Ultrasound has been proven to be a valid tool for ranging, especially in water. In this paper, we design a high-resolution ultrasonic ranging system that uses a thin laser beam as an ultrasonic sensor. The laser sensing provides a noncontact method for ultrasound detection based on acousto-optic diffraction. Unlike conventional methods, the ultrasound transmitted from the transducer is recorded as the reference signal when it first passes through the laser. It can be used to improve the accuracy and resolution of the time-of-flight (TOF) by a cross-correlation method. Transducers with a central frequency of 1 MHz and diameters of 20 mm and 28 mm are used in the experiment. Five targets and a test piece are used to evaluate the ranging performance. The sound velocity is measured by the sound velocity profiler (SVP). The repeatability error of TOF is less than 4 ns, and the theoretical resolution of TOF is 0.4 ns. The results show a measurement resolution within one-tenth of the wavelength of ultrasound and an accuracy better than 0.3 mm for targets at a distance up to 0.8 m. The proposed system has potential applications in underwater ranging and thickness detection.

Stress corrosion cracks (SCC) in low-pressure steam turbine discs are serious hidden dangers to production safety in the power plants, and knowing the orientation and depth of the initial cracks is essential for the evaluation of the crack growth rate, propagation direction and working life of the turbine disc. In this paper, a method based on phased array ultrasonic transducer and artificial neural network (ANN), is proposed to estimate both the depth and orientation of initial cracks in the turbine discs. Echo signals from cracks with different depths and orientations were collected by a phased array ultrasonic transducer, and the feature vectors were extracted by wavelet packet, fractal technology and peak amplitude methods. The radial basis function (RBF) neural network was investigated and used in this application. The final results demonstrated that the method presented was efficient in crack estimation tasks.

Stress corrosion cracks are a huge potential security problem for steam turbine discs, and the quantitative detection of the initial cracks is significant for estimating the safety factor and the service life of the turbine discs. In this paper, the initial corrosion cracks are detected using the ultrasonic phased array, and a binary particle swarm optimization-radial basis function neural network (BPSO-RBFNN) model is proposed for the quantitative evaluation of small cracks in the early corrosion stage. The echoes reflected from the small cracks with different depths and orientation are acquired using the ultrasonic phased array. From these echo signals, some related features in the time domain, frequency domain and about nonlinearity are extracted. A quantitative analysis model based on the BPSO algorithm and the RBFNN is investigated for the feature optimization and the quantitative evaluation of the crack depth and orientation. The results showed that this method was effective in this quantitative evaluation work.

This paper proposes a method of sensing an acoustic wave field by merging the divergence sparsity and the acousto-optic interferometry. For simplicity, we call this method the Compressed Acousto-optic Interferometry. Specifically, the Compressed Acousto-optic Interferometry refers to the integration of compressed tomography with acousto-optic interferometry, which is a potential solution for the real-time capture of an acoustic wave field that will apply in building future sonar systems. In this sensing regime, two important improvements to the preceding works are: (1) using a specific diffraction order of the sensing laser caused by the acousto-optic effect to establish the interferometer, which omits the reference arms broadly adopted in conventional experiments; (2) using the sparse extremums of a cross-section of the acoustic wave field as the features to integrate the compressed sensing into the construction of an acoustic wave field, which will reduce the number of necessary lasers and photodetectors. Detailed construction of the corresponding mathematical model, simulations and experiments are presented in this paper. The results show that the Compressed Acousto-optic Interferometry proposed is potentially an effective method for capturing the acoustic wave field in real-time, maintaining the accuracy of the reconstruction, as well as reducing the number of laser-photodetector pairs by more than 30%.

Underwater sound speed plays a vital role in maritime safety. Based on the acousto-optic self-interference effect, we proposed a new method to measure underwater sound speed utilizing Raman–Nath diffraction, generated by the acousto-optic effect between an optical frequency comb and pulsed chirp signal. When the pulsed chirp travels between the measurement and reference arm in the experimental setup that we constructed, the same signal resulting from acousto-optic self-interference is produced. The time gap between the two identical signals represents the time interval. Thus, we can determine the time-of-flight using cross-correlation. The optical path difference between the two arms is double the flight distance of ultrasonic waves and can easily be obtained using femtosecond laser interferometry. The time gap and the distance can be used to measure sound speed. The experimental results show that the chirp signal improves the signal-to-noise ratio and expands the applicable time-of-flight algorithm. The waveform pulse width after cross-correlation is 1.5 μs, compared with 40 μs before. The time-of-flight uncertainty can achieve 1.03 ns compared to 8.6 ns before. Uncertainty of sound velocity can achieve 0.026 m/s.