated.The largest variability occurs due to the height and impedance of the ground,especially for a low receiver.This leads to standard deviations of up to 4 dB at low frequencies.For the roof-mounted sources,uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.