Search Results (137)

In this work, a partitioned coupling algorithm is developed by integrating the improved discrete velocity method (IDVM) with the lattice Boltzmann flux solver (LBFS) to address conjugate heat transfer (CHT) in microscale systems across all flow regimes. Specifically, the flow field is solved by the IDVM, generating a heat flux that acts as a Neumann boundary condition at the interface for the solid domain. Subsequently, the LBFS calculates the thermal distribution inside the solid, and the updated temperature at the interface is then applied to the fluid computations as a Dirichlet condition. The proposed framework effectively combines the strengths of the IDVM in modeling rarefied gas flows with the advantages of the LBFS in handling heat conduction in complex geometries. Crucially, the current approach implicitly captures temperature jump discontinuities at the conjugate boundary, bypassing the requirement for supplementary jump conditions. To evaluate its performance, several CHT test cases involving rarefied gas in microchannels were conducted. Computational evidence suggests that the scheme is robust across diverse flow regimes. Full article

Electromagnetic spectrum planning is a critical challenge in modern wireless communication systems, characterized by multiple conflicting objectives including spectrum utilization efficiency, interference minimization, and fairness among users. This paper proposes an Enhanced Discrete Multi-Objective Particle Swarm Optimization (EDMOPSO) algorithm specifically designed for spectrum assignment problems. The proposed method introduces a novel probabilistic discrete velocity update mechanism with adaptive dynamic bounds, an adaptive inertia weight strategy based on normalized population diversity, and an improved archiving technique with enhanced diversity preservation. To handle the discrete nature of spectrum allocation, we develop a binary encoding scheme combined with a problem-specific repair mechanism for constraint satisfaction. The algorithm is evaluated on both synthetic benchmark problems and real-world spectrum planning scenarios. Experimental results demonstrate that EDMOPSO achieves competitive performance advantages over seven established multi-objective evolutionary algorithms, with Hypervolume improvements of 18.7% and Inverted Generational Distance reductions of 23.4% compared to the second-best-performing algorithm. A comprehensive ablation study with 15 configurations validates the synergistic interaction between components. The proposed method provides an effective solution for macro-level periodic spectrum management in complex electromagnetic environments. Full article

For the Vacuum Induction Gas Atomization (VIGA) powder preparation process, a multi-scale coupled numerical simulation and experimental validation were employed to systematically reveal the influence mechanisms of process parameters on the primary atomization flow field structure, secondary atomization droplet breakup behavior, and powder particle size distribution Using Computational Fluid Dynamics (CFD) methods combined with the VOF (Volume of Fluid) multiphase flow model, the fragmentation morphology of the melt during primary atomization was simulated, capturing the dynamic characteristics of liquid film thinning and the reduction in initial droplet area. Concurrently, the DPM (Discrete Phase Model) coupled with the TAB (Taylor Analogy Breakup) model was applied to predict the droplet size distribution in secondary atomization. The results indicate that increasing atomization pressure (2.5–4.5 MPa) significantly enhances secondary fragmentation intensity, reducing the median particle size (D50) from 42.1 μm to 37.5 μm. Experimental studies on Ni-based superalloys, validated by laser particle size analysis, confirmed that higher atomization pressure improves gas velocity and gas–liquid energy conversion efficiency, optimizes turbulent flow structures, and refines powder particles. The study concludes that the multi-scale coupled model effectively predicts atomization dynamics. By optimizing atomization pressure, powder particle size can be significantly refined, providing a theoretical basis for process control of high-performance spherical powders used in additive manufacturing. Full article

In this paper, a radial basis function (RBF) neural network based trajectory generation strategy is proposed to solve the online rapid generation of initial reference trajectory for low-cost hypersonic glide vehicles (HGV) under initial state perturbation. Firstly, the feasible trajectories that constitute the sample sets are offline generated by pseudospectral method according to the possible distribution of heights and velocities. Then, the sample set is randomly divided into training subset and test subset, by which the RBF neural network is trained and verified. Moreover, the input of the RBF neural network is a vector comprised by height and velocity from the initial state, whereas the output is a discrete state-control sequence which represents the trajectory from the current state to the expected final state. The simulation results validate that the proposed method has high confidence and small errors, which can improve the on-line generation efficiency of the trajectory. Full article

