Sound & Vibration

Description

Sound & Vibration is a journal intended for individuals with broad-based interests in noise and vibration, dynamic measurements, structural analysis, computer-aided engineering, machinery reliability, and dynamic testing. The journal strives to publish referred papers reflecting the interests of research and practical engineering on any aspects of sound and vibration. Of particular interest are papers that report analytical, numerical and experimental methods of more relevance to practical applications.

This journal is an indispensable reading and publishing area for all scientists, researchers, engineers, university and professional teachers, industrialists, and people in business interested in inventing, developing, implementing, commercializing, and using processes and products based totally or partly on sound and vibration.

Starting from Volume 59, 2025, Sound & Vibration will be published by Academic Publishing. As of 5 September 2024, new submissions should be made to the Open Journal Systems. To view your previous submissions, please access TSP system.

Papers are sought that contribute to the following general topics:

6. dynamic testing

Latest Articles

Open Access

Articles

Article ID: 2073

Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning
by Haiyang Ding, Ruiqian Wang, Xuefei Zhang, Ziyan Xu, Ancong Zhang, Lei Xu

Sound & Vibration, Vol.59, No.1, 2025; 29 Views, 9 PDF Downloads

DOI: https://doi.org/10.59400/sv2073

In order to improve the sound insulation performance of high-speed train floors, this study first obtained the necessary data for model training based on the reverberation test method, and then conducted data sorting and feature selection. Next, the maximum mutual information minimum redundancy (mRMR) feature selection algorithm was used to calculate the selected features and screen out a subset of significant features. Subsequently, the decision tree, BP neural network, and support vector machine regression (SVR) methods were applied in sequence, and the standardized feature data were used for the high-speed train floor under the same evaluation criteria of the mean square error ( MSE ) and coefficient of determination ( R 2 ). We conducted training and validation of the sound insulation prediction models for timber-framed support structures. The prediction accuracy of the trained model was compared and evaluated with the finite element statistical energy analysis (FE-SEA) prediction model. Finally, the SVR model was used to optimize the design under constraint conditions. The research results show that based on the research object, sample library, and model training in this article, compared with the FE-SEA model, the prediction error of the SVR model is only 0.3 dB, showing better performance. In engineering practice, the SVR model can effectively optimize the wooden support structure in the floor under certain constraints, and it predicts that the weighted sound insulation of the entire floor is 50.45 dB, which has important engineering application value.

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Open Access

Articles

Article ID: 2048

Design optimization of vibration amplitude reduction based on virtual prototype and machine learning
by Hong Bao, Jinxuan Tao, Jing Yang, Bin Cao, Liuxian Zhao

Sound & Vibration, Vol.59, No.1, 2025; 25 Views, 9 PDF Downloads

DOI: https://doi.org/10.59400/sv2048

The traditional design optimization of vibration amplitude reduction mainly has the disadvantages of low modeling and prediction accuracy as well as low optimization efficiency. Therefore, this paper presents a design optimization method for vibration amplitude reduction based on virtual prototyping and machine learning, which combines the high accuracy of numerical calculations with the efficiency of machine learning, overcoming the shortcomings of traditional methods. Firstly, sample points are collected through the design of experiments and virtual prototype simulation. Then, based on the sampled data, a prediction model for the relationship between the design parameters and the amplitude of the product is established using Genetic Algorithm-Support Vector Regression (GA-SVR). On the basis of the GA-SVR prediction model, a multi-objective optimization model of product is established, and Multiple Objectives Particle Swarm Optimization -entropy weight- Technique for Order Preference by Similarity to Ideal Solution (MOPSO-entropy weight-TOPSIS) is used to solve for the optimal design parameters. Finally, the washing machine suspension system is used as an example to verify the effectiveness of the model. The results show that, compared with the original design scheme, the design scheme obtained by the model can reduce the amplitude of the washing machine suspension system by 12.68%, and reduce the total weight of the counterweight by 7.35%. This method is conducive to the intelligent and efficient design optimization of vibration amplitude reduction, and is of great significance to product life cycle design.

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Open Access

Articles

Article ID: 2036

Research on intelligent vibration damping base of washing machine based on a stiffness-variable magnetorheological elastomer
by Zihan Li, Weifang Yin, Zefeng Li

Sound & Vibration, Vol.59, No.1, 2025; 81 Views, 8 PDF Downloads

DOI: https://doi.org/10.59400/sv2036

Traditional washing machine vibration-damping bases have a fixed stiffness, which can lead to structural resonance when the motor’s excitation frequency aligns with the machine’s inherent vibration frequency during the washing and spinning processes. This not only amplifies the noise caused by vibrations but also accelerates the wear and tear of the internal components. In this study, a pioneering approach has been introduced, developing an intelligent vibration-damping base for drum washing machines based on the dynamics of a mass-spring-damped single-degree-of-freedom system. This innovative base is designed with variable stiffness magnetorheological elastomer, utilizing the dynamics of the mass-spring-damped system to adaptively counteract vibrations. A vibration transmission rate curve specific to drum washing machines has been derived, and an intelligent control strategy for the drum washing machine’s vibration-damping base has been proposed. Dynamic testing of the drum washing machine’s vibration-damping base was conducted to verify the effectiveness of the intelligent damping base. The intelligent damping base addresses the unavoidable low-frequency resonance issues of traditional passive damping bases in washing machines and overcomes the drawback that the performance parameters of traditional passive isolation structures cannot be changed once set, providing a new direction for the damping of household appliances.

