Search Results (41)

Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid inversion of oceanic hydrological parameters in complex underwater environments. Based on the open-source VTUAD (Vessel Type Underwater Acoustic Data) dataset, the method first utilizes a fine-tuned Paraformer (a fast and accurate parallel transformer) model for precise classification of sound source targets. Then, using structural causal models (SCM) and potential outcome frameworks, causal embedding vectors with physical significance are constructed. Finally, a cross-modal Transformer network is employed to fuse acoustic features, sound source priors, and environmental variables, enabling inversion of temperature and salinity in the Georgia Strait of Canada. Experimental results show that the method achieves accuracies of 97.77% and 95.52% for temperature and salinity inversion tasks, respectively, significantly outperforming traditional methods. Additionally, with GPU acceleration, the inference speed is improved by over sixfold, aimed at enabling real-time Ocean Acoustic Tomography (OAT) on edge computing platforms as smart hardware, thereby validating the method’s practicality. By incorporating causal inference and cross-modal data fusion, this study not only enhances inversion accuracy and model interpretability but also provides new insights for real-time applications of OAT. Full article

Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability. Full article

Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption. This review systematically addresses these issues by evaluating the synthesis methods (ex situ vs. in situ), microstructural design strategies, and the role of magnetic particle alignment under varying curing conditions. Special attention is given to the influence of material composition—including matrix types, magnetic fillers, and additives—on the mechanical and magnetorheological behaviors. While the primary focus of this review is on MR foams, relevant studies on MR elastomers, which share fundamental principles, are also considered to provide a broader context. Recent advancements are also discussed, including the growing use of artificial intelligence (AI) to predict the rheological and magneto-mechanical behavior of MR materials, model complex device responses, and optimize material composition and processing conditions. AI applications in MR systems range from estimating shear stress, viscosity, and storage/loss moduli to analyzing nonlinear hysteresis, magnetostriction, and mixed-mode loading behavior. These data-driven approaches offer powerful new capabilities for material design and performance optimization, helping overcome long-standing limitations in conventional modeling techniques. Despite significant progress in MR foams, several challenges remain to be addressed, including achieving uniform particle dispersion, enhancing viscoelastic performance (storage modulus and MR effect), and improving durability under cyclic loading. Addressing these issues is essential for unlocking the full potential of MR foams in demanding applications where consistent performance, mechanical reliability, and long-term stability are crucial for safety, effectiveness, and operational longevity. By bridging experimental methods, theoretical modeling, and AI-driven design, this work identifies pathways toward enhancing the functionality and reliability of MR foams for applications in vibration damping, energy harvesting, biomedical devices, and soft robotics. Full article

Smart sensors and networks have spread worldwide over the past few decades. In the industry field, these concepts have found an increasing quantity of applications. The omnipresence of smart sensor networks and smart devices, especially in the industrial world, has contributed to the emergence of the concept of Industry 4.0. In a world where everything is interconnected, communication among smart devices is critical to technological development in the field of smart industry. To improve communication, many engineers and researchers implemented methods to standardize communication along the various levels of the ISO-OSI model, from hardware design to the implementation and standardization of different communication protocols. The objective of this paper is to study and implement an unconventional type of communication, exploiting acoustic wave propagation on metallic structures, starting from the state of the art, and highlighting the advantages and disadvantages found in existing literature, trying to overcome them and describing the progress beyond the state of the art. The proposed application for acoustic communication targets the field of smart industries, where implementing signal transmission via wireless or wired methods is challenging due to interference from the widespread presence of metallic structures. This study explores an innovative approach to acoustic communication, with a particular focus on the physical challenges related to acoustic wave propagation. Additionally, communication performance is examined in terms of noise rejection, analyzing the impact of injected acoustic noise on communication efficiency. Full article

The rapid growth of marine industries has emphasized the focus on environmental impacts for all industries, as well as the influence of key environmental parameters on, for instance, offshore wind or aquaculture performance, animal welfare and structural integrity of different constructions. Development of automatized sensors together with efficient communication and information systems will enhance surveillance and monitoring of environmental processes and impact. We have developed a modular Smart Ocean observatory, in this case connected to a large-scale marine aquaculture research facility. The first sensor rigs have been operational since May 2022, transmitting environmental data in near real-time. Key components are Acoustic Doppler Current Profilers (ADCPs) for measuring directional wave and current parameters, and CTDs for redundant measurement of depth, temperature, conductivity and oxygen. Communication is through 4G network or cable. However, a key purpose of the observatory is also to facilitate experiments with acoustic wireless underwater communication, which are ongoing. The aim is to expand the system(s) with demersal independent sensor nodes communicating through an “Internet of Underwater Things (IoUT)”, covering larger areas in the coastal zone, as well as open waters, of benefit to all ocean industries. The observatory also hosts experiments for sensor development, biofouling control and strategies for sensor self-validation and diagnostics. The close interactions between the experiments and the infrastructure development allow a holistic approach towards environmental monitoring across sectors and industries, plus to reduce the carbon footprint of ocean observation. This work is intended to lay a basis for sophisticated use of smart sensors with communication systems in long-term autonomous operation in remote as well as nearshore locations. Full article

