Vol. 58 No. 1 (2024)

Open Access

Articles

Article ID: 1673

Loss Factors and their Effect on Resonance Peaks in Mechanical Systems
by Roman Vinokur

Sound & Vibration, Vol.58, No.1, 2024; 106 Views, 39 PDF Downloads

The loss factors and their effects on the magnitude and frequency of resonance peaks in various mechanical systems are reviewed for acoustic, vibration, and vibration fatigue applications. The main trends and relationships were obtained for linear mechanical models with hysteresis damping. The well-known features (complex module of elasticity, total loss factor, etc.) are clarified for practical engineers and students, and new results are presented (in particular, for 2-DOF in-series models with hysteresis friction). The results are of both educational and practical interest and may be applied for NVH analysis and testing, mechanical and aeromechanical design, and noise and vibration control in buildings.

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Open Access

Articles

Article ID: 1674

A Method of Evaluating the Effectiveness of a Hydraulic Oscillator in Horizontal Wells
by Zhen Zhong

Sound & Vibration, Vol.58, No.1, 2024; 53 Views, 22 PDF Downloads

Bent-housing motor is the most widely used directional drilling tool, but it often encounters the problem of high friction when sliding drilling in horizontal wells. In this paper, a mathematical model is proposed to simulate slide drilling with a friction reduction tool of axial vibration. A term called dynamic effective tractoring force (DETF) is defined and used to evaluate friction reduction effectiveness. The factors influencing the DETF are studied, and the tool placement optimization problem is investigated. The study finds that the drilling rate of penetration (ROP) can lower the DETF but does not change the trend of the DETF curve. To effectively work, the shock tool stiffness must be greater than some critical value. For the case study, the best oscillating frequency is within 15∼20 Hz. The reflection of the vibration at the bit boundary can intensify or weaken the friction reduction effectiveness, depending on the distance between the hydraulic oscillator and the bit. The optimal placement position corresponds to the plateau stage of the DETF curve. The reliability of the method is verified by the field tests. The proposed method can provide a design and use guide to hydraulic oscillators and improve friction reduction effectiveness in horizontal wells.

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Open Access

Articles

Article ID: 1675

Research on Human-Vehicle-Road Friendliness Based on Improved SH-GH-ADD Control
by Yangyang Bao

Sound & Vibration, Vol.58, No.1, 2024; 56 Views, 8 PDF Downloads

The hub-driven virtual rail train is a novel urban transportation system that amalgamates the benefits of modern trams and buses. However, this system is plagued by issues such as decreased ride comfort and severe deformation of urban roads due to the increase in sprung mass and long-term rolling at the same position. To address these concerns and improve the human-vehicle-road friendliness of the virtual rail train, we propose an Improved Sky-Ground Hook and Acceleration-Driven Damper control (Improved SH-GH-ADD control) strategy for the semi-active suspension system. This control monitors the vibration acceleration signal of the unsprung mass in real-time and selects the mixed Sky-Hook and Acceleration-Driven Damper (SH-ADD) control or the mixed Ground-Hook and Acceleration-Driven Damper (GH-ADD) control based on the positive and negative values of the vibration acceleration of the unsprung mass. The Improved SH-GH-ADD control combines the advantages of SH-ADD control and GH-ADD control to achieve control of the sprung mass and unsprung mass in the full frequency band. Finally, through simulation and comparative analysis with traditional SH-ADD, GH-ADD, and mixed SH-GH control, we demonstrate the exceptional performance of the proposed algorithm.

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Open Access

Articles

Article ID: 1676

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network
by Yunru Wu

Sound & Vibration, Vol.58, No.1, 2024; 65 Views, 9 PDF Downloads

Vehicle interior noise has emerged as a crucial assessment criterion for automotive NVH (Noise, Vibration, and Harshness). When analyzing the NVH performance of the vehicle body, the traditional SEA (Statistical Energy Analysis) simulation technology is usually limited by the accuracy of the material parameters obtained during the acoustic package modeling and the limitations of the application conditions. In order to effectively solve these shortcomings, based on the analysis of the vehicle noise transmission path, a multi-level objective decomposition architecture of the interior noise at the driver’s right ear is established. Combined with the data-driven method, the ResNet neural network model is introduced. The stacked residual blocks avoid the problem of gradient disappearance caused by the increasing network level of the traditional CNN network, thus establishing a higher-precision prediction model. This method alleviates the inherent limitations of traditional SEA simulation design, and enhances the prediction performance of the ResNet model by dynamically adjusting the learning rate. Finally, the proposed method is applied to a specific vehicle model and verified. The results show that the proposed method has significant advantages in prediction accuracy and robustness.

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Open Access

Articles

Article ID: 1677

Research on Stick-Slip Vibration Suppression Method of Drill String Based on Machine Learning Optimization
by Kanhua Su

Sound & Vibration, Vol.58, No.1, 2024; 73 Views, 29 PDF Downloads

During the drilling process, stick-slip vibration of the drill string is mainly caused by the nonlinear friction generated by the contact between the drill bit and the rock. To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations, the Fractional-Order Proportional-Integral-Derivative (FOPID) controller is used to suppress stick-slip vibrations in the drill string. Although the FOPID controller can effectively suppress the drill string stick-slip vibration, its structure is flexible and parameter setting is complicated, so it needs to use the corresponding machine learning algorithm for parameter optimization. Based on the principle of torsional vibration, a simplified model of multi-degree-of-freedom drill string is established and its block diagram is designed. The continuous nonlinear friction generated by cutting rock is described by the LuGre friction model. The adaptive learning strategy of genetic algorithm (GA), particle swarm optimization (PSO) and particle swarm optimization improved (IPSO) by arithmetic optimization (AOA) is used to optimize and adjust the controller parameters, and the drill string stick-slip vibration is suppressed to the greatest extent. The results show that: When slight drill string stick-slip vibration occurs, the FOPID controller optimized by machine learning algorithm has a good effect on suppressing drill string stick-slip vibration. However, the FOPID controller cannot get the drill string system which has fallen into serious stick-slip vibration (stuck pipe) out of trouble, and the machine learning algorithm is required to mark a large amount of data on adjacent Wells to train the model. Set a reasonable range of drilling parameters (weight on bit/drive torque) in advance to avoid severe stick-slip vibration (stuck pipe) in the drill string system.

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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 58 (2024)

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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.