To address the poor mechanical adaptability of conventional equipment to 40 cm narrow-row sesame cultivation and the high weeding resistance and energy consumption of traditional weeding tools, this study developed an integrated bionic weeding and plant protection vehicle. The vehicle features a modular structure capable of three-row weeding and four-row plant protection, coupled with an extended-range hybrid powertrain. Its parallel linkage design enables terrain adaptation, ensuring consistent weeding depth of 3–6 cm and stable spraying height. Combined with an adjustable spraying width and a “detection–feedback–adjustment” mechanism to prevent plant collisions, the vehicle is fully compatible with the agronomic requirements of narrow-row cultivation. Inspired by mole cricket forelegs, the vehicle’s bionic weeding wheel blade model incorporates quantified biological features: quadratically fitted claw toe contours (R² > 0.97), a toe base height-to-width ratio of 1:2, and a toe groove radius-to-toe height ratio of 1:1. This design achieves a reliable biological-to-engineering translation. EDEM-based Discrete Element Method (DEM) simulations confirm that the bionic wheel outperforms conventional designs: the average torque is 17.4% lower (7.75 vs. 9.38 N·m), the soil disturbance rate is 8.2 percentage points higher (95.2% vs. 87.0%), and soil particle motion is more ordered (average velocity: 0.52 vs. 0.58 m/s), effectively reducing energy waste and improving weeding efficiency. Full article

In robotic grinding of complex curved surfaces, the low stiffness of serial robots causes tool tip deflection and degrades surface quality. The axial symmetry of grinding discs introduces a free rotational parameter at each waypoint, converting a standard 6-DOF robot into a functionally redundant system. However, this redundancy has not been systematically exploited for stiffness optimization along the trajectory. This paper proposes a hierarchical dynamic programming framework to optimize the redundancy angle sequence over the entire grinding trajectory. A kinematic transformation parameterizes the flange target by the redundancy angle, enabling enumeration of feasible candidate configurations over a discretized grid. A composite stiffness index that accounts for the normal, feed, and cross-feed grinding force components is formulated at the contact point. Hierarchical constraint filtering removes configurations that violate posture, singularity, velocity, acceleration, and stiffness constraints. The Viterbi algorithm then recovers the minimum-cost path that balances stiffness performance and joint motion smoothness. Finally, a post-processing step based on a cubic smoothing spline generates $C^2$-continuous joint trajectories. Simulations on a UR5 robot grinding a curved surface evaluate the proposed framework against fixed-angle, greedy, and flange-stiffness baselines. The proposed method improves the mean composite stiffness by 31.7% and 17.9% over the fixed-angle and flange-stiffness baselines, respectively, and reduces the maximum joint jump by two orders of magnitude compared with the greedy strategy. Experimental validation on a UR5 robot confirms that the smoothed trajectory is accurately tracked while the stiffness threshold is preserved. A multi-trajectory analysis further shows that the stiffness threshold is maintained across all grinding trajectories. These results demonstrate the effectiveness of the proposed framework for redundancy optimization in robotic grinding with tool spin symmetry. Full article

In this article, we explore the possibility of using reinforcement learning to create convective flow approximation schemes that maintain accuracy and stability at high Courant-Friedrichs-Lewy (CFL) numbers in the finite-volume discretization of advection equations. Unlike most existing data-driven discretization methods, which primarily concentrate on spatial grid refinement, this work emphasizes increasing the allowable time step without compromising solution accuracy. This approach reduces the total number of time integration steps, thereby enabling faster computation. A neural network is used as a surrogate model for reconstructing the convective flow, which takes as input local information about the flow, scalars, and geometry and predicts scalar values at node points. Reinforcement learning is used for training and is formulated as a policy optimization problem, where the long-term reward is defined as the difference between the numerical and reference solutions over the entire simulation period. Both the genetic algorithm and the Deep Deterministic Policy Gradient (DDPG) method are investigated. The effectiveness of the approach is evaluated using a one-dimensional nonlinear advection problem with a constant velocity field. Despite the simplicity of the test case, the results demonstrate that the trained convective flux approximation scheme achieves accuracy comparable to or better than the classical second-order linear upwind (LUD) scheme, while operating at CFL numbers 2–50 times higher than the optimal CFL for LUD, thereby reducing the simulation time by the same factor. This allows for a wider range of stability and accuracy in the finite-volume method and the use of larger time steps without compromising the quality of the solution. The study is intentionally limited to a single spatial dimension and serves as a basic analysis of the method’s applicability. The results demonstrate that reinforcement learning can successfully find more convective flow approximation schemes that improve efficiency at high CFL numbers than conventional explicit second-order schemes, establishing a framework that is subsequently extended in our follow-up work to improve training methods and three-dimensional complex transport problems. The proposed method improves the spatial discretization of convective fluxes, which is independent of the choice of time integration scheme. Therefore, the neural reconstruction can in principle be used in both explicit and implicit finite-volume solvers. Full article

