Search Results (4,558)

Aiming at addressing the problem whereby the traditional time-optimal trajectory planning based on the steady-state torque–speed characteristic cannot fully exploit the short-term dynamic output performance of the servo permanent magnet synchronous motor (SPMSM), a time-optimal trajectory planning method for the SPMSM based on the short-term dynamic feasible region constraint is proposed to effectively improve the response speed. Firstly, the dynamic trapezoidal domain operation boundary is obtained by analyzing the motor working point variation curve and considering factors such as the working temperature and trajectory control, which constitutes the torque–speed value and the dynamic constraint mechanism of trajectory planning. Secondly, based on the energy consumption model, the average thermal power is used to represent the torque overload limit condition, and a dynamic constraint method based on the short-term dynamic torque–speed operation boundary is proposed. Then, in order to reduce the computational load in the online millisecond-level response, a time-optimal trajectory optimization algorithm based on sequential least squares is proposed to calibrate the positioning time of the time-optimal trajectory under different working temperatures and angles. Finally, a simulation and experimental comparisons of the time-optimal trajectories under different angles and working temperatures are carried out to verify the effectiveness of the proposed method. Full article

Exhaust emission assessment of heavy-duty diesel engines is commonly based on complementary steady-state and transient procedures, represented by the World Harmonized Steady-State Cycle (WHSC) and the World Harmonized Transient Cycle (WHTC). However, under transient operation, tailpipe NO_x and CO₂ signals cannot be directly assigned to instantaneous engine operating states because the measured response is affected by transport delay, analyser dynamics, and signal dispersion within the measurement chain. This paper proposes a machine-learning-assisted dynamic synchronization framework for aligning engine operating signals with tailpipe emissions under transient conditions. The method uses actual engine torque as the primary dynamic reference and determines local effective alignment between emission readings and the engine operating states that generated them. The synchronized data are then evaluated using an XGBoost-based modelling approach to assess whether emission characteristics obtained from WHSC steady-state operation can be transferred to WHTC transient operation. The results show that the proposed synchronization improves the physical consistency of transient emission data and provides a more reliable basis for comparing stationary and dynamic test outcomes. The transferability analysis indicates good predictive consistency for CO₂, whereas NO_x shows only partial transferability, reflecting stronger transient sensitivity and more complex formation dynamics. The proposed framework supports intelligent emission-data preprocessing, data-driven interpretation of heavy-duty engine tests, and assessment of the representativeness of steady-state tests under transient operating conditions. Full article

Uncoordinated and instantaneous charging decisions made by electric vehicle (EV) drivers create bottlenecks in existing infrastructure, leading to inefficiencies and prolonged waiting times, and resource losses that challenge sustainable transportation systems. This study proposes a “scenario-based” optimization approach targeting the stochastic behaviors of independent EV drivers, incorporating individual risk-taking profiles and balking mechanisms to promote infrastructure sustainability. The proposed algorithm integrates a discrete-event simulation with a Genetic Algorithm (GA) as a decision support mechanism. The optimization focuses on a vehicle cohort entering the route once the system reaches a steady-state saturation point during peak evening hours. GA parameters are optimized using the Taguchi method to maximize robustness. The results demonstrate that, compared to the baseline scenario where drivers act individually, the proposed decision-making mechanism can achieve up to a 20% reduction in the total travel time of the optimized vehicle group. Overall, the proposed model offers a scalable framework for optimizing individual charging behaviors, thereby contributing to more predictable, resource-efficient, and sustainable management of electric vehicle charging infrastructures. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Deep and ultra-deep carbonate reservoirs commonly experience fracture closure and conductivity reduction under high-temperature and high-stress conditions. In this study, triaxial creep tests were conducted on unacid-etched and acid-etched carbonate cores under different stress levels to investigate their time-dependent deformation behavior and the influence of acid etching on rock rheology. The results indicate that carbonate rocks exhibit pronounced creep behavior, including instantaneous elastic deformation, primary creep, and steady-state creep. Acid etching significantly altered the creep characteristics and rheological parameters of carbonate rocks, leading to distinct time-dependent deformation responses compared with the unacid-etched core. The Burgers constitutive model was employed to characterize the creep behavior, and all fitting correlation coefficients exceeded 0.9. Finite element simulations based on the fitted parameters successfully reproduced the experimental creep curves, verifying the reliability of the constitutive model. This study provides a theoretical and numerical basis for evaluating the long-term deformation behavior of acid-etched carbonate rocks and its implications for fracture closure and conductivity evolution. Full article

