Multimodal ultrasonic guided wave (UGW) signal reconstruction technology can accurately separate individual modes, providing more comprehensive and precise information for material nondestructive testing. However, the accuracy of existing reconstruction techniques heavily depends on the precision and completeness of time-frequency (TF) ridge extraction. To address this challenge, this paper proposes a TF energy segmentation reconstruction method without relying on complete TF ridge extraction, as traditionally required. This approach introduces an adaptive noise variance estimation Bayesian filter to extract the TF ridges under unknown noise distribution, particularly in regions where TF ridges intersect or overlap. By using the extracted TF ridges as references, the energy segmentation method directly separates and reconstructs UGW modes from the TF representation even when the extracted TF ridges are incomplete. This is because the proposed method can automatically retrieve the energy of each mode with a region growing algorithm from the time domain and frequency domain so that both modes with rapidly changing instantaneous frequency or group delay can be recovered, while the traditional method can only separate modes from a single domain. Numerical simulations and photoacoustic-guided wave experiments validate the effectiveness of the proposed method, achieving reconstruction accuracies of 96.9% and 92.5% for the simulated and experimental signals, respectively.

Advancement of computation nondestructive evaluation (CNDE) creates an opportunity to visualize predicted signals received by sensors and may aid the development of artificial intelligence (AI) for NDE 4.0. However, traditional methods face limitations for crack propagation and guided wave propagation simulation, simultaneously. Modeling crack propagation using mesh-based method requires remeshing and implementation of cohesive zone model to name a few alternatives. Multiple meshfree methods have also been implemented for crack propagation but did not immediately translate to simulate the guided waves that are used to interrogate the cracks under nondestructive evaluation (NDE) framework. Ultrasonic CNDE with new era of Machine Learning (ML)/AI requires understanding the signals and its physics-based features when the guided waves propagate to interact with the crack while the crack is simultaneously growing at different time scales. To enable the future of physics to be informed and physics driven ML/AI this article presents a framework of CNDE where guided wave propagation and crack propagation are simultaneously simulated without remeshing and creates an enabling approach for the future AI implementation. A few successful case studies are presented for feasibility demonstration. Detailed flowcharts are presented for easy implementation of the method for the ultrasonic NDE community.

Pipe wall loss assessment is crucial in oil and gas transportation. Ultrasonic guided wave is an effective technology to detect pipe defects. However, accurately inverting weak-feature defects under limited view conditions remains challenging due to constraints in transducer arrangements and inconsistent signal characteristics. This paper proposes a stepwise inversion method based on feature compensation and network reconstruction through deep learning, combined with high-order helical guided waves to expand the imaging view and achieve high-resolution imaging of pipe defects. A forward model was established using the finite difference method, with the two-dimensional Pearson correlation coefficient and maximum wall loss estimation accuracy defined as imaging metrics to evaluate and compare the method. Among 50 randomly selected defect samples in the test set, the inversion model achieved a correlation coefficient of 0.9669 and a maximum wall loss estimation accuracy of 96.65 %. Additionally, Gaussian noise was introduced to assess imaging robustness under pure signal, 5 dB, and 3 dB conditions. Laboratory experiments validated the practical feasibility of the proposed method. This approach is generalizable and holds significant potential for nondestructive testing in cylindrical waveguide structures represented by pipes.

Acoustic tweezers enable non-contact, non-invasive manipulation, with promising applications in fields such as biology, micromechanics, and advanced materials. The circular array, commonly used to generate acoustic vortices-an important type of acoustic tweezer-consists of multiple independently addressable elements arranged in a circular configuration. By adjusting the element excitations, the circular array can flexibly control the location of particles. In this study, we employed numerical and experimental methods to analyse the relationship between device geometrical parameters and acoustic field distribution, as well as their impact on particle manipulation. Results from the three-dimensional model indicate that water surface height, array radius, and the material and thickness of the bottom observation layer, significantly influence the acoustic field distribution and, hence trapping performance. Additionally, we used trap stiffness theory to evaluate particle movement capability, and experimentally identified conditions under which trapping may fail, providing theoretical support for improving acoustic tweezer technology.

