Vol. 59 No. 3 (2025)

Open Access

Article

Article ID: 2315

A new Helmholtz type sonic crystal for wide-band sound attenuation

by Javad Goodini, Davood Younesian

Sound & Vibration, Vol.59, No.3, 2025;

In this paper, a Helmholtz shape sonic crystal is proposed for bandgap realization and sound attenuation. Using Bloch’s theory, bandgap properties of the sonic crystal are investigated for the primitive design of the unit-cell. A geometrical parametric study is implemented for the unit-cell to present its potential in creating bandgaps over the low-frequency range, and an optimization is applied to find its best design according to the low-frequency objective function. A frequency analysis and experimental tests are used to verify the calculated bandgaps from Bloch’s theory and to confirm the sound attenuation ability of the proposed design. It is shown that the present design not only creates wide bandgap frequencies in the low-frequency range but also, due to the Helmholtz shape of the unit-cell, provides significant sound attenuation.

Open Access

Article

Article ID: 2656

Evaluation of acoustic performance of Guzheng based on dynamic measurement

by Chenyan Du

Sound & Vibration, Vol.59, No.3, 2025;

This research assessed the Acoustic performance of Guzheng using dynamic measurement technology. The study conducted spectral analysis, examined the correlation between playing techniques and sound productions, defined major sound quality factors, and proposed solutions for enhancing the instrument. In dynamic measurement, dynamic frequency spectra were obtained, which were different from those of the normal Guzheng and had traditional characteristics of Guzheng acoustics. There were significant relationships between playing techniques and particular acoustic results. There were strong correlations between sound quality parameters; thus, it was possible to optimize them systematically. The findings offered specific recommendations for improving Guzheng production that were detailed to retain traditional qualities. The research set quantitative measures for Acoustics analysis and other quality control in Guzheng manufacturing and playing

Open Access

Article

Article ID: 2124

Biodegradable microcrystalline cellulose composites: Optimization of isotropic polybutene-1 crystallization and its vibration and noise reduction properties

by Chafaa Hamrouni, Aarif Alutaybi, Jacer Hamrouni, Nahaa Eid B. Alsubaie

Sound & Vibration, Vol.59, No.3, 2025;

With the acceleration of industrialization, environmental noise and mechanical vibration problems are becoming increasingly serious. Traditional polymer damping materials have shortcomings in durability, environmental protection and performance optimization. In this study, MCC-iPB biodegradable composites were prepared. The interface and crystallization behavior were optimized by introducing maleic anhydride grafted polybutene-1 (MAPB). Experimental results showed that MCC can accelerate the crystal transformation and significantly improve the thermal stability and storage modulus. In the comparative experiment under the simulated operating conditions of industrial motors, compared with traditional polyurethane foam materials, the MCC-iPB composite material can reduce the vibration acceleration by about 40%, the sound pressure level by 6–9 dB, and the power fluctuation by more than 50% in the range of 500–2500 Hz, showing excellent energy dissipation and vibration suppression capabilities. This material has broad engineering application prospects in the fields of building sound insulation, automobile structure noise reduction and high-frequency industrial equipment shock absorption, and provides a new theoretical basis and experimental support for the development of sustainable vibration and noise reduction materials.

Open Access

Article

Article ID: 3143

Physics-informed GRU model for vehicle road noise prediction: Integrating transfer path analysis and hybrid data

by Yan Ma, Ruxue Dai, Tao Liu, Mingzheng Wang, Qichen Ying, Haibo Huang

Sound & Vibration, Vol.59, No.3, 2025;

With the continuous release of automobile market potential and the steady growth of automobile ownership, consumers’ concern for automobile NVH problems makes the road noise performance directly affect the sales of automobiles. Therefore, it is of great significance to study the problem of vehicle road noise. In the actual research process, it often faces the challenge of insufficient effective sample size. In this paper, 102 sets of sample data are collected by combining the real vehicle test method and the CAE simulation method. By comparing the road noise prediction model based on the GRU algorithm with the LSTM model and the CNN model, the results show that the GRU model performs roughly similarly to the LSTM model in terms of prediction accuracy (RMSE = 2.18) and robustness (MSE = 7.66%), and the GRU model and the LSTM model are significantly better than the CNN model, but the prediction efficiency of the GRU model is significantly better than the LSTM model. Therefore, the vehicle road noise prediction model based on the GRU algorithm is optimal. This paper provides an efficient method for road noise performance analysis and prediction, which can be applied to the vehicle design and performance improvement process and provide technical support for improving vehicle comfort and market competitiveness.

