Renewable Energy Systems: Advances in Sound, Vibration, Structural Health Monitoring, and Intelligent Diagnostics

Deadline for Manuscript Submissions: 31 August 2027

Special Issue Editor

Hitesh Panchal Website E-Mail: engineerhitesh2000@gmail.com
Gujarat Technological University, Ahmedabad, Gujarat, India.
Interests: mechanical engineering, Thermal Engineering, vibration analysis; nanomaterials

Background and Motivation

Renewable energy technologies are playing a central role in the global transition toward a low-carbon and sustainable future. Wind turbines, solar photovoltaic farms, concentrated solar power plants, hydropower facilities, and offshore renewable energy systems are being deployed at an unprecedented scale. As these installations become larger and more complex, ensuring their reliability, safety, and long-term performance has become increasingly important.

Many renewable energy systems operate under variable environmental and loading conditions that can lead to excessive vibration, noise generation, component degradation, and structural failures. Wind turbine blades are subjected to cyclic aerodynamic loads, solar tracking systems experience continuous motion and environmental stresses, while hydropower equipment faces vibration and cavitation-related challenges. These issues can reduce energy production, increase maintenance costs, and shorten system lifespan.

Recent developments in structural health monitoring, smart sensing technologies, artificial intelligence, machine learning, digital twins, and advanced signal processing have created new opportunities for monitoring system health, detecting faults at an early stage, and implementing predictive maintenance strategies. The integration of these technologies with sound and vibration analysis offers effective solutions for improving the performance and reliability of renewable energy infrastructure.

This Special Issue aims to provide a platform for researchers, scientists, and industry professionals to share the latest advances in sound and vibration engineering, structural health monitoring, and intelligent diagnostics for renewable energy systems.

Aim and Scope

The Special Issue seeks high-quality original research articles, review papers, and case studies that address emerging challenges and innovative solutions related to sound, vibration, structural health monitoring, and intelligent diagnostics in renewable energy applications.

Contributions focusing on experimental investigations, numerical modeling, signal processing, machine learning, digital twins, smart materials, and predictive maintenance are particularly encouraged.

Topics of Interest

The Special Issue welcomes submissions including, but not limited to:

Wind Energy Systems

Wind turbine blade vibration analysis
Gearbox and bearing fault diagnostics
Aeroelastic instability and structural dynamics
Offshore wind turbine vibration control
Wind turbine noise prediction and mitigation

Solar Energy Systems

Vibration assessment of solar tracking systems
Structural dynamics of photovoltaic installations
Sound and vibration issues in concentrated solar power plants
Fault detection in solar energy systems
Smart monitoring of solar infrastructures

Hydropower and Marine Renewable Energy

Turbine vibration and cavitation-induced noise
Structural health monitoring of hydropower plants
Wave and tidal energy converter dynamics
Offshore renewable platform monitoring

Structural Health Monitoring

Sensor-based condition monitoring
Wireless sensor networks
Acoustic emission techniques
Damage detection and prognosis
Remaining useful life prediction

Intelligent Diagnostics and AI

Machine learning-based fault diagnosis
Deep learning for vibration signal analysis
Physics-informed neural networks
Digital twin technologies
Data-driven maintenance frameworks

Advanced Vibration and Acoustic Technologies

Active and passive vibration control
Smart materials and adaptive structures
Acoustic metamaterials
Noise reduction technologies
Energy harvesting from ambient vibrations

Computational and Experimental Methods

Finite element modeling
Vibro-acoustic simulations
Multiphysics analysis
Signal processing and feature extraction
Uncertainty quantification and reliability assessment

Keywords

Renewable Energy Systems; Wind Turbines; Solar Energy; Hydropower; Structural Health Monitoring; Condition Monitoring; Intelligent Diagnostics; Artificial Intelligence; Machine Learning; Digital Twins; Vibration Control; Noise Reduction; Vibro-Acoustics; Fault Diagnosis; Predictive Maintenance; Smart Sensors; Energy Harvesting; Sustainable Infrastructure.

Expected Contributions

The Special Issue aims to:

Advance understanding of sound and vibration phenomena in renewable energy systems.
Promote intelligent diagnostic methodologies for predictive maintenance.
Encourage interdisciplinary research combining renewable energy, mechanics, acoustics, and artificial intelligence.
Provide practical solutions for enhancing the reliability and safety of renewable energy infrastructures.
Foster collaboration between academia, research institutions, and industry.

Editor-in-Chief

Prof. Jun Yang

Institute of Acoustics, Chinese Academy of Sciences, China

ISSN

1541-0161 (Print)

2693-1443 (Online)

Publication Frequency

Bi-monthly

Indexing

Web of Science Coverage

Emerging Sources Citation Index (IF 4.2, Q1)

Elsevier Solutions

Scopus (2025 CiteScore 2.0);

Portico, etc.

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.

more

Member of ASC

Volume Arrangement

Featured Articles

New scaling of critical damping and reduced frequency for mechanically excited systems

This paper introduces a universal framework for understanding the vibration responses of systems subjected to harmonic excitation. By examining a simplified cylinder-spring-damper model, the study refurbishes traditional scaling methods for the excitation frequency ratio and critical damping ratio. The findings indicate that in damped systems, the maximum amplitude of vibration does not align with the natural frequency. This observation leads to the introduction of a new scaling method for reduced frequency. This new approach aligns resonance peaks at the new reduced velocity of 1.0 across different damping ratios, providing a consistent characterization of vibration behavior. A new critical damping ratio of 0.707 is identified for an excited system as opposed to the traditional damping ratio of 1.0 for an unexcited system. Key properties such as maximum amplitude, phase lag, bandwidth, and quality factor are analyzed, demonstrating that the proposed reduced frequency and critical damping ratio effectively capture the dynamics of both damped and undamped excited systems. The findings offer significant insights for practical applications in engineering and various scientific fields.

Ultrasonic wave velocity as a universal metric for defect detection in timber structures: A case study on Japanese cedar wood (Cryptomeria japonica)

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

Vehicle structural road noise prediction based on an improved Long Short-Term Memory method

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.