This study provides a thorough evaluation of self-compacting concrete (SCC) properties in both its fresh and hardened states using advanced ultrasonic testing techniques. While existing research has offered valuable insights into SCC characteristics, a systematic analysis correlating ultrasonic parameters with both rheological and mechanical properties has been lacking. This research addresses this gap through extensive testing and analysis of critical parameters, including air content, water absorption, plastic viscosity, yield stress, density, compressive strength, and Young's modulus. Our findings reveal that ultrasonic velocity exhibits strong positive correlations with plastic viscosity (R = 0.95), yield stress (R = 0.97), and fresh density (R = 0.99). Increased plastic viscosity and yield stress are associated with higher ultrasonic velocity, indicating enhanced internal consistency and material integrity. Similarly, fresh density positively correlates with ultrasonic velocity, reflecting improved compactness and uniformity. These relationships underscore the significance of these parameters in influencing SCC's ultrasonic characteristics. In the hardened state, ultrasonic velocity shows a robust positive correlation with compressive strength (R = 0.98 to 0.99) and Young's modulus (R = 0.96 to 0.98), suggesting that a denser and stiffer concrete matrix facilitates better ultrasonic wave transmission, indicative of superior structural integrity. These insights are instrumental for optimizing SCC mixture designs, enhancing both performance and durability in construction applications.

In this work, an implementation of the ultrasonic shear wave reflectometry technique for the viscoelastic characterization of liquids is reported. An alternative approach for shear wave generation was implemented. It employees a mode conversion inside a prism, avoiding the need for fluid delay lines. This approach provided shear waves without spurious echoes and good SNR in a wide frequency range. A measurement cell was implemented and its operation tested with glycerin and hydraulic oil samples. The cell allowed the determination of the complex shear modulus and viscosity in the frequency range of 0.36-6.1 MHz. The viscosity values calculated by two different methodologies led to deviations less than 16% in the case of glycerin and 40% in the case of Hydra-68, when compared to the values obtained with conventional viscometry. The experimental results showed that the viscoelastic behavior of the samples fitted well with the Kelvin-Voigt model.

In this study, a novel procedure for identification of ultrasonic wave attenuation in heterogeneous materials based on signal energy was presented. The main objective was to develop a method for simple and robust determination of wave characteristics for further use in numerical modelling of ultrasonic wave propagation including attenuation of signals. Experimental investigations supported by numerical simulations were proposed as an approach to determine the mass proportionality coefficient in the Rayleigh proportional damping model. A number of concrete samples with different sensor configurations were investigated to prove the efficiency of the developed algorithm. The limitations of the established approach were characterized, specifically the maximum frequency that can be considered should be determined in advance. The ability of the proposed method to detect fracture in concrete samples under three-point bending was initially verified for further development of the attenuation-based diagnostic technique.

Ultrasound-induced cavitation can be used in various biomedical therapies, including localized drug delivery, sonoporation, gene transfer, noninvasive sonothrombolysis, lithotripsy, and histotripsy. It can also enhance thermal ablation of tumors and facilitate trans-blood-brain-barrier treatments. Accurate monitoring of cavitation activity, including dose and location, is essential for the safe and effective application of these therapies. Passive cavitation mapping (PCM) is a key technique used to achieve this. However, conventional Delay and Sum (DAS) beamforming methods suffer from low resolution and high side-lobe levels in standard diagnostic ultrasound transducer, limiting their effectiveness or are computationally expensive, in the case of robust capon beamformer (RCB). To address these challenges, we propose a higher-order nonlinear Delay Multiply and Sum (DMAS) beamformer for improved passive cavitation mapping. Our approach utilizes a novel implementation with linear complexity, using a determinant from symmetrical polynomials. Simulation and experimental results demonstrate that the proposed method enhances both axial and lateral point spread function, resolution and increasing image quality, while exhibiting linear complexity. These improvements suggest that higher-order nonlinear beamforming is a promising advancement for more accurate and reliable cavitation monitoring in biomedical applications.

Pulse-echo ultrasonic techniques play a crucial role in assessing wall thickness deterioration in safety-critical industries. Current approaches face limitations with low signal-to-noise ratios, weak echoes, or vague echo patterns typical of heavily corroded profiles. This study proposes a novel combination of Convolution Neural Networks (CNN) and Transformer Neural Networks (TNN) to improve thickness gauging accuracy for complex geometries and echo patterns. Recognizing the strength of TNN in language processing and speech recognition, the proposed network comprises three modules: 1. pre-processing CNN, 2. a Transformer model and 3. a post-processing CNN. Two datasets, one being simulation-generated, and the other, experimentally gathered from a corroded carbon steel staircase specimen, support the training and testing processes. Results indicate that the proposed model outperforms other AI architectures and traditional methods, providing a 5.45% improvement over CNN architectures from NDE literature, a 1.81% improvement over ResNet-50, and a 17.5% improvement compared to conventional thresholding techniques in accurately detecting depths with a precision under 0.5λ.