Obstacle-avoidance risk threshold control and global discrete keypoint re-entry are critical factors influencing the smooth dynamic obstacle avoidance of unmanned vessels. For underactuated USVs, which operate in planar motion with three degrees of freedom (surge, sway, and yaw) but only two independent control inputs (surge velocity and yaw rate), this paper designs a layered obstacle-avoidance strategy featuring adaptive global path re-entry points, combined with short- and long-term obstacle trajectory prediction and risk perception. This method employs an Interactive Multiple Model (IMM) integrating Constant Velocity (CV), Constant Acceleration (CA), and Constant Turn Rate and Acceleration (CTRA) models to perform long-term spatiotemporal trajectory prediction for dynamic obstacles, constructing a spatiotemporal risk cost map. Long-term dynamic obstacle-avoidance trajectory planning is achieved through optimized adaptive global trajectory re-entry points and an improved A* algorithm. This long-term avoidance trajectory replaces the global path from the avoidance start to the re-entry point, providing a smooth, continuous long-term avoidance prediction. To ensure real-time collision avoidance effectiveness, an improved Dynamic Window Approach (DWA) algorithm uses the long-term avoidance trajectory as a foundation. It integrates the IMM’s short-term spatiotemporal obstacle trajectory prediction, sampling in the velocity and steering angle space to generate short-term avoidance control commands. Finally, the long-term and short-term obstacle-avoidance planning are executed in a receding-horizon manner, where the local DWA planner updates control inputs over a short rolling window without solving a full constrained optimization problem. This establishes a hierarchical avoidance strategy: long-term prediction enables smooth avoidance, while short-term prediction enables real-time avoidance, ensuring the continuity and timeliness of dynamic obstacle avoidance. Simulation results demonstrate that compared with traditional A* planning, the proposed risk-aware A* reduces cumulative collision risk by 62% and increases the minimum obstacle clearance distance by over 32.1%, while maintaining acceptable path length growth. This approach effectively reduces collision risks during navigation, enhances path smoothness, and improves navigation safety. Full article

The traditional Discrete Element Method (DEM) can track the motion details of individual particles, but its computational cost becomes excessively high when simulating large-scale systems involving millions or even billions of particles. In this study, a coarse-grained DEM approach was employed to analyze the flow behavior of mixed particles in a coal powder silo. This method maintains reasonable simulation accuracy while effectively reducing the total number of computational particles and significantly improving computational efficiency. After conducting investigations on the mesh-to-particle size ratio and model validation, this paper focuses on examining the effects of coal particle size distribution and mixing ratio on the characteristics of particle motion. The results indicate that during the discharge process of mixed particles, the downward velocity of particles in the central axis region near the outlet is significantly higher than that in the wall region, exhibiting typical funnel flow characteristics. The particle size distribution has a notable impact on the particle descent velocity. The uniform distribution case shows the highest descent velocity, the linear distribution case the lowest, while the normal distribution case falls between the two. Notably, in the normal distribution case, the descent velocity in the central axis region is similar to that of the uniform distribution, while the descent velocity in the wall region approaches that of the linear distribution. This presents a combined characteristic of the two extreme distributions rather than a simple transitional state. In contrast, the particle mixing ratio has a relatively minor influence on the overall motion characteristics. The mass flow rate of particles and the cross-sectional velocity distribution remain largely consistent, with only slight differences observed in the velocity within the central axis region. Full article