The constitutive analysis model and hot processing map of the ZM6 alloy across various deformation conditions were investigated during hot compression experiments. True stress-strain curves within 300–450 °C and 0.0001–0.1 s⁻¹ were obtained from compression tests on a Gleeble-1500 platform. The results showed that higher strain rates (e.g., 0.1 s⁻¹) induced pronounced work hardening, whereas high temperatures (300–400 °C) combined with low strain rates (10⁻⁴ s⁻¹) promoted conditions conducive to dynamic recrystallization (DRX), leading to a softening tendency of steady-state flow stress. Additionally, a modified strain-compensated constitutive model was built for flow stress prediction. Material constants were plotted as fifth-order polynomial functions of strain (0.025–0.80) for precise stress predictions. The derived activation energy (Q = 182.38 kJ/mol) falls within the typical range for Mg-RE alloys. Leave-one-temperature-out cross-validation showed average AARE values of 7.2–9.8%, demonstrating the model’s interpolation capability and its sensitivity to extrapolation. Cross-validation within the training dataset showed reasonable consistency between experimental and predicted stresses (R > 0.997, AARE < 4.35%). Using the dynamic materials model, hot processing maps identified safe deformation zones and instability zones of the ZM6 alloy. Flow instability was observed at strain rates >0.01 s⁻¹, particularly at low temperatures (300–350 °C). Optimal processing windows appeared in high-energy dissipation (η > 30%) regions, e.g., 400–450 °C/10⁻⁴–10⁻³ s⁻¹. Optical microscopy confirmed that at high temperatures (≥400 °C) and low strain rates (≤0.001 s⁻¹), a uniform, fine-grained, fully recrystallized structure can be obtained, whereas low temperatures (350 °C) and high strain rates (0.1 s⁻¹) produce coarse elongated grains with limited DRX, consistent with the instability regime predicted by the processing maps. Under intermediate conditions (e.g., 400 °C, 0.01 s⁻¹), a bimodal grain distribution indicates incomplete recrystallization. Although EBSD analysis was not performed in this study, the optical microstructures directly validate the predicted safe and unstable windows. Together, all these findings provide preliminary model-based guidance for optimizing hot working parameters to balance microstructural stability and processing efficiency. Full article

Dense granular materials under low confining stress and low shear velocity—conditions relevant to low-pressure powder handling, near-surface transport, and the upper layers of stored bulk solids—remain insufficiently characterized at the microstructural level. We perform three-dimensional discrete element method (DEM) simulations of annular shear of monodisperse glass spheres at σ = 1 kPa and v = 0.01 m/s, corresponding to an inertial number I ≈ 1.06 × 10⁻³ at the quasi-static limit of the dense flow regime. The steady-state friction coefficient stabilizes at μ_ss ≈ 0.78, consistent with the quasi-static limit of the μ(I) framework. The solid volume fraction decreases monotonically from φ ≈ 0.50 at the base to φ ≈ 0.35 near the top, while the tangential velocity decays exponentially with depth (decay length δ_s ≈ 10 mm). Particle trajectory tracking reveals a sharp kinematic transition near z ≈ 5–6 mm separating a quasi-rigid basal layer (z ≲ 5 mm) from an upper shear-active zone (z ≳ 6 mm). The contact force distribution follows an exponential decay P($f$/$⟨f⟩$) ∝ exp(−β·$f$/$⟨f⟩$) with β ≈ 0.45, with strong force chains selectively concentrated in the upper zone. Together, these four microstructural descriptors co-locate within a single transition band, providing quantitative benchmarks for material characterization and constitutive modelling at the lower boundary of dense flow. Full article

This paper introduces an adaptive fixed-time position controller (AFxTPC) designed for the lifting axis servo mechanism of a computer numerical control (CNC) plasma machine. It integrates a permanent magnet synchronous motor, gearbox, and ball screw into a unified electromechanical model. The proposed AFxTPC combines a fixed-time terminal sliding surface function with adaptive fixed-time sliding mode control to achieve fixed-time convergence, precise tracking, and robustness in the presence of parameter uncertainties. A specially designed reaching law guarantees accurate trajectory tracking, while the fixed-time terminal sliding surface function effectively minimizes chattering near the sliding manifold. Importantly, a novel fixed-time nonlinear disturbance observer is developed to simultaneously estimate the unmeasured system states and lumped disturbances in real time within a guaranteed initial-state-independent settling time. These estimated values are explicitly fed back into controller for active disturbance compensation. The stability of the overall closed-loop system is rigorously established using Lyapunov stability theory. Simulation results demonstrate that the proposed observer-based controller achieves superior performance compared with conventional proportional–integral–derivative (PID) and standard sliding mode controllers. It exhibits zero steady-state error, reduced overshoot, minimal chattering, and strong robustness over a wide range of operating conditions. Full article