Defect localization in homogeneous structures using ultrasonic waves is relatively easy to implement. However, locating defects in heterogeneous structures made of different materials can be challenging. This is because complicated reflections, refractions and scatterings occur when ultrasonic waves pass through the interfaces between two dissimilar materials of the heterogeneous structures. To address this issue, a localization methodology based on geometric phase change - index (GPC-I), derived from topological acoustic (TA) sensing, is proposed to adapt to the complicated scenarios when defects are present in heterogeneous plate structures. The GPC-I is adopted as the damage index (DI) to present the possibility of defects appearing on different acoustic sensing paths. A maximum peak value-dependent threshold in GPC-I plots (GPC-I vs. sensor sites) is defined to filter out unreliable sensing paths resulting from the heterogeneity. Different sensing modes (I and II) are combined to comprehensively provide a more reliable and accurate localization framework. Numerical modeling carried out by Abaqus/CAE software verifies the proposed GPC-I based localization technique. Comparison results among GPC-I and other two commonly used acoustic parameters-wave velocity differences (VD) and amplitude ratio (AR) (or wave attenuation) show that the GPC-I has superiority with higher sensitivity and stability for defect localization. This work can provide promising guidance for localizing defects in complex heterogeneous plate structures used in real-world engineering applications.

Fullerenes have exhibited excellent performance in solar cells, electric transducer and catalysts. The rather high absorption coefficient, combined with its low specific heat capacity, as well as hydrophobicity and antioxidant, are key features for applications in acoustic emission (AE), which has never been reported. Here, we fabricate and characterize a flexible an AE source based on the fullerenes-polydimethylsiloxane (PDMS) composite. By controlling the composite concentration or thickness, the center frequency can be changed in laser ultrasound excitation. The assembled transducer simultaneously achieves relatively wide frequency range (10-dB bandwidth>10 MHz) and efficient laser ultrasound conversion (1.13×10). The mechanical robustness of the AE source is also quantitatively characterized in water. Notably, compared to graphene nano-flakes, the fullerenes exhibit a more than threefold increase in excitation amplitude. Owing to high-intensity ultrasound excitation of the fullerenes-PDMS composite, the structure characteristics of centimeter-scaled physical models are clearly resolved by irradiating the material as a laser-ultrasound source. To construct a compact fiber-optic exciter, the fullerenes-PDMS film is additionally applied to a fiber end via dip coating. The findings suggest that fullerenes possess significant competitive advantages as a high-efficiency AE source in the field of ultrasound applications.

This study aims to explore the feasibility of a deep learning approach to correct the distortion caused by the skull, thereby developing a transcranial adaptive focusing method for safe ultrasonic treatment in opening of the blood-brain barrier (BBB). However, aberration correction often requires significant computing power and time to ensure the accuracy of phase correction. This is due to the need to solve the evolution procedure of the sound field represented by numerous discretized grids. A combined method is proposed to train the phase prediction model for correcting the phase accurately and quickly. The method comprises pre-segmentation, k-Wave simulation, and a 3D U-net-based network. We use the k-Wave toolbox to construct a nonlinear simulation environment consisting of a 256-element phased array, a small piece of skull, and water. The skull sound speed sample combining with the phase delay serves as input for the model training. The focus volume and grating lobe level obtained by the proposed approach were the closest to those obtained by the time reversal method in all relevant approaches. Furthermore, the mean peak value obtained by the proposed approach was no less than 77% of that of the time reversal method. In this study, the computational cost of each sample's phase delay was no more than 0.05 s, which was 1/200th of the time reversal method. The proposed method eliminates the complexity of numerical calculation processes requiring consideration of more acoustic parameters, while circumventing the substantial computational resource demands and time-consuming challenges to traditional numerical approaches. The proposed method enables rapid, precise, and adaptive transcranial aberration correction on the 3D skull-based conditions, overcoming the potential inaccuracies in predicting the focal position or the acoustic energy distribution from 2D simulations. These results show the possibility of the proposed approach enabling near-real-time correction of skull-induced phase aberrations to achieve transcranial focus, thereby offering a novel option for treating brain diseases through temporary BBB opening.

