Search Results (2,404)

The article presents research on a wide frequency range piezo sensor applied to surfaces by touch. It details the design of the piezo sensor, its operating principles, and usage characteristics. Calculations of the main vibration forms and modes, modeling, and experimental verifications are provided. The objective of the research was to create a lightweight, ergonomic device that enables quick detection and testing of ultrasonic vibrations on objects (ultrasonic concentrators, their replaceable tips, concentrator mounting structures, device casings, etc.) with a brief touch—up to 1 s. After optimizing the design parameters and conducting tests, it was determined that the piezo sensor identifies vibrations in the range of 20–96 kHz, which is a commonly used range in ultrasonic vibration systems (UVS). A distinctive feature of the sensor is that in this frequency range, it does not generate amplitude peaks, and its structural elements do not enter into the resonances of lower modes (1–5). The piezo sensor is not intended to determine precise vibration amplitudes and forms. It is designed to quickly find the points of minimum and maximum vibrations in vibrating objects, where precise measurements will later be conducted. The conducted research will assist in the design and manufacturing of such devices. Full article

Research into floating photovoltaics (FPV) has seen a significant increase in recent years. Still, the observed outputs are poorly quantified, isolated, and occasionally contradictory, with reported cooling-induced efficiency increases varying widely across sources. To address the need for consensus in the field, a systematic literature review (SLR) and meta-analysis were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to provide a comprehensive overview of the current state-of-the-art in FPV systems. 3751 articles were identified through Boolean queries on three databases (Scopus, Web of Science, and Google Scholar). Using Python programming to ensure objectivity and replicability, the dataset was screened to 109 publications (subject to a manual, full-text review) relating strictly to modelling, simulation, and performance analysis of FPV systems with regard to the observed effect of reduced operating temperature. Focusing on these areas, this study provides a fundamental understanding of the temperature-based performance, as well as insights into the operation and simulation of FPV systems. Consistent temperature reductions were observed between ground-mounted and floating systems. Experimental data on FPV temperature were subject to a regression analysis, and the resulting equation was found to correspond well to a reported relation in the literature. The article concludes with a set of informed research directions to underpin the further development and implementation of FPV technology. Full article

The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency. Full article

This study investigates the hydrodynamics of the net system of the bottom-mounted aquaculture farms located in the Bohai Sea, addressing the growing demand for high-quality aquatic products and the limitations of coastal aquaculture. Based on the validation part, the established lumped-mass method integrated with the finite element method ABAQUS/AQUA was employed to evaluate the structural responses of the net system with three arrangement schemes under diverse environmental loads. The hydrodynamic loads on net twines are modeled with Morison formulae. With the motivation of investigating the trade-offs between volume expansions, load distributions, and structural reliabilities, Scheme 1 refers to the baseline design enclosing the basic aquaculture volume, while Scheme 2 targets to increase the aquaculture volume and utilization rate and Scheme 3 seeks to optimize the load distributions instead. The results demonstrate that Scheme 1 provides the optimal balance of structural safety and functional efficiency. Specifically, under survival conditions, Scheme 1 reduces peak bottom tension rope loads by 14% compared to Scheme 2 and limits maximum netting displacement to 4.0 m. It is 21.3% lower than Scheme 3, of which the displacement is 5.08 m. It has been confirmed that Scheme 1 effectively minimizes collision risks, whereas the other schemes exhibit severe collisions. Scheme 1 trades off maximum volume expansion for optimal load management, minimal deformation, and the highest overall structural reliability, making it the recommended design. These findings offer valuable insights for the design and optimization of net systems in offshore aquaculture structures serviced in comparable offshore regions. Full article

High-rise building facades offer an alternative site for installing photovoltaic panels, which are traditionally placed on rooftops. The unique spatial configuration of high-rise buildings, characterized by a small footprint area relative to their height, supports the application of vertical facades for this purpose. Photovoltaic panels installed in these areas not only generate electricity but also enhance the aesthetic dimension of the urban landscape. The proposed methodology uses the EnergyPlus software to simulate the electricity generation of photovoltaic panels mounted on the walls of high-rise buildings in the city of Kragujevac, Serbia. A technical, economic, and environmental analysis was conducted for two scenarios: (1) photovoltaic panels installed on two facade areas with the highest solar potential, and (2) photovoltaic panels installed on all four available facade areas. In Scenario 1, the annual reduction in electricity consumption, annual cost savings in electricity consumption, and investment payback period range from 13 to 38%, 11 to 31%, and 8.4 to 10.6 years, respectively. In Scenario 2, these values range from 23 to 58%, 18 to 47%, and 10.9 to 12.9 years, respectively. The results indicate that southeast and southwest facades consistently achieve higher levels of electricity generation, underscoring the importance of prioritizing high-performing orientations rather than maximizing overall surface coverage. The methodology is particularly efficient for analyzing the solar potential of numerous buildings with comparable shapes, which is a characteristic commonly found in Eastern European architecture from the late 20th century. The study demonstrates the applicability of the proposed methodology as a practical and adaptable tool for assessing early-stage solar potential and providing decision support in urban energy planning. The approach addresses the identified methodological gap by offering a low-cost, flexible framework for assessing solar potential across diverse urban contexts and building typologies, while significantly simplifying the modeling process. Full article