Aiming at satisfying the micro-dynamic lubrication requirements of moving parts in spacecraft on-orbit operation, this paper proposes a micro-oil supply scheme based on a piezoelectric drive. The working mode and transient sound pressure characteristics of the micro-oil supply device were analyzed by the numerical simulation method, and the influence of driving voltage and oil dynamic viscosity on droplet growth and desorption behavior was investigated. The research results show the following: (1) The driving voltage is a key external control parameter that affects the droplet ejection characteristics. The increase of its amplitude will promote the droplet morphology to change from a stable and concentrated overall state to a dispersed state with multi-satellite droplets and easy necking fracture. At the same time, the droplet size is significantly reduced, the distribution tends to be discretized and the droplet velocity and droplet mass are simultaneously improved. Under the 220 V voltage condition, the droplet ejection velocity can reach 17.8 m/s, and the single ejection mass can reach 6.98 mg, which can realize the rapid and large-scale delivery of droplets. (2) Dynamic viscosity is the core intrinsic parameter to determine the droplet ejection characteristics. When the value of dynamic viscosity increases, the droplet morphology will change from the wire-like fracture characteristics of ‘spherical head + satellite droplet developed’ to the droplet-like structure with overall concentration and stable mechanical state. The average droplet size increased and the distribution was more aggregated. The droplet velocity and droplet mass decreased significantly. When the viscosity increased to 0.16 Pa·s, the droplet ejection velocity decreased to 1.14 m/s, and the single ejection mass decreased to 0.35 mg. Full article

While extensive research on laser powder bed fusion has focused on optimizing process parameters to improve the performance of manufactured parts, the critical role of effective spatter particle removal in mitigating defects during manufacturing has not received commensurate attention. To address these issues, this study investigates the influence of a key parameter in the atmosphere protection system, namely, airflow velocity, on part performance. Methodologically, a combined approach of numerical simulation and experimental methods was employed to examine in detail the effect of airflow velocity on spatter removal efficiency and its corresponding contribution to the enhancement of formed Inconel 718 part quality. First, Computational Fluid Dynamics–Discrete Phase Model simulations identified an optimal airflow velocity of 0.57 m/s. Subsequently, experimental observations using a high-speed camera system revealed that velocities below 0.6 m/s led to spatter redeposition, resulting in pore and defect formation, whereas velocities exceeding 0.6 m/s increased spatter size and reduced molten-pool stability. The simulation and experimental results are consistent, demonstrating that an appropriate airflow velocity can effectively suppress defects and thereby improve the quality of the fabricated components. This research provides a viable pathway for significantly enhancing the mechanical properties of laser powder bed-fused Inconel 718. Full article

Wakes generated by upstream turbines in an offshore wind farm severely reduce the efficiency and power output of downstream turbines. Wind farm layout optimization offers a way to alleviate these negative impacts, where the main challenge lies in accurate and efficient evaluation across a vast number of potential configurations. Analytical wake models are crucial tools for this optimization, owing to their superb ability to efficiently predict wake distributions. This paper evaluates and discusses recent advances and persistent challenges in analytical wake modelling for layout optimization of wind farms. While the Jensen model remains efficient for discrete searches, the models capturing radial velocity gradients have become a preferred choice for high-fidelity optimization designs. Advanced models show the transition to full wakes to cover near-wake characteristics and complex inflow conditions. Motion corrections and physically based superposition methods improve the performance evaluation of floating offshore wind farms. Multi-objective optimization frameworks balance energy production and fatigue life by the integration of turbulence modelling. However, the increasing scale of modern wind turbines, the dynamic complexity of floating offshore wind farms, the clustering, and the model validation of large-scale wind farms present significant challenges to the applicability of these models. This paper highlights these emerging limitations in optimization problems, clarifying that addressing the gaps in these specific areas is essential for the development of high-fidelity optimizations and the design of future large-scale offshore wind turbine clusters. Full article