Over the past decade, distributed acoustic sensing has been utilized for structural health monitoring in various applications, owing to its continuous measurement capability in both time and space and its ability to deliver extensive data on the conditions of large structures using just a single optical cable. This work aims to evaluate the performance of distributed acoustic sensing for monitoring a multilayer structure on a laboratory scale. The proposed structure comprises four layers: a medium-density fiberboard and three rigid polyurethane foam slabs. Three different damages were emulated in the structure: two in the first layer of rigid polyurethane foam and another in the medium-density fiberboard layer. The results include the detection of the mechanical wave, comparing the response with point sensors used for reference, and evaluating how the measured signal behaves in time and frequency in the face of different damages in the multilayer structure. The tests demonstrate that evaluating signals in both time and frequency domains presents different characteristics for each condition analyzed. The supervised support vector machine classifier was used to automate the classification of these damages, achieving an accuracy of 93%. The combination of distributed acoustic sensing with this learning algorithm creates the condition for developing a smart tool for monitoring multilayer structures. Full article

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. Full article

Acoustic metamaterials (AMs) composed of periodic artificial structures have extraordinary sound wave manipulation capabilities compared with traditional acoustic materials, and they have attracted widespread research attention. The sound insulation performance of thin-walled structures commonly used in engineering applications with restricted space, for example, vehicles’ body structures, and the latest studies on the sound insulation of thin-walled metamaterial structures, are comprehensively discussed in this paper. First, the definition and math law of sound insulation are introduced, alongside the primary methods of sound insulation testing of specimens. Secondly, the main sound insulation acoustic metamaterial structures are summarized and classified, including membrane-type, plate-type, and smart-material-type sound insulation metamaterials, boundaries, and temperature effects, as well as the sound insulation research on composite structures combined with metamaterial structures. Finally, the research status, challenges, and trends of sound insulation metamaterial structures are summarized. It was found that combining the advantages of metamaterial and various composite panel structures with optimization methods considering lightweight and proper wide frequency band single evaluator has the potential to improve the sound insulation performance of composite metamaterials in the full frequency range. Relative review results provide a comprehensive reference for the sound insulation metamaterial design and application. Full article

The transition to sustainable or green(er) cities requires the development and implementation of many innovative technologies. It is vital to ensure that these technologies are themselves as sustainable and green as possible. In this context, smart materials offer excellent prospects for application. They are capable of performing a number of tasks (e.g., repair, opening/closing, temperature measurement, storage and release of thermal energy) without embedded electronics or power supplies. In this short review paper, we present some of the most promising smart material-based technologies for sustainable or green(er) cities. We will briefly present the state-of-the-art in smart concrete for the structural health monitoring and self-healing of civil engineering structures, phase-change materials (PCM) for passive air-conditioning, shape-memory materials (SMA) for various green applications, and meta-surfaces for green acoustics. To better illustrate the potential of some of the solutions discussed in the paper, we present, where appropriate, our most recent experimental results (e.g., embedded SAW sensors for the Structural Health Monitoring of concrete structures). The main aim of this paper is to promote green solutions based on smart materials to engineers and scientists involved in R&D projects for green(er) cities. Full article

A novel deterministic method to harvest energy within a broadband frequency (0~25 kHz) from a mass-in-mass metamaterial is presented herein. Traditional metamaterials are composed of multiple materials (named as resonators and matrix) with different mechanical properties (e.g., stiffness, density). In this work, the stiffnesses of matrix materials are altered systematically to allow diversified property mismatches between the constituent components to introduce local resonance in the unit cell. While local resonance leverages wave energy passing through the acoustic metamaterials trapped within the relatively soft matrix as dynamic strain energy, a strategic and deterministic methodology is investigated to obtain a broadband local resonance frequency. The frequency band can then be utilized to harvest the trapped energy by embedding a smart material inside the matrix which is capable of electromechanical transduction (e.g., lead zirconate titanate). This concept has been proved numerically by harvesting energy at a broadband frequency with a power density of ~10 μW/in². Finally, an experimental study is performed to prove the hypothesis proposed in this article. Full article