This research paper presents the results of an analysis conducted on a microstrip patch antenna designed to operate within the 1.559–1.591 GHz frequency band, which encompasses three major satellite constellations: GPS, Galileo and BeiDou. The objective of this study is to perform a comparative evaluation of the materials used in the antenna design, assess the geometric configuration and analyze the key performance parameters of the proposed microstrip patch antenna. Prior to the numerical modeling and simulation process, a preliminary assessment was conducted to evaluate how different substrate materials influence antenna efficiency. For instance, a comparison between FR-4 and RT Duroid 5880 dielectric substrates revealed signal attenuation differences of approximately −1 dB at the target frequency. The numerical simulations were carried out using Ansys HFSS design. The antenna was mounted on a dielectric substrate, which was also mounted on a ground plane. The microstrip antenna was fed using a coaxial cable at a single point, strategically positioned to achieve circular polarization within the operating frequency band. The aim of this study is to design and analyze a microstrip antenna that operates within the previously specified frequency range, ensuring optimal impedance matching of 50 Ω with a return loss of S₁₁ < −10 dB at the operating frequency (with these parameters also contributing to the definition of the antenna’s operational bandwidth). Furthermore, the antenna is required to provide a gain greater than 3 dB for integration into GNSS’ receivers and to achieve an Axial Ratio value below 3 dB in order to ensure circular polarization, thereby facilitating the antenna’s integration into GNSSs. Full article

The accelerating pace of urbanization has significantly complicated traffic management systems, leading to mounting challenges, such as persistent congestion, increased travel delays, and heightened environmental impacts. In response to these challenges, this study presents a novel deep learning framework designed to enhance short-term traffic flow prediction and support intelligent transportation systems within the context of smart cities. The proposed model integrates Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks, augmented by an attention mechanism that dynamically emphasizes relevant temporal patterns. The model was rigorously evaluated using the publicly available datasets and demonstrated substantial improvements over current state-of-the-art methods. Specifically, the proposed framework achieves a 3.75% reduction in the Mean Absolute Error (MAE), a 2.00% reduction in the Root Mean Squared Error (RMSE), and a 4.17% reduction in the Mean Absolute Percentage Error (MAPE) compared to the baseline models. The enhanced predictive accuracy and computational efficiency offer significant benefits for intelligent traffic control, dynamic route planning, and proactive congestion management, thereby contributing to the development of more sustainable and efficient urban mobility systems. Full article

This paper evaluates a novel series-type suspension mount designed for electric vehicles (EVs), in which the spring and damper are arranged in series rather than in a conventional parallel configuration. This structurally simple yet innovative design avoids the need for additional mechanical components, such as inerters or costly active devices, while effectively mitigating vibration. Comparative quarter-car simulations demonstrated that the series-type configuration provided a faster reduction in transmissibility across the analyzed frequency range, highlighting its superior isolation capability compared to conventional mounts. An extended series-type model was also investigated by incorporating auxiliary sub-mount elements to assess the parametric effects. The results showed that damping variations had a limited influence, whereas the sub-mount stiffness played a decisive role in shaping the transmissibility curves and generating the secondary resonance behavior. To validate the concept experimentally, a prototype consisting of four coil springs and a vibration isolation pad was prepared and tested using impact-hammer excitation. The measured transmissibility confirmed improved vibration isolation up to 100 Hz under the given specimen conditions, with resonance features attributable to the inherent stiffness of the isolation pad. Overall, the findings verified that a simple series-type mount can provide efficient and practical vibration isolation tailored to EV applications. Full article

This article presents the development of a new pneumatic device for the precise application of mineral fertilizers, designed for use in precision agriculture systems involving farming robots. The proposed device is mounted on an autonomous agricultural platform and utilizes a machine vision system to determine plant coordinates. Its operating principle is based on accumulating a single dose of fertilizer in a chamber and delivering it precisely to the plant’s root zone using a directed airflow. The study includes a theoretical investigation of fertilizer movement inside the applicator tube under the influence of airflow and rotational motion of the tube. A mathematical model has been developed to describe both the relative and translational motion of the fertilizer. The equations, which account for frictional forces, inertia, and air pressure, enable the determination of optimal structural and kinematic parameters of the device depending on operating conditions and the properties of the applied material. The use of numerical methods to solve the developed mathematical model allows for synchronization of the device’s operating time parameters with the movement of the agricultural robot along the crop rows. The obtained results and the developed device improve the accuracy and speed of fertilizer application, minimize fertilizer consumption, and reduce soil impact, making the proposed device a promising solution for precision agriculture. Full article

