Acoustics

In recent years, switched reluctance motors have emerged as a promising option for various applications due to their low manufacturing cost, rare-earth-free construction, and mechanical robustness. However, their widespread adoption is often limited by high torque ripple and acoustic noise. To address these challenges, this paper presents a comparative study of the acoustic noise performance of an 18/12 switched reluctance motor under various current control techniques. This comparison offers valuable insight into the motor’s vibroacoustic characteristics, which is essential for optimizing SRM performance, particularly in applications where noise reduction is critical. Dynamic simulations of an SRM are carried out in MATLAB/Simulink, and multi-physics analyses are performed in ANSYS Workbench. The multi-physics modeling includes electromagnetic, modal, and harmonic response analyses for four current control techniques evaluated across different operating speeds under light-load conditions. The simulation results are validated experimentally using an actual motor mounted on a dynamometer setup. The corresponding acoustic signatures for each control technique are presented as 2D plots of equivalent radiated power from simulations and sound power level from experimental tests. In addition, experimental waterfall diagrams are provided for each control technique.

Acoustic-based diagnosis (ABD) technology demonstrates promising application prospects for rotating machinery such as gears. However, non-stationary background noise may obscure or distort the target acoustic signal, potentially resulting in misdiagnosis or inadequate diagnosis in practical application. Therefore, preserving the inherent periodicity and sparsity features of mechanical sound signals from non-stationary background noise constitutes a critical challenge to facilitating the effective application of ABD in practical industrial environments. To address the shortcoming, this paper proposes an ABD method based on Recursive Generative Adversarial Denoising (RGAD). Specifically, a Global Window-aware Attention Module (GWAM)-based generator is first designed to reconstruct periodic structural features of gear rotational acoustic signals by adaptively representing non-stationary noise components and recursively capturing global dependencies in the time–frequency domain. Subsequently, a generative adversarial mechanism is established through developing a recursive discriminative architecture, which enables the model to effectively alleviate the vanishing gradients during adversarial learning and recover the texture details of gear acoustic features in a coarse-to-fine manner through progressive guidance. Finally, combined with a fault diagnosis network (FDN), a complete RGAD-based ABD framework is constructed. Experimental results demonstrate that the proposed method effectively suppresses noise components while simultaneously reconstructing the periodic characteristics and fine texture details of gear rotational acoustic signals, thereby significantly improving the accuracy and reliability for gear acoustic diagnosis in real industrial scenarios.

The development of sound-absorbing coatings for underwater structures has attracted significant attention due to their critical role in stealth and noise mitigation. While much of the recent research has focused on novel materials and complex configurations, the present study adopts a fundamentally different approach by establishing theoretical bounds on acoustic absorption that are independent of specific designs. Assuming only linearity and viscous damping, we model coatings using discrete mechanical elements characterized by mass, stiffness, and damping parameters. These models incorporate practical design constraints on added mass and hydrostatic compression of the coating. To identify configurations that maximize average acoustic absorption over a frequency range, we employ a Particle Swarm Optimization Algorithm that performs a global search over the constrained parameter space. A method for constraining the search space, which can be extended to any optimization algorithm, is presented and illustrated by examples. Perhaps surprisingly, our findings reveal that complex topologies yield only marginal performance gains compared to simpler configurations. For the canonical mass-spring-damper model, we derive closed-form approximations for absorption in the low-, mid-, and high-frequency regimes. These results establish performance ceilings for each topology, providing a benchmark for evaluating and guiding future material and structural innovations in underwater acoustic coatings.