Search Results (1,492)

To address the issue of hysteresis nonlinearity adversely affecting the tracking accuracy of piezoelectric actuators, an improved particle swarm optimization (PSO) algorithm is proposed to improve the accuracy of hysteresis model parameter identification. Additionally, a discrete-time linear quadratic optimal tracking (DLQT) control strategy incorporating hysteresis compensation is developed to improve tracking performance. This study employs the Hammerstein model to characterize the nonlinear hysteresis behavior of piezoelectric actuators. Regarding parameter identification, the conventional PSO algorithm tends to suffer from premature convergence and being trapped in local optima. To address this, a cross-variation mechanism is introduced to enhance population diversity and improve global search ability. Furthermore, adaptive and dynamically adjustable inertia weights are designed based on evolutionary factors to balance exploration and exploitation, thereby enhancing convergence and identification accuracy. The inertia weights and learning factors are adaptively adjusted based on the evolutionary factor to balance local and global search capabilities and accelerate convergence. Benchmark function tests and model identification experiments demonstrate the improved algorithm’s superior convergence speed and accuracy. In terms of control strategy, a hysteresis compensator based on an asymmetric hysteresis model is designed to improve system linearity. To address the issues of incomplete hysteresis compensation and low tracking accuracy, a DLQT controller is developed based on hysteresis compensation. Hardware-in-the-loop tracking control experiments using single and composite frequency reference signals show that the relative error is below 3.3% in the no-load case and below 4.5% in the loaded case. Compared with the baseline method, the proposed control strategy achieves lower root-mean-square error and maximum steady-state error, demonstrating its effectiveness. Full article

As an important technology to enhance nutrient use efficiency and reduce agricultural non-point source pollution, controlled-release fertilizers (CRFs) have been widely applied in modern agriculture. However, during packaging, transportation, and field application, CRF particles are prone to mechanical impacts, which can lead to particle fragmentation and damage to the controlled-release coating. This compromises the release kinetics, increases nutrient loss risk, and ultimately exacerbates environmental issues such as eutrophication. Currently, studies on the impact-induced fragmentation behavior of CRF particles remain limited, and there is an urgent need to investigate their fragmentation susceptibility mechanisms from the perspective of internal stress evolution. In this study, the mechanical properties of CRF particles were first experimentally determined to obtain essential parameters. A two-layer finite element model representing the coating and core structure of the particles was then constructed, and a fragmentation susceptibility index was proposed as the key evaluation criterion. The index, defined as the ratio of fractured volume to peak impact energy, reflects the efficiency of energy conversion at the critical moment of particle rupture (1–5). An explicit dynamic simulation framework incorporating multiple influencing factors—equivalent diameter, sphericity, impact material, velocity, and angle—was developed to analyze fragmentation behavior from the perspective of energy transformation. Based on the observed effects of these variables on fragmentation susceptibility, three regression models were developed using response surface methodology to quantitatively predict fragmentation susceptibility. Comparative analysis between the simulation and experimental results showed a fragmentation rate error range of 0–11.47%. The findings reveal the relationships between particle fragmentation modes and energy responses under various impact conditions. This research provides theoretical insights and technical guidance for optimizing the mechanical stability of CRFs and developing environmentally friendly fertilization strategies. Full article

Quantum walks are, at present, an active field of study in mathematics, with important applications in quantum information and statistical physics. In this paper, we determine the influence of basic chaotic features on the walker behavior. For this purpose, we consider an extremely simple model consisting of alternating one-dimensional walks along the two spatial coordinates in bidimensional closed domains (hard wall billiards). The chaotic or regular behavior induced by the boundary shape in the deterministic classical motion translates into chaotic signatures for the quantized problem, resulting in sharp differences in the spectral statistics and morphology of the eigenfunctions of the quantum walker. Indeed, we found, for the Bunimovich stadium—a chaotic billiard—level statistics described by a Brody distribution with parameter $\delta \simeq 0.1$. This indicates a weak level repulsion, and also enhanced eigenfunction localization, with an average participation ratio $(\mathrm{PR})\simeq 1150$ compared to the rectangular billiard (regular) case, where the average $\mathrm{PR}\simeq 1500$. Furthermore, scarring on unstable periodic orbits is observed. The fact that our simple model exhibits such key signatures of quantum chaos, e.g., non-Poissonian level statistics and scarring, that are sensitive to the underlying classical dynamics in the free particle billiard system is utterly surprising, especially when taking into account that quantum walks are diffusive models, which are not direct quantizations of a Hamiltonian. Full article

