Ultrasonics最新文献

Ultrasound technology, as a versatile activation method, offers notable advantages in accelerating oxidative combustion, improving flow fields, and reducing emissions. Yet ultrasonic-assisted combustion currently faces two major limitations: first, the understanding of ultrasonic ignition and combustion mechanisms remains incomplete; second, existing applications predominantly rely on generic piezoelectric transducers, which are not tailored to the constrained environments of combustion chambers, where spatial limitations and fuel properties pose specific challenges, resulting in a lack of specialized transducers. This study integrated theoretical modeling with experimental validation to systematically design and optimize ultrasonic-assisted combustion igniters. A 35-kHz high-efficiency device was designed, and its impedance characteristics and dynamic resistance were optimized through an iterative optimization strategy based on impedance feedback. The piezoelectric stack topology was upgraded from a dual-plate configuration to a four-plate configuration, increasing the power-handling capacity by a factor of 3.7 relative to the original system. A focusing amplitude-rod acoustic architecture was introduced, markedly enhancing the flame development rate. Finite element simulations validated that the theoretical target frequency aligned with the longitudinal resonant mode, with an error of 0.03%. Experiments were conducted in a rarefied hydrogen environment, using a hydrogen-air mixture as the fuel on a constant-volume combustion bomb platform. Three ultrasonic-assisted combustion igniters exhibited different influences on flame development, but all promoted flame propagation, with more pronounced effects observed near the lean-burn limit. The results provide theoretical support and optimization strategies for practical applications of ultrasonic-assisted combustion technology.

This study presents a comprehensive analysis of guided wave propagation in viscoelastic [0/Φ/0] composite laminates over the frequency range of 1.3-6 MHz. Using both the Kelvin-Voigt and hysteretic viscoelastic models implemented within a Legendre polynomial framework, we systematically characterize attenuation behavior and establish a frequency-dependent selection principle: the Least Attenuated Wave (LAW) mode. A key finding is the numerical identification of a LAW mode that exhibits a quasi-isotropic trend above 5 MHz, acting as a high-frequency analogue of the classical S₀ mode. This mode exhibits weak dispersion, minimal sensitivity to fiber orientation, and wavelengths of 1-2 mm, enabling the detection of sub-millimeter defects such as micro-cracks and early delaminations. The analysis further reveals a direct correlation between attenuation maxima and Minimum Group Velocity (MGV) frequencies, clarifying the mechanisms of viscoelastic energy trapping in laminated composites. Practical attenuation maps as functions of frequency and fiber orientation are constructed, providing a valuable tool for designing multi-scale Structural Health Monitoring (SHM) systems. By comparing the two viscoelastic models, we establish bounds for attenuation predictions, offering essential guidance for experimental validation. This work bridges the gap between long-range monitoring and high-resolution local inspection, proposing a hierarchical SHM strategy suitable for advanced composite structures in aerospace and automotive applications.

With the rapid development of acoustic imaging and detection technologies, overcoming the intrinsic wave diffraction limit to achieve super-resolution imaging has long been a central goal in acoustics. In this work, we propose a genetic-algorithm-designed meta-lens fabricated via 3D printing that enables subwavelength ultrasound focusing in the far field (>20λ), surpassing the classical 0.5λ resolution limit with the lateral full width at half maximum of 0.42λ while maintaining deep penetration. Quantitative point spread function measurements validate the super-resolution performance and its superiority over the Fresnel lenses is demonstrated. By integrating the phase-modulated metamaterial design with high-precision 3D printing technique, our approach provides a practical and scalable strategy for super-resolution functional devices for biomedical diagnostics and non-destructive testing applications.

Guided wave ultrasonic testing (GWUT) in industrial environments is often limited by low signal-to-noise ratio (SNR), which reduces defect detectability. This study proposes a knowledge-guided framework that combines synthetic data generation with a tailored denoising network. From a single reference acquisition, paired clean and noisy signals are constructed using dual-Gaussian echo modeling and composite noise synthesis based on measured spectra. A Wavelet-Initialized Attention U-Net is developed with wavelet-informed kernels, a dual-decoder structure, and an attention bottleneck for efficient temporal integration. Experiments on two representative GWUT systems, a railway switch rail monitoring setup and a storage tank wall inspection robot, show that the proposed framework achieves up to 29.7 dB ROI-based SNR improvement on synthetic data, and substantial CNR improvement on real signals accompanied by a marked reduction of false detections (FP/FN), outperforming classical and deep learning baselines. The method also achieves real-time inference and efficient data generation with moderate computational cost. These results indicate that physics-guided synthesis combined with a tailored network provides a practical solution for GWUT denoising and supports reliable defect detection in industrial applications.