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Open Access

Articles

Article ID: 1787

Acoustic sensor-based field efficacy evaluation of three different insecticides—Trunk injections against the red palm weevil, Rhynchophorus ferrugineus
by Abdalsalam O. Omer, Hattan. A. Alharbi, Mureed Husain, Khawaja G. Rasool, Waleed S. Alwaneen, Abdulrahman S. Aldawood

Sound & Vibration, Vol.59, No.1, 2025; 7 Views, 3 PDF Downloads

DOI: https://doi.org/10.59400/sv1787

Red palm weevil (RPW) is one of the major pests that has caused significant losses in date palm production worldwide in recent years. Effective management of RPW is important to minimizing its impact on date palm yields. Conventional techniques utilized to manage RPW have shown minimal effectiveness. The study aimed to evaluate the efficacy of the insecticides Fipronil, Imidacloprid, and Thiamethoxam against RPW by applying a trunk injection technique in naturally infested date palm fields. Additionally, the study monitored the efficacy of the insecticides for ten months post-treatment using an acoustic sensor. After treatment with Fipronil, Imidacloprid, and Thiamethoxam, the mean burst rate impulses from RPW sound activities inside the date palm trunk was reduced, confirming the gradually mortality of RPW. The RPW impulse burst rate was decreased within 1–2 months post-treatment with these insecticides, while it increased in the control treatment. The results reveal that Fipronil reduced the RPW impulse burst rate from 0.50/s on day 0 to 0.07/s after 50 days post-treatment. In comparison, Imidacloprid reduced the RPW impulse burst rate to 0.07/s after 70 days post-treatment, which indicates a low level of infestation. Similarly, Thiamethoxam reduced the impulse burst rate from 0.97/s on day 0 to 0.08/s after 70 days of treatment. After 4 months of insecticide treatments, the RPW impulse burst rate dropped to zero which indicates the complete cessation of the RPW sound activities. The results suggest that a balloon injector may aid in delivering insecticides directly into the date palm trees, reaching the target more effectively. Furthermore, the acoustic sensor proved to be an effective tool for detecting and monitoring RPW activities in date palms.

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Open Access

Articles

Article ID: 1702

Coupled Helmholtz resonators for broadband Aeroacoustic noise mitigation
by Zixiang Xiong, Xuxu Zhuang, Zhaoyong Sun, Liuxian Zhao

Sound & Vibration, Vol.59, No.1, 2025; 55 Views, 13 PDF Downloads

DOI: https://doi.org/10.59400/sv1702

As a structurally simple acoustic element, Helmholtz resonators can exhibit strong resonance when acoustic waves enter the cavity, thus providing excellent sound absorption effects. Consequently, they are widely applied in automotive engine and exhaust systems. This paper systematically investigates the noise reduction performance of multiple coupled Helmholtz resonators under conditions with and without tangential flow. A finite element simulation model with multiple Helmholtz resonators is established by employing COMSOL Multiphysics software to solve the linearized Navier-Stokes equations in the frequency domain. The simulation results demonstrate that the structure, which couples multiple Helmholtz resonators, can effectively broaden the low-frequency sound absorption band under the influence of a flow field, enhancing the transmission loss across the entire low-frequency band. This structure holds significant potential for applications in automotive exhaust systems and aero-engine noise reduction.

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Open Access

Articles

Article ID: 1978

Adapting music education to the new normal: Integrative teaching strategies from a global perspective
by Yuchen Guo

Sound & Vibration, Vol.59, No.1, 2025; 52 Views, 12 PDF Downloads

DOI: https://doi.org/10.59400/sv1978

This study explores how music education can innovate through various integrative teaching strategies in the global “new normal”. Challenges and opportunities are identified. It highlights the need for educational reform and strengthening cultural identity in the post-pandemic world. By comparing global music education models, the positive role of cultural exchange in fostering diversity is examined. Based on the experimental findings, the study introduces “integrative teaching”, focusing on student-centered methods and interdisciplinary approaches. The experiment results show that integrating music with technology, language arts, and environmental science can enhance creativity, cultural understanding, and environmental awareness. In addition, practical examples are listed to demonstrate the effective use of virtual tools in addressing ecological issues through music creation. The study also underscores the importance of school-community collaboration and highlights successful case studies from Asia, Europe, and the U.S. How interdisciplinary strategies improve student learning outcomes are determined. More importantly, an evaluation framework focusing on diversified feedback and personalized assessments is established, proving that continuous feedback can enhance student engagement.