This study utilizes the Standard k-ε turbulence model and ANSYS CFX software to tackle silt erosion in the top cover clearances of guide vane of the Francis turbine at Genda Power Station (Minjiang River Basin section, 103°17′ E and 31°06′ N) under sediment-laden flow conditions. A numerical simulation of a solid–liquid two-phase flow along the whole flow route was performed under rated operating circumstances to examine the impact of varying guide vane end clearance heights (0.3 mm, 0.5 mm, and 1.0 mm) on internal flow patterns and sediment erosion characteristics. The simulation parameters employed an average sediment concentration of 2.9 kg/m³ and a median particle size of 0.058 mm, indicative of the flood season. The findings demonstrate that augmenting the clearance height intensifies leaky flow and secondary flow, resulting in a 0.49% reduction in efficiency. As the gap expanded from 0.3 mm to 1.0 mm, the leakage flow velocity notably increased to 40 m/s, exacerbating flow separation, enlarging the vortex structures in the vaneless space, and augmenting the sediment velocity gradient and concentration, consequently heightening the risk of erosion. An experimental setup was devised based on the numerical results, and the dynamic resemblance between the constructed test section and the prototype turbine was confirmed for flow velocity, concentration, and Reynolds number. Tests on sediment erosion revealed that the erosion resistance of the anti-sediment erosion material 04Cr13Ni5Mo markedly exceeded that of the base cast steel, especially in high-velocity areas. This study delivers a systematic, quantitative analysis of clearance effects on flow and erosion, along with an experimental wear model specifically for the Gengda Power Station, thereby providing direct theoretical support and engineering guidance for its wear protection strategy and maintenance planning. Full article

Recycling of lithium-ion batteries (LIBs) requires efficient separation of active material from current collectors to enable high-quality recovery of both the coating and the metal foil. In this study, a water-based delamination process for anode foils was systematically investigated under variations in temperature, particle size, ultrasonic power, and prior mechanical stressing of the particles. Mechanically cut and pre-folded foil pieces were treated in a batch setup at different temperatures (room temperature to 100 °C) and ultrasonic power levels (50 and 100%). Results show that higher temperatures strongly promote delamination, with 100% removal of the active layer achieved on the smooth foil side at 80 °C without ultrasonic treatment. Ultrasonic treatment at moderate power (50%) yielded greater delamination than at full power (100%), likely due to more effective cavitation dynamics at moderate intensity. Mechanical pre-stressing by folding significantly reduced delamination, with three folds effectively preventing separation. In comparison, mechanically comminuted particles from a granulator achieved similar delamination to three-folded particles after 5 min treatment, and higher delamination after 30 min. These findings highlight the importance of process parameters in achieving efficient aqueous delamination, providing insights for scaling low-energy recycling processes for LIB production scrap. Full article

The paper presents experimental results for a modified pulsed plasma thruster (PPT) with solid propellant, using a coaxial anode–cathode design. Graphite from pencil leads served as propellant, and a tungsten trigger electrode was tested to reduce carbonization effects. Experiments were performed in a vacuum chamber at 0.001 Pa, employing diagnostics such as discharge current/voltage recording, power measurement, ballistic pendulum, time-of-flight (TOF) method, and a Faraday cup. Current and voltage waveforms matched an oscillatory RLC circuit with variable plasma channel resistance. Key discharge parameters were measured, including current pulse duration/amplitude and plasma channel formation/decay dynamics. Impulse bit values, obtained with a ballistic pendulum, reached up to 8.5 μN·s. Increasing trigger capacitor capacitance reduced thrust due to unstable “pre-plasma” formation and partial pre-discharge energy loss. Using TOF and Faraday cup diagnostics, plasma front velocity, ion current amplitude, current density, and ion concentration were determined. Tungsten electrodes produced lower charged particle concentrations than graphite but offered better adhesion resistance, minimal carbonization, and stable long-term performance. The findings support optimizing trigger electrode materials and PPT operating modes to extend lifetime and stabilize thrust output. 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