High-pressure, broadband, and miniatured ultrasound emitters are urgently needed in biomedical imaging and treatment as well as non-destructive detection. In this work, we report a laser generated ultrasonic photoacoustic transducer (LGUPT) based on an ultra-thin layer of MXene (TiCT) nanosheets. Under the excitation of 532nm nanosecond laser pulses, the amplitude of the generated sound pressure can reach 8.7MPa, with a bandwidth of 17.4MHz at the irradiation intensity of 17.72mJ/cm. The photoacoustic conversion efficiency of the 1.2μm-thick MXene film/PDMS transducer was found to be 1.21×10, which is among the highest values reported to date. The MXene thin film can also be drop-casted on the curved surface of a focusing lens. The amplitude of the sound pressure signal can reach 25.3 MPa and the bandwidth 19.7MHz at a pulse laser energy of 28.12mJ/cm. The width of the focal spot at -3 dB of maximum amplitude was found in the range of 0.14mm for the optical lens based LGUPT under the condition of a laser spot diameter of 15mm by theoretical simulation. The water processable focusing LGUPT demonstrated excellent ultrasonic cavitation effect on the tissue mimicking agar plate. Our experimental and theoretical work highlights the potential of ultra-thin MXene film based LGUPTs for high precision photoacoustic therapy, integrated imaging and sensing instruments.

Extending the depth of focus is necessary in many scenarios. Recent progress in ultrasonic applications demands various kinds of foci and poses challenges to science and technologies. Here, we propose to connect individual foci forming an ultrasonic needle beam (UNB) through complex amplitude encoding with super-units. Two phase distributions are encoded into one metalens through super-units, which realizes a complex-amplitude modulation and achieve multifocal points in space. The performance of the metalens can be improved by adjusting the parameters of super-units. Both simulations and experiments have demonstrated that the metalens designed through the proposed method can efficiently produce single or twin UNBs. Our work has potential applications in biomedical treatment and imaging, remote communication, and nondestructive detection.

Coal seam water injection can effectively improve the water content of the coal seam and control the dust pollution in the mining process from the source. In this paper, we investigate the changes in internal fracture structure and water transport in coal samples by double treatment of surfactant and ultrasonic wave on anthracite samples. Ultrasonic treatment of coal samples immersed in water and observation of the difference in wetting characteristics before and after treatment. The results demonstrate that the primary fracture in the coal samples expands within 4-5 h of ultrasonic stimulation, a new fracture with an opening between 10 and 15 µm is generated during the stimulation process and the permeability of the coal samples increases by four to eight times compared with the untreated one. It is worth noting that water can fully penetrate the newly formed fracture during the ultrasonic intervals. Ultrasound can make surfactants dissolve better, but the thermal and cavitation effects of ultrasound can also inhibit the effect of surfactants in promoting water absorption in coal. The results guide future research and development of ultrasonic-enhanced water injection technology in coal seams.

PCA reconstruction-based techniques are widely used in guided wave structural health monitoring to facilitate unsupervised damage detection. The measurement interval of collecting evaluation data significantly influences the correlation among the data points, impacting principal component values and, consequently, the accuracy of damage detection. Despite its importance, there has been limited research on the selection of suitable components and measurement intervals to reduce false alarms. This paper seeks to develop strategies for identifying the optimal number of principal components and measurement intervals for PCA reconstruction-based damage detection methods. Our results indicate that the patterns of change in reconstruction coefficients, based on the number of components used in PCA reconstruction and the measurement interval for collecting evaluation data, are effective indicators for determining the optimal principal components and measurement intervals for damage detection, without using any damage information. The effectiveness of the indicators for determining optimal components and measurement intervals is validated using evaluation sets collected under uncontrolled and dynamic monitoring conditions, with measurement intervals ranging from 86 to 8600 s per measurement.

In this work, the spatiotemporal pressure field of MHz-focused ultrasound is measured using a background-oriented schlieren technique combined with fast checkerboard demodulation and vector tomography (VT-BOS). Hydrophones have been commonly employed to directly measure the local pressure in underwater ultrasound. However, their limitations include that they disturb the acoustic field and affect the measured pressure through the spatial averaging effect. To overcome such limitations, we propose VT-BOS as a non-contact technique for acoustic field measurements using only a background image and a camera. In our experiments, VT-BOS measures focused acoustic fields with a focal width of 1.0 mm and a frequency of 4.55 MHz, capturing traveling, reflected, and standing waves. We discuss three key features of this approach: (1) the temporal evolution of pressure measured by VT-BOS and hydrophones, (2) the differences in computational cost and spatial resolution between VT-BOS and other techniques, and (3) the measurement range of VT-BOS. The results demonstrate that VT-BOS successfully quantifies spatiotemporal acoustic fields and can estimate the hydrophones' spatial averaging effect over a finite area. VT-BOS measures pressure fields of several MPa with high spatiotemporal resolution, requiring less computational and measurement time. It is used to measure pressure amplitudes from 0.4 to 6.4 MPa, with the potential to extend the range to 0.3-201.6 MPa by adjusting the background-to-target distance. VT-BOS is a promising tool for measuring acoustic pressure in the MHz and MPa ranges, critical for applications such as vessel flow measurement and hydrophone calibration.