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Editor-in-Chief

Prof. Jun Yang

Institute of Acoustics, Chinese Academy of Sciences, China

ISSN

1541 - 0161 (Print)

2693 - 1443 (Online)

Publication Frequency

Continuously

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

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Volume Arrangement

Vol 59 Iss 1 (2025)

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Featured Articles

Soundscapes in Arab Cities: A Systematic Review and Research Agenda

In the context of Arab cities, this study explores the intricate interplay between cultural, historical, and environmental elements that shape their unique soundscapes. The paper aims to shed light on this underrepresented field of study by employing a three-fold research approach: systematic review, a comprehensive literature review, and the formulation of a future research agenda. The first part of the investigation focuses on research productivity in the Arab world regarding soundscape studies. An analysis of publication trends reveals that soundscape research in Arab cities is still an emerging area of interest. Critical gaps in the existing body of literature are identified, highlighting the importance of addressing these gaps within the broader context of global soundscape research. The second part of the study delves into the distinctive features that inform the soundscapes of Arab cities. These features are categorized into three overarching groups: (i) cultural and religious life, (ii) daily life, and (iii) heritage and history, by exploring these factors, the study aims to elucidate the multifaceted nature of Arab urban soundscapes. From the resonating calls to prayer and the vibrant ambiance of traditional cafes to the bustling markets and architectural characteristics, each factor contributes to the auditory tapestry that defines Arab cities. The paper concludes with a forward-looking research agenda, proposing sixteen key questions organized into descriptive and comparative categories. These questions emphasize the need for a more profound understanding of sound perception, sources, and the impact of urban morphology on the soundscape. Additionally, they highlight the need for interdisciplinary research, involving fields such as urban planning, architecture, psychology, sociology, and cultural studies to unravel the complexity of Arab urban soundscapes.

Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

The Noise, Vibration, and Harshness (NVH) experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package. As such, accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs. However, the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering (CAE) methods, leading to low modeling efficiency and prediction accuracy. To address these limitations, a hierarchical multi-objective decomposition system for predicting the sound insulation performance of the floor acoustic package has been developed based on an analysis of the noise transmission path. This approach involves introducing a 1D-Convolutional Neural Network (1D-CNN) model for predicting the sound insulation performance of the floor acoustic package, thereby avoiding the limitations of conventional CAE approaches that rely solely on “data-driven” methods. The proposed method was applied and tested using specific vehicle models, and the results demonstrated the effectiveness and superiority of the proposed approach relative to those obtained using 2D-CNN and Support Vector Regression (SVR) models.

A Sound Quality Evaluation Method for Vehicle Interior Noise Based on Auditory Loudness Model

When designing and optimizing the hull of vehicles, their sound quality needs to be considered, which greatly depends on the psychoacoustic parameters. However, the traditional psychoacoustic calculation method does not consider the influence of the real human ear anatomic structure, even the loudness which is most related to the auditory periphery. In order to introduce the real physiological structure of the human ear into the evaluation of vehicle sound quality, this paper first carried out the vehicle internal noise test to obtain the experimental samples. Then, the physiological loudness was predicted based on an established human ear physiological model, and the noise evaluation vector was constructed by combining the remaining four psychoacoustic parameters. Finally, the evaluation vector was fitted into the subjective evaluation results of vehicle interior noise by a deep neural network. The results show that our proposed method can estimate the human subjective perception of vehicle interior noise well.

Prediction and Analysis of Vehicle Interior Road Noise Based on Mechanism and Data Series Modeling

Currently, the inexorable trend toward the electrification of automobiles has heightened the prominence of road noise within overall vehicle noise. Consequently, an in-depth investigation into automobile road noise holds substantial practical importance. Previous research endeavors have predominantly centered on the formulation of mechanism models and data-driven models. While mechanism models offer robust controllability, their application encounters challenges in intricate analyses of vehicle body acoustic-vibration coupling, and the effective utilization of accumulated data remains elusive. In contrast, data-driven models exhibit efficient modeling capabilities and can assimilate conceptual vehicle knowledge, but they impose stringent requirements on both data quality and quantity. In response to these considerations, this paper introduces an innovative approach for predicting vehicle road noise by integrating mechanism-driven and data-driven methodologies. Specifically, a series model is devised, amalgamating mechanism analysis with data-driven techniques to predict vehicle interior noise. The simulation results from dynamic models serve as inputs to the data-driven model, ultimately generating outputs through the utilization of the Long Short-Term Memory with Autoencoder (AE-LSTM) architecture. The study subsequently undertakes a comparative analysis between different dynamic models and data-driven models, thereby validating the efficacy of the proposed series vehicle road noise prediction model. This series model, encapsulating the rigid-flexible coupling dynamic model and AE-LSTM series model, not only demonstrates heightened computational efficiency but also attains superior prediction accuracy.