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Ultrasonic nondestructive evaluation is used for detection, characterization, and sizing of defects. The accurate sizing of defects that are of similar or less size than the ultrasonic wavelength is of particular importance in assessing structural integrity. In this paper, we demonstrate how measurement of the scattering coefficient matrix of a cracklike defect can be used to obtain its size, shape, and orientation. The scattering coefficient matrix describes the far field amplitude of scattered signals from a scatterer as a function of incident and scattering angles. A finite element (FE) modeling procedure is described that predicts the scattering coefficient matrix of various cracklike defects. Experimental results are presented using a commercial 64-element, 5 MHz array on 2 aluminum test samples that contain several machined slots and through thickness circular holes. To minimize the interference from the reflections of neighboring defects, a subarray approach is used to focus ultrasound on each target defect in turn and extract its scattering coefficient matrices. A circular hole and a fine slot can be clearly distinguished by their different scattering coefficient matrices over a specific range of incident angles and scattering angles. The orientation angles of slots directly below the array are deduced from the measured scattering coefficient matrix to an accuracy of a few degrees, and their lengths are determined with an error of 10%.

We observe distinct regimes of orbital angular momentum (OAM) transfer from two-dimensional Bessel-shaped acoustic vortices to matter. In a homogeneous diphasic mixture of microparticles and water, slow swirling about the vortex axis is seen. This effect is driven by absorption of OAM across the mixture, the motion following the OAM density distribution. Larger particles are formed into clusters by the acoustic radiation force making the mixture non-homogeneous. Here OAM transfer to the microparticle clusters dominates and they spin at high speeds entraining the surrounding fluid.

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The roughness of crack-like defects affects ultrasonic wave scattering and this, in turn, affects defect detection and characterization. The first part of this paper is concerned with the efficient numerical modeling of scattering from rough cracks, i.e., a finite element local scattering (FELS) model. The scattered field is presented in the form of a scattering matrix, which describes the far-field scattering coefficient for all possible combinations of incident and scattering directions. The scattering matrices for many different realizations of rough cracks are simulated using both a FELS model and a model based on the Kirchhoff approximation. It is shown that the difference between scattering matrices extracted from the Kirchhoff model and the FELS model is less than 8%, for rough cracks with a standard deviation less than 0.3 wavelengths and a correlation length longer than 0.5 wavelengths, at incident and scattering angles ranging from -80° to 80° relative to the normal direction of the mean surface. Because the Kirchhoff model is significantly more efficient than the FELS model, it is used for subsequent simulations in which many realizations of rough cracks are studied to gain insight into the statistical nature of the scattering process. In line with previous work, a distinction is made between the coherent and diffuse contributions to the overall scattered field, in which the former represents the ensemble average over multiple surface realizations. The coherent and diffuse contributions of scattered field from various types of rough cracks are simulated. It is shown that surface roughness directly affects the coherent contribution to scattering behavior, whereas the diffuse contribution is affected by both surface roughness and correlation length, especially for rougher cracks.

The quality of an ultrasonic array image depends on accurate information about its acoustic properties. Inaccurate acoustic properties can cause image degradation such as blurring, mislocation of reflectors, and the introduction of artifacts. In this paper, for the specific case of an inhomogeneous and anisotropic austenitic steel weld, Monte Carlo Markov Chain (MCMC) inversion is used to estimate unknown acoustic properties from array data. The approach uses active beacons that transmit ultrasound through the anisotropic weld; the ultrasound is then captured by a receiving array. A forward model of the ultrasonic array data is then optimized with respect to the experimental data using an MCMC inversion. The result of this process is the extraction of a material property map that describes the anisotropy distribution within the weld region. These extracted material properties are then used within an imaging algorithm-the total focusing method in this paper-to produce autofocused images. This MCMC inversion approach is first applied to simulated data to test the convergence, robustness, and accuracy of the method and its implementation. The extracted weld map is used to show improved imaging of defects within the weld, relative to an image formed assuming a constant velocity. Finally, the MCMC inversion approach is used on experimental data from a 110-mm-thick steel plate containing an austenitic weld. Here the extracted weld map is used to show that defect location errors of greater than 5 mm are reduced to around 2 mm when the extracted weld map is used.

All naturally occurring crack-like defects in solid structures are rough to some degree. Based on simulated array data for incident and scattered longitudinal waves from various rough cracks and the total focusing method (TFM) imaging algorithm, the effect of roughness on defect imaging and characterization is examined. The array data are simulated by using a previously validated model based on the Kirchhoff approximation to predict the rough crack scattering matrix. The scattering matrix describes the scattered field for all possible incident and scattering directions. The performance of TFM imaging for detecting rough crack-like defects is investigated by considering the averaged scattering coefficient over specified angular coverage ranges and the maximum scattered amplitude in the final image. It is shown that roughness can be either beneficial or detrimental to the detectability of a crack-like defect, depending on the defect characteristics, such as length, roughness, correlation length, orientation angle, and array inspection configuration. It is also shown that roughness can cause underestimation of the crack length if using an image-based approach for sizing.

Ultrasonic array imaging algorithms have been widely used and developed in nondestructive evaluation in the last 10 years. In this paper, three imaging algorithms [total focusing method (TFM), phase-coherent imaging (PCI), and spatial compounding imaging (SCI)] are compared through both simulation and experimental measurements. In the simulation, array data sets were generated using a hybrid forward model containing a single defect among a multitude of randomly distributed point scatterers to represent backscatter from material microstructure. The number of point scatterers per unit area and their scattering amplitude were optimized to reduce computation cost. The SNR of the final images and their resolution were used to indicate the quality of the different imaging algorithms. The images of different types of defects (point reflectors and planar cracks) were used to investigate the robustness of the imaging algorithms. It is shown that PCI can yield higher image resolution and higher SNR for defects in material with weak backscatter than TFM, but that the images of cracks are distorted. Overall, TFM is the most robust algorithm across a range of different types of defects. It is also shown that the detection limit of all three imaging algorithms is almost equal for weakly scattering defects.