To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency. Full article

Recent advances in cardiovascular imaging have revolutionized the assessment and management of abdominal aortic aneurysm (AAA) through the integration of sophisticated haemodynamic biomarkers. This comprehensive review evaluates the clinical utility and mechanistic significance of multiple biomarkers in AAA pathogenesis, progression, and treatment outcomes. Advanced cardiac imaging modalities, including four-dimensional magnetic resonance imaging (4D MRI), computational fluid dynamics (CFD), and specialized echocardiography, enable precise quantification of critical haemodynamic parameters. Wall shear stress (WSS) emerges as a fundamental biomarker, with values below 0.4 Pa indicating pathological conditions and increased risk for aneurysm progression. Time-averaged wall shear stress (TAWSS), typically maintaining values above 1.5 Pa in healthy arterial segments, provides crucial information about sustained haemodynamic forces affecting the vessel wall. The oscillatory shear index (OSI), ranging from 0 (unidirectional flow) to 0.5 (purely oscillatory flow), quantifies directional changes in WSS during cardiac cycles. In AAA, elevated OSI values between 0.3 and 0.4 correlate with disturbed flow patterns and accelerated disease progression. The relative residence time (RRT), combining TAWSS and OSI, identifies regions prone to thrombosis, with values exceeding 2–3 Pa⁻¹ indicating increased risk. The endothelial cell activation potential (ECAP), calculated as OSI/TAWSS, serves as an integrated metric for endothelial dysfunction risk, with values above 0.2–0.3 Pa⁻¹ suggesting increased inflammatory activity. Additional biomarkers include the volumetric perivascular characterization index (VPCI), which assesses vessel wall inflammation through perivascular tissue analysis, and pulse wave velocity (PWV), measuring arterial stiffness. Central aortic systolic pressure and the aortic augmentation index provide essential information about cardiovascular load and arterial compliance. Novel parameters such as particle residence time, flow stagnation, and recirculation zones offer detailed insights into local haemodynamics and potential complications. Implementation challenges include the need for specialized equipment, standardized protocols, and expertise in data interpretation. However, the potential for improved patient outcomes through more precise risk stratification and personalized treatment planning justifies continued development and validation of these advanced assessment tools. Full article

To address the limited adaptability of traditional fixed-parameter strategies for hexapod robots operating in multi-material terrain environments, this study proposes a terrain-aware gait optimization method based on an improved particle swarm optimization algorithm that incorporates foot-end sinking perception. This method establishes a ground sinking detection mechanism based on foot-end position sensors, constructs a dynamic weight allocation strategy based on ground bearing capacity, and develops a Terrain-aware Ground Particle Swarm Optimization algorithm (TGPSO) that integrates Latin hypercube sampling, linearly decreasing inertia weights, and stagnation exploration mechanisms. Furthermore, it establishes a unified terrain-based reward function framework to achieve dynamic adjustment of weights for velocity, stability, and transportation efficiency. Simulink simulation verification demonstrates that TGPSO achieves superior optimization performance compared to traditional strategies across three typical terrain types, while exhibiting faster convergence speed and enhanced stability. The research findings provide theoretical foundations and technical support for intelligent motion control of hexapod robots across varying material properties, achieving targeted optimization of locomotion performance under diverse terrain conditions. Full article

Wet agglomeration is essential in heap leaching of minerals, as it improves permeability by forming agglomerates through capillary and viscous forces. The Discrete Element Method (DEM) has been used to model this phenomenon, enabling the detailed tracking of interactions between individual particles. This study employs DEM to analyze the effect of particle-size distribution (PSD) on agglomerate formation inside a rotating agglomeration drum. The DEM model was validated using geometry and parameters reported in the literature, which are based on experimental studies of agglomeration in rotating drums. Both wide and bimodal PSD cases were simulated. The results demonstrate that DEM simulations of drums with exclusively fine particles are prone to producing poorly defined macrostructures. In contrast, the presence of coarse particles promotes the formation of stable agglomerates with fine particles attached to them. Additionally, decreasing the maximum particle size increases the number of agglomerates and improves the homogeneity of the final PSD. These findings improve our understanding of wet agglomeration dynamics and provide practical criteria for optimizing feed design in mineral-processing applications. Full article

Drilling fluid loss is a common and complex downhole problem that occurs during drilling in deep fractured formations, which has a significant negative impact on the exploration and development of oil and gas resources. Establishing a drilling fluid loss model for the quantitative analysis of drilling fluid loss is the most effective method for the diagnosis of drilling fluid loss, which provides a favorable basis for the formulation of drilling fluid loss control measures, including the information on thief zone location, loss type, and the size of loss channels. The previous loss model assumes that the drilling fluid is driven by constant flow or pressure at the fracture inlet. However, drilling fluid loss is a complex physical process in the coupled wellbore circulation system. The lost drilling fluid is driven by dynamic bottomhole pressure (BHP) during the drilling process. The use of a single-phase model to describe drilling fluids ignores the influence of solid-phase particles in the drilling fluid system on its rheological properties. This paper aims to model drilling fluid loss in the coupled wellbore–-fracture system based on the two-phase flow model. It focuses on the effects of well depth, drilling pumping rate, drilling fluid density, viscosity, fracture geometric parameters, and their morphology on loss during the drilling fluid circulation process. Numerical discrete equations are derived using the finite volume method and the “upwind” scheme. The correctness of the model is verified by published literature data and experimental data. The results show that the loss model without considering the circulation of drilling fluid underestimates the extent of drilling fluid loss. The presence of annular pressure loss in the circulation of drilling fluid will lead to an increase in BHP, resulting in more serious loss. Full article

