Search Results (2,116)

In this paper, the natural frequencies of pure bending, axial–bending, and torsional-bending coupled modes of CFRP Anisogrid cylindrical shell-beams supporting non-structural masses and inertias at the boundaries are firstly analytically investigated and, secondly, verified by FEM. Indeed, the design of shell-beam elements in various engineering applications is driven by the minimum frequency value that is necessary to achieve in order not to compromise the proper functionality of the assembly for which these elements are designed. In turn, this minimum frequency depends on the geometry, mass, and dynamics of the main components of the assembly. A typical point in space applications is to control the lowest frequency of the spacecraft body, commonly supported by a shell structure, in order to avoid the occurrence of resonance issues that may be induced by dynamic loads during the launch phase. As a rule, to keep the lowest frequency sufficiently high, in conjunction with non-structural masses, means to increase the stiffness and the mass of the load-carrying structure and, ideally, to identify the most efficient solution. In order to effectively address this topic, the analytical models of the natural frequencies of Anisogrid cylindrical shell-beams are finally introduced into an optimization routine as constraints on the fundamental frequency. This approach allows us to readily explore the various Anisogrid configurations and find the best candidate solutions in the framework of preliminary design. Full article

The ultrasonic power supply constitutes the core component of an ultrasonic welding system, and its main function is to convert the industrial frequency electricity into resonant high-frequency electricity in order to achieve mechanical energy conversion. However, factors such as changes in ambient temperature or component aging may cause the resonant frequency of the transducer to drift, thus detuning the resonant system and seriously affecting system performance. Therefore, an ultrasonic welding system requires high-frequency tracking in real time. Traditional frequency tracking methods (such as acoustic tracking, PID control, etc.) have defects such as poor stability, narrow bandwidth, or cumbersome parameter setting, making it difficult to meet the demand for fast tracking. To address these problems, this study adopts a T-matching network and utilizes sliding mode control for frequency tracking. In order to solve the problems of slow convergence and obvious jitter in sliding mode control (SMC), a Hippopotamus Optimization (HO) algorithm is introduced to simulate hippopotamuses’ group behavior and predation mechanisms, thereby optimizing the control parameters. It is verified through simulation that the SMC algorithm optimized by the HO algorithm (HO-SMC) is able to suppress frequency drift more effectively and demonstrates the advantages of fast response, high accuracy, and strong robustness in the scenario of sudden load changes. Full article

The increasing integration of renewable energy sources, particularly photovoltaic (PV) systems, into power grids presents challenges in maintaining frequency stability due to the absence of traditional mechanical inertia. This paper proposes a hybrid control strategy combining Particle Swarm Optimization (PSO) and Reinforcement Learning (RL) to provide Adaptive Virtual Inertia Control for frequency stability in multi-microgrid PV systems. The proposed system dynamically adjusts virtual inertia and damping parameters in response to real-time grid conditions and frequency deviations. The PSO algorithm optimizes the base inertia and damping parameters offline, while the RL algorithm fine-tunes these parameters online by learning from the system’s performance. The adaptive control mechanism effectively mitigates frequency fluctuations and enhances grid synchronization, ensuring stable operation even under varying power generation and load conditions. The hybrid PSO–RL controller demonstrates a superior performance, maintaining a frequency close to nominal (50.02 Hz), with the fastest settling time (0.10 s), minimal RoCoF (0.2 Hz/s), and effectively zero steady-state error. Simulation results demonstrate the effectiveness of the hybrid control approach, showing fast and accurate frequency regulation with minimal power quality degradation. The system’s ability to adapt in real time provides a promising solution for next-generation smart grids that rely on renewable energy sources. Full article

Variations in the drive motor’s load inertia during wafer transfer robot arm motion critically degrade end-effector trajectory accuracy. To address this challenge, this study proposes an anti-disturbance control strategy integrating Radial Basis Function Neural Network (RBFNN) and event-triggered mechanisms. Firstly, dynamic simulations reveal that nonlinear load inertia growth increases joint reaction forces and diminishes trajectory precision. The RBFNN dynamically approximates system nonlinearities, while an adaptive law updates its weights online to compensate for load variations and external disturbances. Secondly, an event-triggered mechanism is introduced, updating the controller only when specific conditions are met, thereby reducing communication burden and actuator wear. Subsequently, Lyapunov stability analysis proves the closed-loop system is Uniformly Ultimately Bounded (UUB) and prevents Zeno behavior. Finally, simulations on a planar 2-DOF manipulator demonstrate significantly enhanced trajectory tracking accuracy under variable loads. Critically, the adaptive neural network control method reduces trajectory tracking error by 50% and decreases controller update frequency by 84.7%. This work thus provides both theoretical foundations and engineering references for high-precision wafer transfer robot control. Full article