Open Access

Article

Article ID: 2973

Optimization of noise barriers in a ±500 kV switchyard: Field testing and numerical modelling

by Li Li, Shenghui Xu, Xing Du, Cai Zeng, Meng Wei, Xinbiao Xiao

Sound & Vibration, Vol.59, No.3, 2025;

This study investigates noise characteristics of a ±500 kV high-voltage (HV) switchyard through field testing. Noise levels and spectral properties near the reactor and fence were analyzed. An acoustic boundary element method (BEM) model was developed to predict the reactor-generated noise. Using this model, the effects of fence height, the height and placement of an additional noise barrier on the combined noise reduction effects (NRE) were systematically examined. To further enhance the NRE, various top structures and sound-absorbing materials were applied to noise barriers, and their additional NREs were compared. The results indicate that increasing fence height by 1–2 m reduces the sound pressure levels (SPL) outside the fence by 1–5.8 dB. Adding L-type or T-type top structures further reduces SPLs by 0.4–4.6 dB. Installing an additional erect noise barrier between the reactor and fence reduces SPLs by 1.4–8.8 dB, with the insertion loss (IL) increasing by 0.6–2.8 dB per 1-m barrier height increase. The T-type or Y-type top structures on the barriers also reduce SPLs by 0.4–4.6 dB, while traditional sound-absorbing materials have minimal impact on the NRE.

Open Access

Article

Article ID: 2112

Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances

by Chafaa Hamrouni, Aarif Alutaybi

Sound & Vibration, Vol.59, No.3, 2025;

In complex environments such as industrial sites and rail transit, communication equipment often faces multi-source interference from mechanical vibration and structural noise, which seriously affects its signal quality and transmission stability. Although previous studies have explored the influence mechanism of a single interference source, there is still a lack of in-depth understanding and quantitative modeling of the coupled interference effect of vibration and noise. To this end, this paper builds an experimental platform based on the ESP32 Wi-Fi communication module, which includes controllable electromagnetic vibration and sound pressure loading, and collects communication performance indicators (RSSI, BER, throughput and delay) and synchronous physical disturbance data under different interference conditions. Through multivariate statistics and variance analysis methods, the interaction law between vibration frequency, amplitude and noise sound pressure level is revealed for the first time. It is found that the combination of the two under medium and high intensity conditions will cause significant nonlinear amplification effects, which will have a synergistic degradation effect on communication performance. The long short-term memory neural network (LSTM) is further introduced to construct a time series prediction model under multi-disturbance environment. The results show that the model has excellent fitting accuracy (R² > 0.97) in RSSI and throughput prediction tasks, which is better than the comparison models such as SVM and polynomial regression, and has good feedforward control potential. The study also proposed communication anti-interference optimization suggestions and equipment structure improvement strategies suitable for industrial and rail scenarios, providing a theoretical basis and experimental support for the intelligent adaptive design of wireless communication systems in high-interference environments.

Open Access

Article

Article ID: 2059

Impact of vibration on wind turbine efficiency and LSTM-based power conversion prediction

by Aarif Alutaybi, Chafaa Hamrouni

Sound & Vibration, Vol.59, No.3, 2025;