A straightforward ex vivo approach has been developed and refined to enable high-resolution imaging and quantitative assessment of motile cilia function in mouse airway epithelial tissue, allowing critical insights into cilia motility and cilia generated flow using different mouse models or following different sample treatments. In this method, freshly excised mouse trachea is cut longitudinally through the trachealis muscle which is then sandwiched between glass coverslips within a thin silicon gasket. By orienting the tissue along its longitudinal axis, the natural curling of the trachealis muscle helps maintain the sample in a configuration optimal for imaging along the full tracheal length. High-speed video microscopy, utilizing differential interference contrast (DIC) optics and a fast digital camera capturing at >200 frames per second is then used to record ciliary motion. This enables detailed measurement of both cilia beat frequency (CBF) and waveform characteristics. The application of 1 µm microspheres to the bathing media during imaging allows for additional analysis of fluid flow generated by ciliary activity. The entire procedure typically takes around 40 min to complete per animal: ~30 min for tissue harvest and sample mounting, then ~10 min for imaging samples and acquiring data. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

The operational risks of equipment in coalbed methane (CBM) gathering stations exhibit dynamic characteristics. To address this, a dynamic risk assessment method based on Dynamic Bayesian Networks (DBNs) is proposed for CBM station equipment. Additionally, a comprehensive safety management evaluation model is established for gathering station equipment. This approach enables accurate risk assessment and effective implementation of safety management in CBM gathering stations. This method primarily consists of three core components: risk factor identification, dynamic risk analysis, and comprehensive safety management evaluation. First, the Bow-tie model is applied to comprehensively identify risk factors associated with station equipment. Next, a DBN is constructed based on the identified risks, and Markov theory is employed to determine the state transition matrix. Finally, a Comprehensive Safety Management (CSM) evaluation model for gathering station equipment is established. The feasibility of the proposed method is validated through case study applications. The results indicate that during the operation of equipment at CBM gathering stations, priority should be given to strengthening maintenance for medium-hole and enhancing prevention and emergency measures for jet fires. Temperature-controlled spiral-wound heat exchangers, skid-mounted circulating pumps, and pipelines have been identified as critical factors affecting accident occurrence at CBM gathering stations. Enhanced daily inspection and maintenance of this equipment should be implemented. Furthermore, compared to other safety evaluation indicators, the Emergency Preparedness and Response indicator has the most significant impact on the operational safety of CBM gathering station equipment. It requires high-priority attention, thorough implementation of relevant measures, and continuous improvement through targeted actions. Full article

Basalt fiber-reinforced polymer (BFRP) composites are increasingly utilized in photovoltaic mounting systems due to their excellent mechanical properties and durability. Bolted connections, valued for their simplicity, ease of installation, and effective load transfer, are widely employed for joining composite components. An orthogonal experimental design was adopted to investigate the effects of key parameters—including bolt end distance, number of bolts, bolt material, bolt diameter, preload, and connection length—on the load-bearing performance of three bolted BFRP plate configurations: lap joint (DJ), single lap joint (DP), and double lap joint (SP). Test results showed that the DJ connection exhibited the highest average tensile load capacity, exceeding those of the SP and DP connections by 45.3% and 50.2%, respectively. This superiority is attributed to the DJ specimen’s longer effective shear length and greater number of load-bearing bolts. Conversely, the SP connection demonstrated the largest average peak displacement, with increases of 29.7% and 52.9% compared to the DP and DJ connections. The double-sided constraint in the SP configuration promotes more uniform preload distribution and enhances shear deformation capacity. Orthogonal sensitivity analysis further revealed that the number of bolts and preload magnitude significantly influenced the ultimate tensile load capacity across all connection types. Finally, a calculation model for the tensile load capacity of bolted BFRP connections was established, incorporating a friction decay coefficient (α) and shear strength (τ). This model yields calculated errors under 15% and is applicable to shear slip-dominated failure modes, thereby providing a parametric basis for optimizing the tensile design of bolted BFRP joints. Full article

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions. Full article

This study presents a flutter answer analysis of the Romanian IAR-99 HAWK advanced trainer aircraft equipped with multiple external store configurations. A high-fidelity finite element model (FEM) of the complete aircraft, including pylons and external stores, was coupled with a Doublet Lattice Method (DLM) aerodynamic model. The aeroelastic framework was validated against Ground Vibration Test (GVT) data to ensure structural accuracy. Four representative configurations were assessed: (A) RS-250 drop tanks on inboard pylons and PRN 16 × 57 unguided rocket launchers on outboard pylons; (B) four B-250 bombs; (C) eight B-100 bombs mounted on twin racks; and (D) a hybrid layout with B-100 bombs inboard and PRN 32 × 42 launchers outboard. Results show that spanwise distribution governs aeroelastic stability more strongly than total carried mass. Distributed stores lower wing-bending frequencies and densify the modal spectrum, producing critical pairs and subsonic crossings near M ≈ 0.82 at sea level, whereas compact heavy loads remain subsonic-stable. A launcher-specific modal family around ≈29.8 Hz is also identified in the hybrid layout. The validated FEM–DLM framework captures store-driven mode families (≈4–7 Hz) and provides actionable guidance for payload placement, certification, and modernization of the IAR-99 and similar platforms. Full article