Experiment and simulation for ultrasonic wave propagation in multiple-particle reinforced composites

Ultrasonic Signal Processing Method for Dynamic Burning Rate Measurement Based on Improved Wavelet Thresholding and Extreme Value Feature Fitting

Ultrasonic measurement techniques are increasingly used to measure the burning rates of solid rocket fuel, but challenges arise due to noise and signal attenuation caused by the motor's multi-layered structure. This paper proposes an adaptive thresholding method combined with a wavelet threshold function for effective ultrasonic signal denoising. Additionally, an extreme value feature fitting algorithm is introduced for accurate echo signal localization, even in low signal-to-noise ratio (SNR) conditions. Numerical simulations show a 10 dB improvement in SNR at -20 dB, with a correlation coefficient of 0.83 between the denoised and true signals. Echo localization tests across 12 SNR levels demonstrate a consistent error below 1 μs. Compared to other algorithms, the proposed method achieves higher precision, with a maximum displacement error of 0.74 mm. Hardware-in-the-loop experiments show an increase in SNR from -15 dB to 5.78 dB, with maximum displacement and rate errors of 0.9239 mm and 0.781 mm/s. In fuel-burning experiments, the burning rate curve closely matches the theoretical curve, with an initial fuel thickness error of only 0.12 mm, confirming the method's effectiveness in complex environments.

Investigating the flexural behavior of nanomodified multi-delaminated composites using acoustic emission technique

The formation of multiple delaminations is a frequently observed damage mechanism in composite materials, exerting a more pronounced influence on their strength properties compared to single delaminations. To tackle this issue, the incorporation of nanoparticles has been investigated as a means to enhance composite materials. This study aims to examine the effects of nano-additives, specifically carbon nanotubes and nanosilica, on the flexural behavior of glass/epoxy composites containing multiple embedded delaminations. The acoustic emission technique is employed to gain deeper insights into the damage mechanisms associated with flexural failure. Artificial delaminations of varying sizes, arranged in a triangular pattern, were introduced into four interlayers of a [(0/90)2]s oriented glass/epoxy composite. The findings reveal a notable reduction in flexural properties due to the presence of multiple delaminations. However, the addition of nanoparticles demonstrates a significant improvement in the flexural behavior of the multi-delaminated specimens. The most substantial enhancement is observed in the composite incorporating 0.3 wt% nanosilica + 0.5 wt% carbon nanotubes. Furthermore, genetic K-means and hierarchical clustering techniques are employed to classify different damage mechanisms based on the peak frequency and amplitude of the acoustic emission signals. The results indicate that the hierarchical clustering method outperforms the genetic K-means method in accurately clustering the acoustic emission signals. Moreover, the incorporation of nanoparticles' impact on the occurrence of distinct damage mechanisms is evaluated through the analysis of acoustic signals using Wavelet Packet Transform. By investigating the flexural behavior of nanomodified multi-delaminated composites and employing the acoustic emission technique, this study offers valuable insights into the role of nanoparticles in enhancing the mechanical properties and monitoring the damage mechanisms of composite materials.