During the long-term operation of wind power generation systems, the impact of mechanical vibration on energy conversion efficiency is often overlooked. Existing studies mostly use vibration signals as a means of fault warning, lacking a systematic analysis of the quantitative relationship between vibration characteristics and power generation efficiency. This study, based on Supervisory Control and Data Acquisition (SCADA) data and high-frequency vibration monitoring from a wind farm, extracts vibration features from both time domain (e.g., root mean square, peak value, skewness, and kurtosis) and frequency domain (e.g., dominant frequency and spectral energy ratio). Through Pearson and Spearman correlation analyses, as well as a comparative time series analysis of high-vibration intervals (revealing an average efficiency drop of 3.5%), it is demonstrated that intensified vibrations significantly reduce generation efficiency and increase output fluctuations. Furthermore, a dual-layer LSTM prediction model is proposed, integrating wind speed, wind direction, temperature, and vibration features. The training process is optimized using a sliding window strategy, Dropout regularization, and early stopping. On the test set, the model achieves an RMSE of 0.035 and a MAPE of 3.6%, outperforming support vector machines (SVM), random forests, and single-layer GRU models by 20%–40% in accuracy. Finally, an integrated “monitoring–prediction–warning–control” framework is proposed to support real-time deployment and intelligent operation and maintenance (O&M), offering a practical solution for wind farm health management and O&M optimization.

Open Access

Article

Article ID: 3502

Small-sample and imbalanced milling chatter detection: Improved GAN with attention and hybrid deep learning

by Haining Gao, Xinli Xiong, Hongdan Shen, Yong Yang, Yinlin Wang

Sound & Vibration, Vol.59, No.3, 2025;

Chatter detection during milling processes plays a pivotal role in ensuring machining quality and efficiency. While the accuracy of chatter detection heavily relies on experimental data, systems tend to exhibit overfitting phenomena under conditions of limited training samples, resulting in diminished detection precision. To address this limitation, this study presents a data augmentation algorithm based on an Improved Generative Adversarial Network (IGAN). This algorithm integrates advanced techniques including Wasserstein distance metrics, cycle consistency constraints, and channel attention mechanisms, effectively enhancing the quality of generated data. An innovative milling chatter detection deep learning model (MNBGA) is constructed, synthesizing cutting-edge architectures such as multi-scale convolutional neural networks, bidirectional gated recurrent neural networks, and attention mechanisms. To optimize model performance, the Ivy algorithm is employed for hyperparameter optimization of the MNBGA model. When the training dataset comprises 40 or more samples, the proposed method achieves detection accuracy exceeding 90%. Notably, under extreme imbalanced data conditions (24:1:1 ratio), the detection accuracy maintains 84.32%. The processing time for 40 samples requires only 76.17 ms, meeting real-time monitoring requirements. This research presents a novel technical solution for addressing the challenge of milling chatter detection under small-sample conditions.

Open Access

Article

Article ID: 3478

Type-2 fuzzy logic framework for adaptive noise control in vibrating structures

by N. Yogeesh, N. Raja, Asokan Vasudevan, H. C. Shashikumar, P. C. Jayaprakasha

Sound & Vibration, Vol.59, No.3, 2025;

Active noise control (ANC) in vibrating structures often suffers performance degradation under uncertain excitation and parameter drift. This study introduces an interval Type-2 fuzzy logic controller (IT2 FLC) for adaptive ANC in multi-mode systems, explicitly modelling “uncertainty about uncertainty” via a footprint of uncertainty in the fuzzy rule base. A two-degree-of-freedom mass–spring–damper model is used to represent structural dynamics, and both broadband and tonal disturbances are simulated. The IT2 FLC adapts its membership-function bounds online based on error variance, yielding a robust control law that compensates for up to ±25% drift in mass and stiffness. Controller performance is evaluated in MATLAB/Simulink and on a dSPACE DS1104 rapid-prototyping platform interfaced with a physical beam rig. Compared to classical ANC methods such as least-mean-square filtering and H∞ control, as well as Type-1 fuzzy logic, the proposed interval Type-2 fuzzy logic controller (IT2 FLC) consistently demonstrates superior performance. It achieves up to 28 dB broadband attenuation under nominal conditions and sustains over 23 dB even under ±25% structural parameter drift, significantly outperforming other benchmarks. The controller exhibits fast convergence (≤ 0.35 s) and maintains real-time feasibility with ≤ 25% CPU utilization at a 1 kHz update rate on standard DSP hardware. A hardware-in-the-loop implementation on a physical cantilever beam rig confirms robustness and stability. These results validate the IT2 FLC as a computationally efficient, highly adaptive, and cost-effective solution for industrial noise control applications in uncertain environments.