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

This study investigates numerically a Coupled Solar Ventilation Evaporative Cooling system for hot and arid climates. The system uses a solar wall chimney to produce natural ventilation and generate hot and dry airflow, which is then directed through a roof-mounted humid hay packed bed to enhance evaporative air conditioning. The resulting cold is transferred via a thermally conductive inner roof plate while a membrane condenser recovers moisture for reusing. A mathematical model was developed to describe heat and mass transfer in the hay packed bed, including solar chimney airflow, pressure drop and the evaporation energy balance. Parametric simulations were carried out for inlet air temperature of 40–60 °C, airflow rates of 0.25–0.45 m³/s, hay moisture contents of 0.006–0.014 kg/kg dry basis and air humidity ratio of 0.002–0.006 kg/kg dry air. Results show that evaporative cooling becomes effective only above certain inlet temperature. Increasing airflow from 0.25 to 0.45 m³/s reduced hay temperature from 30 to 26.8 °C when inlet air temperature exceeded 43.5 °C. Higher hay moisture content enhanced cooling performance, reaching about 26 °C, while higher inlet air humidity reduced evaporation and limited cooling. The operating maps obtained from the numerical simulations provide practical guidance for preliminary system sizing and for optimal operating parameters selection in solar-driven evaporative cooling systems. The mathematical model treats the solar chimney, the evaporative packed bed, the conditioned room and the membrane condenser within the same steady state calculation. The solar energy balance and the pressure balance are used to relate the inlet air temperature and the airflow rate to solar irradiance, ambient temperature and chimney geometry. The model also includes the heat transferred from the room through the roof plate, the sensible heat of the supplied water and the mass transfer and pressure drop effects of the membrane condenser. Full article

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

High-fidelity computational fluid dynamics (CFD) remains computationally expensive for steady aerodynamic prediction under multi-condition and variable-geometry configurations, which limits rapid design iteration. To address this issue, this study proposes a data-driven surrogate framework for aircraft surface flow-field prediction on irregular meshes. The framework combines a geometry-unification strategy for variable rudder-deflection configurations with KM-GAT, a hybrid neural architecture that integrates graph attention and KAN-based nonlinear feature transformation. Geometry unification maps the surface flow fields associated with different rudder-deflection states onto a common zero-deflection reference template, thereby establishing consistent mesh correspondence and fixed prediction locations across samples while retaining the rudder angle as an operating-condition variable. The KM-GAT model further combines topology-aware message passing with localized nonlinear refinement, while the Huber loss is adopted to improve training robustness for CFD-derived data. Experiments on the F-22 research model show that the proposed framework achieves lower prediction errors and more concentrated error distributions than baseline MLP and GNN-based models. Qualitative comparisons further indicate that KM-GAT better preserves localized high-gradient structures, including pressure transitions and vortex-dominated regions. These results suggest that the proposed framework provides an effective surrogate modeling strategy for variable-geometry aerodynamic flow field prediction. Full article

This study investigates the impact of internal muffler geometry on flow-related dissipation characteristics potentially relevant to acoustic behavior using steady-state Computational Fluid Dynamics (CFD) simulations. Four variants were analyzed: an empty tube, considered to be a baseline model, a three-chamber baffle system, a single spiral channel, and a complex multi-channel insert manufacturable only via advanced additive technologies. Simulations were conducted in SimScale using a compressible flow model with the k-ω SST turbulence formulation. Key outputs included static pressure distribution and turbulent kinetic energy (TKE), both of which were evaluated as qualitative surrogate indicators associated with flow-induced energy dissipation phenomena. The results indicate that geometries incorporating spiral features modify flow redistribution patterns, pressure gradients and localized turbulence intensity, suggesting potential applicability for future acoustic optimization studies. The study highlights how additive manufacturing enables the integration of geometrically complex internal structures otherwise unattainable through conventional methods. By comparing pressure drop and TKE patterns with internal design features, the research offers a preliminary CFD-based framework for geometry screening and conceptual evaluation of muffler insert designs for automotive exhaust systems. This approach provides computational support for rapid comparative assessment prior to experimental validation and detailed acoustic analysis. Full article

With the increasing scale of new energy grid integration, the risk of low-frequency oscillation in the power system has increased, which seriously affect system safety and stability. It is urgent to identify the risk of low-frequency oscillations through steady-state operating features. This article first analyzes the features that affect low-frequency oscillations and constructs a low-frequency oscillation dataset using transient simulations. Secondly, feature selection was performed using the random forest algorithm, and a low-frequency oscillation risk identification model for GA-CNN was proposed. Thirdly, by combining Pearson correlation coefficient and RF algorithm to eliminate redundant features and screen important features, a low-frequency oscillation frequency recognition model based on GBRT was proposed, and hyperparameter optimization was performed using grid search. Finally, the effectiveness of the proposed method was verified by ablation experiments and comparative experiments using low-frequency oscillation datasets under different operating conditions. Full article

This paper analyzes a single-server queueing system with infinite capacity, where arrivals follow a Markovian arrival process and service and repair times are modeled by phase-type distributions. The service mechanism is two-tier: every customer undergoes a mandatory primary service, after which an optional secondary service is available upon request. When the system is empty, the server initiates a closedown process before taking successive multiple vacations; upon return, the server goes through a setup process before beginning service again. Service can be interrupted by random breakdowns in either mode, triggering a phase-type repair. Matrix-analytic methods are used for the steady-state analysis, yielding the stability condition, stationary probability vectors, busy period analysis and key performance measures. A cost analysis framework is also developed. Numerical experiments validate the analytical results and illustrate the practical applicability of the model. Full article