With the development of urban infrastructure construction, while pedestrian bridges meet traffic functions the issue of their comfort has become a core consideration in structural design. This is because the long-span lightweight structures, with their large flexibility and low fundamental frequencies, are also vulnerable to human-induced vibrations. Pedestrian load modellings include the deterministic time-domain model, which is widely adopted in codes due to its simplicity, the random model that takes into account individual variability, and the frequency-domain model. The deterministic time-domain model has abundant parameter determination results and has become relatively mature, while the latter two, although more rigorous, have relatively lagging development. Numerous studies have shown that acceleration limits are the main indicators for comfort assessment. Vertical vibrations are controlled by amplitude constraints, while for the lateral vibrations the “lateral lock-in” that can cause dynamic instability needs to be evaluated with particular emphasis. When comfort exceeds an acceptable degree, a prevalent countermeasure is to attach a Tuned Mass Damper (TMD) or Multiple Tuned Mass Damper (MTMD) system to the structure—the latter demonstrates stronger robustness when dealing with random pedestrian loads. Full article

To address the collaborative demand for low-frequency vibration control and energy recovery, this paper proposes a dual-functional structure integrating low-frequency vibration isolation and broadband energy harvesting. The structure consists of two core components: one is a quasi-zero stiffness (QZS) vibration isolation module composed of a linkage-horizontal spring (negative stiffness) and a vertical spring; the other is an energy-harvesting component with an array of parameter-differentiated piezoelectric cantilever beams. Aiming at the conflict between the structural dynamic stiffness approaching zero and broadening the effective working range, this paper establishes a dual-objective optimization function based on the Pareto principle on the basis of static analysis and uses the grid search method combined with actual working conditions to determine the optimal parameter combination. By establishing a multi-degree-of-freedom electromechanical coupling model, the harmonic balance method is used to derive analytical solutions, which are then verified by numerical simulations. The influence laws of external excitations and system parameters on vibration isolation and energy-harvesting performance are quantitatively analyzed. The results show that the optimized structure has an initial vibration isolation frequency below 2 Hz, with a vibration isolation rate exceeding 60% in the 3 to 5 Hz ultra-low frequency range and a minimum transmissibility of the order of 10⁻² (vibration isolation rate > 98%). The parameter-differentiated piezoelectric array effectively broadens the energy-harvesting frequency band, which coincides with the vibration isolation range. Synergistic optimization of both performances can be achieved by adjusting system damping, parameters of piezoelectric vibrators, and load resistance. This study provides a theoretical reference for the integrated design of low-frequency vibration control and energy recovery, and its engineering implementation requires further experimental verification. Full article

The thermal, thermomechanical, and viscoelastic properties of continuous unidirectional (UD) glass fiber/high-density polyethylene (GF/HDPE) and ultra-high-molecular-weight polyethylene/high-density polyethylene (UHMWPE/HDPE) tapes are characterized in this paper in order to support their use in extreme environments. Unlike prior studies that focus on short-fiber composites or limited thermal conditions, this work examines continuous fiber architectures under five operational environments derived from Army Regulation 70-38, reflecting realistic defense-relevant extremes. Differential scanning calorimetry (DSC) was used to identify melting transitions for GF/HDPE and UHMWPE/HDPE, which guided the selection of test conditions for thermomechanical analysis (TMA) and dynamic mechanical analysis (DMA). TMA revealed anisotropic thermal expansion consistent with fiber orientation, while DMA, via strain sweep, temperature ramp, frequency sweep, and stress relaxation, quantified their temperature- and time-dependent viscoelastic behavior. The frequency-dependent storage modulus highlighted multiple resonant modes, and stress relaxation data were fitted with high accuracy (R² > 0.99) to viscoelastic models, yielding model parameters that can be used for predictive simulations of time-dependent material behavior. A comparative analysis between the two material systems showed that UHMWPE/HDPE offers enhanced unidirectional stiffness and better low-temperature performance. At the same time, GF/HDPE exhibits lower thermal expansion, better transverse stiffness, and greater stability at elevated temperatures. These differences highlight the impact of fiber type on thermal and mechanical responses, informing material selection for applications that require directional load-bearing or dimensional control under thermal cycling. By integrating thermal and viscoelastic characterization across realistic operational profiles, this study provides a foundational dataset for the application of continuous fiber thermoplastic tapes in structural components exposed to harsh thermal and mechanical conditions. Full article

Abrupt speed variations and motor start-ups have been pointed out as critical challenges in the framework of fault diagnostics in induction motor drives, namely inter-turn short circuit faults. Generally, abrupt accelerations influence the typical symptoms of the fault, and consequently, the fault detection becomes ambiguous, impacting prompt and effective decision-making. To overcome this issue, this study proposes an inter-turn short-circuit fault diagnostic technique for asymmetrical six-phase induction motor drives operating under both smooth and abrupt motor accelerations. A time–frequency domain spectrogram of the AC component extracted from the q-axis reference current signal serves as a reliable fault indicator. This technique stands out for the compromise between robustness and computational effort using only one control variable accessible in the model predictive control algorithm, thus discarding both voltage and current signals. Experimental tests involving various load torques and fault severities, in transient regimes, were performed to validate the proposed methodology’s effectiveness thoroughly. Full article