In complex, semi-structured orchard environments, mobile inspection robots often suffer from excessive turning points, low search efficiency, limited trajectory-tracking accuracy, and poor adaptability to dynamic obstacles. To address these issues, this study proposes an integrated autonomous navigation method that employs an improved A* algorithm for global path planning, a Fuzzy-Weighted Dynamic Window Approach (FW-DWA) for local path optimization, and a model predictive control (MPC)-based trajectory-tracking controller. First, a dynamic heuristic-weight adjustment strategy is introduced into the conventional A* algorithm, in which a correction factor adaptively tunes the heuristic weight; a two-stage node optimization procedure then removes hazardous and redundant nodes to improve path smoothness and safety. Second, the FW-DWA, grounded in fuzzy control theory, uses goal distance and obstacle distance to update the weights of the heading, clearance, and velocity evaluation functions in real time, thereby enhancing obstacle avoidance in dynamic environments. Finally, a discrete kinematic model is established to design the MPC Controller, which achieves high-precision tracking through receding-horizon optimization and feedback correction. Experiments conducted in real orchards demonstrate that the proposed method reduces path length by 5.79%, shortens planning time by 3.64%, and increases the minimum safety distance by 50%. Comparative results further show that the MPC Controller attains a mean position error of 0.032 m and a mean heading error of 3.14°, clearly outperforming a conventional Proportional–Integral–Derivative (PID) controller. These findings provide an effective solution for reliable autonomous navigation of orchard inspection robots and offer a valuable reference for smart agricultural robotics applications. Full article

In this study, a calculation method for two-phase nonequilibrium flow in solid rocket motor nozzles is established, and an in-depth investigation into the nonequilibrium flow within the nozzle is conducted. Based on NEPE high-energy propellant, a simplified reaction mechanism model is established and validated using the full-component sensitivity analysis method for chemical nonequilibrium flow in the nozzle, consisting of 16 components and 22 steps. The nonequilibrium and frozen flow in the nozzle are simulated, and it is found that in nonequilibrium flow, the chemical reactions result in a 22.4% increase in the flow field temperature and an approximate 4.13% improvement in specific impulse. In addition, the impacts of different total pressure conditions on the nonequilibrium flow in the nozzle are studied, in which the increase in pressure enhances the overall temperature, but the change in velocity and Mach number are negligible. Finally, a discrete phase model is adopted in the nonequilibrium flow simulation to predict the evolution of aluminum oxide particles with different sizes within the nozzle. The results indicate that the presence of particles can enhance nozzle total thrust while reducing the specific impulse. As the particle size increases, both the nozzle thrust and specific impulse decrease, with the specific impulse being more significantly affected by particle size variations due to the variation in the gas-phase mass flow rate. Full article

Accurate analysis and forecasting of short-term heavy rainfall (hourly rainfall ≥ 20 mm) are crucial for extending warning, enabling targeted preventive measures, and supporting efficient resource allocation. In recent years, machine learning techniques combined with atmospheric physical variables have offered promising new approaches for analyzing and predicting and forecasting short-term heavy rainfall. However, these methods often lack transparency, which hinders the interpretation of key atmospheric physical variables that drive short-term heavy rainfall and their coupling mechanisms. To address this challenge, the present study integrates the interpretable SHAP (SHapley Additive exPlanations) framework with machine learning to examine potential relationships between widely used atmospheric physical variables and short-term heavy rainfall, thereby improving model interpretability. CatBoost models were constructed based on multiple feature-input strategies using 71 physical variables across five categories derived from ERA5 reanalysis data, and their performance was compared with two benchmark algorithms, XGBoost and LightGBM. The SHAP method was subsequently applied to quantify the contributions of individual features and their interaction effects on model predictions. The results indicate that (1) the CatBoost model, utilizing all 71 physical variables, outperforms other feature combinations, with an AUC of 0.933, and F1 score of 0.930, and a Recall of 0.954, significantly higher than the XGBoost and LightGBM models; (2) Shapley value analysis identified 500 hPa vertical velocity, the A-index, and precipitable water as the most influential features on model performance; (3) The predictive mechanism for short-term heavy rainfall is fundamentally bifurcated: negative instances are classified through the discrete main effects of individual features, whereas positive event detection necessitates a sophisticated coordination of intrinsic main effects and synergistic interactions. Among the feature categories, the horizontal and vertical wind fields, stability and energy indices, and humidity-related variables exhibited the highest contribution ratios, with wind field features demonstrating the strongest interaction effects. The results confirm that integrating atmospheric physical variables with the CatBoost ensemble learning approach significantly improves short-term heavy rainfall identification. Furthermore, incorporating the SHAP interpretability framework provides a theoretical foundation for elucidating the mechanisms of feature influence and optimizing model performance. Full article