In this paper, a previously developed theoretical model for the ultrasonic elastic wave scattering, based on the Stanke and Kino model and applicable to both 2D and 3D single-phase untextured polycrystals, is extended to microstructures with elongated grains. The effect of elongated grains on wave attenuation and phase velocity induced by scattering is investigated, highlighting similarities and discrepancies between the 2D and 3D cases. Additionally, 2D and 3D finite element (FE) models are developed to compare and validate the theoretical predictions under fixed assumptions. The morphology of the numerical polycrystalline samples is characterized using a multi-exponential two-point correlation (TPC) function which, when incorporated with the theoretical model, enables a more direct and accurate comparison. The FE models demonstrate excellent quantitative agreement with the theoretical predictions and, moreover, support the wave propagation's directional dependency in the stochastic scattering region and the 2D-3D dimensionality dissimilarities in the Rayleigh region. It is shown that 2D attenuation can predict 3D behavior in the stochastic limit and provide insights into the estimation of 3D grain morphology in the Rayleigh limit.

Bulk-driven acoustic streaming flows induced by two different high-powered ultrasonic sources in air have been measured and characterised using particle image velocimetry (PIV). These time-averaged flows are driven by the attenuation of the acoustic energy, and appear as jets in the direction of the acoustic propagation. Langevin horns and a focussed array of transducers, which operate at acoustic frequencies of f≈27 kHz and f=40 kHz respectively, were used to create high pressure acoustic fields, with local sound pressure levels of over 160 dB. The magnitude of the acoustic streaming flows that resulted from the Langevin horn and the focussed array were up to V≈0.15m/s and V≈0.2m/s respectively. For a given peak acoustic pressure, the focussed array yielded higher acoustic streaming velocities due to the increased acoustic attenuation at the higher driving frequency. The shape of the acoustic field was found to govern the shape of the acoustic streaming velocity field, with the Langevin horn producing a wider jet with a more gradual velocity increase and decay than the focussed array. The focussed array induced a streaming velocity field where the maximum velocity occurred at a similar location to the peak acoustic pressure. Experimental PIV results were compared to a numerical model based on assumed weak non-linearity in which the attenuation of the first order pressure drives the streaming. The numerical model was able to predict the streaming velocity field with good qualitative and reasonable quantitative agreement.

Laser ultrasound is well suited to monitor metal additive manufacturing processes. Pulse laser-generated Rayleigh waveforms evolve with propagation distance due to material nonlinearity, making them a powerful tool for nondestructive evaluation, particularly for assessing microstructure. Unlike narrow-band Rayleigh waves, where the relative acoustic nonlinearity parameter is commonly used to evaluate material degradation, a pulse laser generates broadband unsymmetrical V-shaped waveforms whose spatial evolution we have characterized by a steepness parameter. Thermal aging precipitates multiple phases (including γ and γ) in Inconel718 samples that we documented by X-ray diffraction. These precipitates are associated with increased material nonlinearity. Comparing waveform spatial evolution, through changes in steepness, in samples before and after thermal aging revealed significant sensitivity to the material state. Thus, the technique has strong potential to provide unique insight into a material's microstructure and the mechanical properties dictated by that microstructure.

Due to the development of new materials and advanced manufacturing technologies, the application of large-scale composite structures has become increasingly widespread. Ensuring the safe and stable operation of such structures presents new challenges across various application domains. Addressing the limitations of existing guided wave structural health monitoring methods for online damage monitoring in large-scale structures, such as cumbersome equipment setup, insufficient signals coverage, and difficulties in processing massive data, a method for sub-area collaborative guided-wave-based structural damage monitoring and severity assessment based on sparse sensing is proposed. By employing a sparse sensing array layout, the structure is divided into multiple monitoring sub-areas with arranged sensing arrays to reduce overall complexity. The characteristic responses of the guided wave signals from different sub-areas are extracted to construct feature sub-spaces. Support vector machines are adopted to construct evaluation sub-networks in each feature sub-space, enabling regional monitoring. Additionally, an improved D-S evidence fusion algorithm is applied to fuse the decision-layer inputs from each evaluation sub-network, effectively utilizing the feature information from multiple sub-areas and enhancing the accuracy of damage severity assessment for large-scale structures. Experimental results on typical composite structure specimens demonstrate that by fusing the support vector machine evaluation results from each sub-area, the accuracy of damage severity assessment reaches 97.5%, with uncertainties in the severity assessment below 5%.