The paper presents an FPGA-based, hardware-accelerated IEC 61850-9-2 Sampled Measured Values (SMV) subscriber—termed the high-speed SMV subscriber (HS3)—by integrating real-time energy-quality (EQ) analytics directly into the subscriber pipeline while preserving a deterministic, microsecond-scale operation under high stream counts. Building on a prior hardware decoder that achieved sub-3 μs SMV parsing for up to 512 subscribed svIDs with modest logic utilization (<8%), the proposed design augments the pipeline with fixed-point RTL modules for single-bin DFT frequency estimation, windowed true-RMS computation, and per-sample active power evaluation, all operating in a streaming fashion with configurable windows and resolutions. A lightweight software layer performs only residual scalar combinations (e.g., apparent power, form factor) on pre-aggregated hardware outputs, thereby minimizing CPU load and memory traffic. The paper’s aim is to bridge the gap between software-centric analytics—common in toolkit-based deployments—and fixed-function commercial firmware, by delivering an open, modular architecture that co-locates SMV subscription and EQ pre-processing in the same hardware fabric. Implementation on an MPSoC platform demonstrates that integrating EQ analytics does not compromise the efficiency or accuracy of the primary decoding path and sustains the latency targets required for protection-and-control use cases, with accuracy consistent with offline references across representative test waveforms. In contrast to existing solutions that either compute PQ metrics post-capture in software or offer limited in-FPGA analytics, the main contributions lie in a cohesive, resource-efficient integration that exposes continuous, per-channel EQ metrics at microsecond granularity, together with an implementation-level characterization (latency, resource usage, and error against reference calculations) evidencing suitability for real-time substation automation. Full article

The accurate short-term forecasting (PV) of power is crucial for grid stability control, energy trading optimization, and renewable energy integration in smart grids. However, PV generation is extremely variable and non-linear due to environmental fluctuations, which challenge the conventional forecasting models. This study proposes a hybrid deep learning architecture, Wavelet Transform–Transformer–Temporal Convolutional Network–Efficient Channel Attention Network–Gated Recurrent Unit (WT–Transformer–TCN–ECANet–GRU), to capture the overall temporal complexity of PV data through integrating signal decomposition, global attention, local convolutional features, and temporal memory. The model begins by employing the Wavelet Transform (WT) to decompose the raw PV time series into multi-frequency components, thereby enhancing feature extraction and denoising. Long-term temporal dependencies are captured in a Transformer encoder, and a Temporal Convolutional Network (TCN) detects local features. Features are then adaptively recalibrated by an Efficient Channel Attention (ECANet) module and passed to a Gated Recurrent Unit (GRU) for sequence modeling. Multiscale learning, attention-driven robust filtering, and efficient encoding of temporality are enabled with the modular pipeline. We validate the model on a real-world, high-resolution dataset of a Moroccan university building comprising 95,885 five-min PV generation records. The model yielded the lowest error metrics among benchmark architectures with an MAE of 209.36, RMSE of 616.53, and an R² of 0.96884, outperforming LSTM, GRU, CNN-LSTM, and other hybrid deep learning models. These results suggest improved predictive accuracy and potential applicability for real-time grid operation integration, supporting applications such as energy dispatching, reserve management, and short-term load balancing. Full article

Harmful cyanobacterial blooms pose significant threats to lake ecosystems, and the stocking of filter-feeding fish has often been used for their control. However, filter-feeding fish like silver carp excrete feces that not only retain viable cyanobacterial cells but also increase nutrient loading to the sediment. Furthermore, the quantity and frequency of fecal input vary depending on the biomass of algae and fish and the stocking strategy. In this study, a two-by-two factorial microcosm experiment was carried out to investigate the effects of silver carp feces on P release in shallow lakes. Results showed that fecal input quantity was the key determinant of P release. The peak flux reached 8.82 mg m⁻² d⁻¹ in high input treatments, compared to 1.01 mg m⁻² d⁻¹ in low input treatments. Phased-input exacerbated these effects compared to single-input. The dominant mechanisms of sediment P release varied with input levels. Microbial reduction was strongly associated with P release at low fecal input, while high-input scenarios showed concurrent hypoxia, an increase in sediment pH (from 7.28 to 7.46), and competition for adsorption sites by dissolved organic matter (DOM up to 38.57 mg L⁻¹). These results indicate that stocking of filter-feeding fish for cyanobacterial bloom control substantially altered P flux dynamics, with high input treatments exhibiting fluxes from −6.02 to 8.82 mg m⁻² d⁻¹ compared to −0.007 to 0.33 mg m⁻² d⁻¹ in controls, depending on the patterns of fecal input. For the prevention and control of cyanobacterial blooms and to ensure the sustainability of lakes, the stocking of filter-feeding fish should be carried out before the outbreak of blooms to avoid the impact of large amounts of fish feces input on P release and water quality during the blooms. Full article