Acoustic emission (AE) has received increased interest as a structural health monitoring (SHM) technique for various materials, including laminated polymer composites. Piezoelectric sensors, including PZT (piezoelectric ceramic) and PVDF (piezoelectric polymer), can monitor AE in materials. The thickness of the piezoelectric sensors (as low as 28 µm—PVDF) allows embedding the sensors within the laminated composite, creating a smart material. Incorporating piezoelectric sensors within composites has several benefits but presents numerous difficulties and challenges. This paper provides an overview of acoustic emission testing, concluding with a discussion on embedding piezoelectric AE sensors within fibre-polymer composites. Various aspects are covered, including the underlying AE principles in fibre-based composites, factors that influence the reliability and accuracy of AE measurements, methods to artificially induce acoustic emission, and the correlation between AE events and damage in polymer composites. Full article

The limited occupied space and various noise spectrum requires an adjustable sound absorber with a smart structure and tunable sound absorption performance. The hexagonal acoustic metamaterial cell of the multiple parallel-connection resonators with tunable perforating rate was proposed in this research, which consisted of six triangular cavities and six trapezium cavities, and the perforation rate of each cavity was adjustable by moving the sliding block along the slideway. The optimal geometric parameters were obtained by the joint optimization of the acoustic finite element simulation and cuckoo search algorithm, and the average sound absorption coefficients in the target frequency ranges of 650–1150 Hz, 700–1200 Hz and 700–1000 Hz were up to 0.8565, 0.8615 and 0.8807, respectively. The experimental sample was fabricated by the fused filament fabrication method, and its sound absorption coefficients were further detected by impedance tube detector. The consistency between simulation data and experimental data proved the accuracy of the acoustic finite element simulation model and the effectiveness of the joint optimization method. The tunable sound absorption performance, outstanding low-frequency noise reduction property, extensible outline structure and efficient space utilization were favorable to promote its practical applications in noise reduction. Full article

In recent years, Micro-Electro-Mechanical Systems (MEMS) technology has had an impressive impact in the field of acoustic transducers, allowing the development of smart, low-cost, and compact audio systems that are employed in a wide variety of highly topical applications (consumer devices, medical equipment, automotive systems, and many more). This review, besides analyzing the main integrated sound transduction principles typically exploited, surveys the current State-of-the-Art scenario, presenting the recent performance advances and trends of MEMS microphones and speakers. In addition, the interface Integrated Circuits (ICs) needed to properly read the sensed signals or, on the other hand, to drive the actuation structures are addressed with the aim of offering a complete overview of the currently adopted solutions. Full article

This paper presents results of the first phase of “Dielectric Elastomer Cooperative Microactuator Systems” (DECMAS), a project within the German Research Foundation Priority Program 2206, “Cooperative Multistable Multistage Microactuator Systems” (KOMMMA). The goal is the development of a soft cooperative microactuator system combining high flexibility with large-stroke/high-frequency actuation and self-sensing capabilities. The softness is due to a completely polymer-based approach using dielectric elastomer membrane structures and a specific silicone bias system designed to achieve large strokes. The approach thus avoids fluidic or pneumatic compo-nents, enabling, e.g., future smart textile applications with cooperative sensing, haptics, and even acoustic features. The paper introduces design concepts and a first soft, single-actuator demonstrator along with experimental characterization, before expanding it to a 3 × 1 system. This system is used to experimentally study coupling effects, supported by finite element and lumped parameter simulations, which represent the basis for future cooperative control methods. Finally, the paper also introduces a new methodology to fabricate metal-based electrodes of sub-micrometer thickness with high membrane-straining capability and extremely low resistance. These electrodes will enable further miniaturization towards future microscale applications. Full article

The shape memory polymer (SMP) is a new type of smart material that can produce a shape memory effect through the stimulation of the external environment. In this article, the viscoelastic constitutive theory of the shape memory polymer and the mechanism of the bidirectional memory effect of the shape memory polymer are described. A chiral poly cellular circular concave auxetic structure based on a shape memory polymer made of epoxy resin is designed. Two structural parameters, α and β, are defined, and the change rule of Poisson’s ratio under different structural parameters is verified by ABAQUS. Then, two elastic scaffolds are designed to assist a new type of cellular structure made of a shape memory polymer to autonomously adjust bidirectional memory under the stimulation of the external temperature, and two processes of bidirectional memory are simulated using ABAQUS. Finally, when a shape memory polymer structure implements the bidirectional deformation programming process, a conclusion is drawn that changing the ratio β of oblique ligament and ring radius has a better effect than changing the angle α of oblique ligament and horizontal in achieving the autonomously adjustable bidirectional memory effect of the composite structure. In summary, through the combination of the new cell and the bidirectional deformation principle, the autonomous bidirectional deformation of the new cell is achieved. The research can be used in reconfigurable structures, tuning symmetry, and chirality. The adjusted Poisson’s ratio achieved by the stimulation of the external environment can be used in active acoustic metamaterials, deployable devices, and biomedical devices. Meanwhile, this work provides a very meaningful reference for the potential application value of metamaterials. Full article