Driven by the growing demand for sustainable resource utilization, the recovery of valuable constituents from spent lithium-ion batteries (LIBs) has attracted considerable attention, whereas conventional recycling processes remain energy-intensive, inefficient, and environmentally detrimental. Herein, an efficient and environmentally benign separation strategy integrating dielectrophoresis (DEP) and magnetophoresis (MAP) is proposed for isolating the primary components of “black mass” from spent LIBs, i.e., lithium iron phosphate (LFP) and graphite microparticles. A coupled electric–magnetic–fluid dynamic model is established to predict particle motion behavior, and a custom-designed microparticle separator is developed for continuous LFP–graphite separation. Numerical simulations are performed to analyze microparticle trajectories under mutual effects of DEP and MAP and to evaluate the feasibility of binary separation. Structural optimization revealed that the optimal separator configuration comprised an electrode spacing of 2 mm and a ferromagnetic body length of 5 mm with 3 mm spacing. Additionally, a numerical study also found that an auxiliary flow velocity ratio of 3 resulted in the best particle focusing effect. Furthermore, the effects of key operational parameters, including electric and magnetic field strengths and flow velocity, on particle migration were systematically investigated. The findings revealed that these factors significantly enhanced the lateral migration disparity between LFP and graphite within the separation channel, thereby enabling complete separation of LFP particles with high purity and recovery under optimized conditions. Overall, this study provides a theoretical foundation for the development of high-performance and environmentally sustainable LIBs recovery technologies. Full article

This study explores the formulation of chitosan microparticles through ionic gelation and presents detailed physicochemical characterization, release studies, and the utility and potential uses for drug delivery. Three formulations were prepared under rate-controlled conditions (stirring at 800 rpm and pH maintained at 4.6) with and without stabilizers to examine the effects of formulation parameters on particle morphology and structural stability. To determine different structural and chemical characteristics, Attenuated Total Reflectance Fourier-Transform Infrared spectroscopy (ATR–FTIR), Scanning Electron Microscopy (SEM), and dynamic light scattering (DLS) were utilized, which confirmed that the particles formed and assessed size distribution and structural integrity. Atomic force microscopy (AFM) was used to quantify surface roughness and potential nanomechanical differences that may derive from the use of different modifiers. Coformulation of bovine serum albumin (BSA) permitted assessment of encapsulation efficiency and drug release capacity. Based on in vitro release evidence, the protein released at a different rate, and the dispersion of formulations under physiological conditions (PBS, pH 7.4, 37 °C) confirmed the differences in stability between formulations. The tunable physical characteristics, mild fabrication conditions, and controlled drug release demonstrated that the chitosan particles could have useful relevance as a substrate for localized drug delivery and as a bioactive scaffold for tissue regenerative purposes. Full article

Virtual power plants (VPPs) integrate distributed energy sources and demand-side resources, but their efficient intelligent resource decision-making faces challenges such as high-dimensional constraints, output volatility of renewable energy, and insufficient adaptability of traditional optimization algorithms. To address these issues, an innovative intelligent decision-making framework based on the Ant Colony Algorithm–Simulated Annealing (ACO-SA) is first proposed in this paper, aiming to realize intelligent collaborative decision-making for the economy and operational stability of VPP in complex scenarios. This framework combines the global path-searching capability of the Ant Colony Algorithm (ACO) with the probabilistic jumping characteristic of the Simulated Annealing Algorithm (SA) and designs a dynamic parameter collaborative adjustment mechanism, which effectively overcomes the defects of traditional algorithms such as slow convergence and easy trapping in local optimal solutions. Secondly, a resource intelligent decision-making cost model under the VPP framework is constructed. To verify algorithm performance, comparative experiments covering multiple scenarios (agricultural parks, industrial parks, and industrial parks with energy storage equipment) are designed and conducted. Finally, the simulation results show that compared with ACO, SA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), ACO-SA exhibits significant advantages in terms of scheduling cost and convergence speed; the average scheduling cost of ACO-SA is 2.31%, 0.23%, 3.57%, and 1.97% lower than that of GA, PSO, ACO, and SA, respectively, and it can maintain excellent stability even in high-dimensional constraint scenarios with energy storage systems. Full article