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

This paper deals with the design and the robustness analysis of a model predictive control (MPC) for the position tracking of primary flight movables driven by electro-mechanical actuators. This study is, in particular, focused on a rotary electro-mechanical actuator (EMA) by UMBRAGROUP, employing a patented mechanical transmission based on a differential ball-screw mechanism characterized by a huge gear ratio. To obtain a baseline reference, conventional PID regulators were initially optimized by using multi-objective cost functions based on tracking accuracy, load disturbance rejection, and power consumption. The position regulator was then replaced by an MPC regulator, designed to balance performance, computational resources, and safety constraints. A nonlinear physics-based simulation model of the EMA, entirely developed in the Matlab–Simulink environment and validated with experiments, was used to compare the two control strategies. The simulation results in both the time and frequency domains highlight that the MPC solution provides faster and more accurate position tracking, improved dynamic stiffness, and reduced power absorption. Finally, the robustness against model uncertainties of the MPC was addressed by imposing random and combined deviations of model parameters from the nominal values (via Monte Carlo analysis). The results demonstrate that the implementation of MPC control laws could enhance the stability and the reliability of EMAs, thus supporting their application for safety-critical flight control functions. Full article

Background/Objectives: Controlled release systems, such as polymeric microparticles (MPs), have emerged as a promising solution to extend the bioavailability and reduce dosing frequency for biologic drugs; however, the formulation of these systems to encapsulate highly sensitive, hydrophilic biologic drugs within hydrophobic polymers remains a nontrivial task. Although scalable manufacturing and FDA approval of single emulsion processes encapsulating small molecules has been achieved, scaling more complex double emulsion processes to encapsulate hydrophilic biologics remains more challenging. Methods: Here, we demonstrate that two hydrophilic, low-molecular-weight, recombinant chemokines, CCL22 and CCL2, can be encapsulated in poly(lactic-co-glycolic acid) (PLGA) MPs using a single emulsion method where the proteins are dissolved in an organic solvent during formulation. Results: As expected, we observed some differences in release kinetics from single emulsion MPs compared to double emulsion MPs, which traditionally have been used to encapsulate proteins. Single emulsion MPs exhibited a substantially reduced initial burst. Importantly, protein released from single emulsion CCL22-MPs also retained biological activity, as determined by a cell-based functional assay. Decreasing particle size or changing the polymer end group from PLGA-COOH to PLGA-OH increased the initial burst from single emulsion MPs, demonstrating tunability of release kinetics for protein-loaded, single emulsion MPs. Finally, to improve scalability and enable more precise control over MP formulations, the single emulsion process was adapted to a microfluidic, continuous manufacturing system, and the resulting MPs were evaluated similarly. Conclusions: Altogether, this study demonstrates the feasibility of using a single emulsion encapsulation method for at least some protein biologics. Full article

In this study, we propose a novel metaheuristic algorithm named Kangaroo Escape optimization Technique (KET), inspired by the survival-driven escape strategies of kangaroos in unpredictable environments. The algorithm integrates a chaotic logistic energy adaptation strategy to balance a two-phase exploration process—zigzag motion and long-jump escape—and an adaptive exploitation phase with local search guided by either nearby elite solutions or random peers. A unique decoy drop mechanism is introduced to prevent premature convergence and ensure dynamic diversity. KET is applied to optimize the parameters of a fractional-order Proportional Integral Derivative (PID) controller for Load Frequency Control (LFC) in interconnected power systems. The designed fractional-order PID controller-based KET optimization extends the conventional PID by introducing fractional calculus into the integral and derivative terms, allowing for more flexible and precise control dynamics. This added flexibility enables enhanced robustness and tuning capability, particularly useful in complex and uncertain systems such as modern power systems. Comparative results with existing state-of-the-art algorithms demonstrate the superior robustness, convergence speed, and control accuracy of the proposed approach under dynamic scenarios. The proposed KET-fractional order PID controller offers 29.6% greater robustness under worst-case conditions and 36% higher consistency across multiple runs compared to existing techniques. It achieves optimal performance faster than the Neural Network Algorithm (NNA), achieving its best Integral of Time Absolute Error (ITAE) value within the first 20 iterations, demonstrating its superior learning rate and early-stage search efficiency. In addition to LFC, the robustness and generality of the proposed KET were validated on a standard speed reducer design problem, demonstrating superior optimization performance and consistent convergence when compared to several recent metaheuristics. Full article