This study examines turbulent natural convection in a differentially heated cavity containing a Cu-water nanofluid under an oblique magnetic field. The novelty lies in investigating the effects of turbulence, magnetic-field inclination and nanoparticle concentration over a wide range of governing parameters. The standard “ k-ε ” turbulence model was implemented and the problem was solved using the finite element method. Key parameters include Rayleigh number ( Ra = 10 7 –10 9 ), Hartmann number ( Ha = 0–6400), magnetic field inclination angle ( γ = 0−90°) and nanoparticle volume fraction ( φ = 2–6%). Results demonstrate that heat transfer intensifies with increasing Rayleigh number, with the Nusselt number rising by 97.5% from Ra = 10 7 to 10 9 but reducing by 57.2% at Ha = 6400. An inclination angle of γ = 60° was found to optimize heat transfer, resulting in an 11% increase in the Nusselt number compared to γ = 0° at Ha = 6400. Furthermore, increasing the nanoparticle concentration improved the thermal performance, with the Nusselt number increasing by 25.1% as φ rose from 0.02 to 0.06. Overall, these findings indicate that heat transfer can be effectively controlled by precisely adjusting the external magnetic field and nanoparticle loading.
Modern industrial technologies require ultrafine materials with particle sizes below several micrometers. However, conventional grinding mills have largely reached their grinding limit and are often unable to achieve the required fineness without excessive processing time and energy consumption. This limitation is mainly caused by the low intensity and insufficient energy of grinding media interactions with the processed material. Therefore, developing more efficient grinding equipment remains an important engineering challenge. This study proposes new approaches to grinding process organization and novel ball drum mill designs incorporating additional excitation elements to increase the velocity, frequency, and energy of grinding media impacts. Comparative experiments were conducted using a conventional ball drum mill and the proposed grinding devices under identical operating conditions. Grinding performance was evaluated in terms of limiting particle size, grinding time, and energy consumption. The proposed grinding devices produced particle sizes 20–30 times smaller, reduced grinding time by six to seven times, and decreased energy consumption by approximately 40% compared with a conventional ball drum mill. These results confirm the effectiveness of the proposed designs and demonstrate their potential for ultrafine grinding applications. Future work will focus on DEM-based process modeling, optimization of operating parameters, and energy efficiency.
Titanium alloys are vital in aerospace, biomedical, and energy sectors due to their high strength and corrosion resistance. However, their poor machinability and the environmental impacts of conventional coolants necessitate sustainable alternatives. This review critically evaluates the dry machining of titanium alloys through the triple-bottom-line (TBL) framework—addressing ecological, economic, and social dimensions. It examines the roles of advanced coatings, surface texturing, and solid lubricants in overcoming thermal and tribological barriers in fluid-free conditions. Furthermore, process-assisted techniques, including cryogenic tool treatment (CTT), ultrasonic vibration-assisted (UVAM), and laser-assisted machining (LAM), are analyzed regarding tool life and energy efficiency. Comparative assessments reveal that while dry machining eliminates coolant waste, hybrid systems like Minimum Quantity Lubrication (MQL) and cryogenic cooling often provide a superior sustainability balance, despite trade-offs like nanoparticle toxicity or cryogen energy costs. The review concludes that achieving truly sustainable titanium machining requires an integrated approach involving eco-efficient tool design, digital optimization, and rigorous life-cycle assessment (LCA) to align manufacturing performance with global sustainability goals.
The internal flow characteristics of a gas-liquid wall-attachment fluidic component in the small-offset-ratio regime were investigated using particle image velocimetry (PIV), with D/b = 0.2. A stable wall-attached jet and an upstream recirculation region were observed at flow rates of 3 to 5 m 3 /h. The influence of flow rate on the internal flow structure was analyzed, and a method for determining the wall-attachment point was proposed. To further understand the flow behavior, numerical simulations were performed using different turbulence and two-phase flow models. The results indicate that the SST turbulence model combined with a free-surface two-phase flow model can accurately reproduce the wall-attachment phenomenon and the associated pressure difference in gas-liquid flow. Based on the PIV measurements, a numerical model for the two-phase flow in the investigated small-offset-ratio fluidic component at D/b = 0.2 was established. Validation against experimental data shows that the model predicts the attachment offset distance within 5%. The proposed numerical approach provides a basis for predicting the main flow characteristics and wall-attachment behavior and for conducting subsequent parametric analyses of the investigated configuration.
Twin-blade planetary mixers are one of important production devices for energetic materials. In order to investigate the effects of different rotating modes on the mixing efficiency inside mixing vessel of the twin-blade planetary mixer, computational fluid dynamics (CFD) software ANSYS Fluent 14.5 was utilized for solving the governing equations. Two parameters (the trajectory and the instantaneous speed of blade tip) were selected for blades kinetic analysis, and four variables (dimensionless Helicity, relative pressure, strain rate, and flow number) for fluid field analysis. Mixing time experiments were conducted utilizing alternate transparent materials. The numerical predictions illustrated the experimental results well, and the counter-rotating mode was more efficient than the co-rotating mode for the mixer via providing stranger strain rate. The existing of hollow blade can improve dispersive mixing and distributive mixing, which in turn improves mixing efficiency.
To improve the accuracy and robustness of bearing fault diagnosis, this paper proposes a fault diagnosis method based on Fast Ensemble Empirical Mode Decomposition (FEEMD) and a multi-strategy improved Cuckoo Search algorithm (Improved Cuckoo Search, ICS) optimized Least Squares Support Vector Machine (LS-SVM). In the signal processing phase, FEEMD is employed to decompose the bearing vibration signal. The Pearson correlation coefficient method is then applied to remove irrelevant noise components and reconstruct the signal, thereby enhancing the effective fault features. Subsequently, multiscale permutation entropy (MPE) is extracted from the reconstructed signal to characterize its complexity and nonlinear dynamics, forming the fault feature vector. In the optimization phase, a multi-strategy improved Cuckoo Search algorithm (SC-ICS) integrating a sine–cosine update strategy and Cauchy mutation is proposed. This approach enhances the global search capability and population diversity, effectively avoiding premature convergence and local optima. The improved ICS is utilized to optimize the key parameters of the LS-SVM, thereby improving its classification performance. Finally, the proposed method is applied to bearing fault diagnosis experiments. The results demonstrate that the method achieves high diagnostic accuracy and robustness under complex operating conditions, outperforming conventional optimization algorithms and diagnostic models, which verifies its effectiveness in practical bearing fault diagnosis applications.
Torispherical heads are prone to buckling failure in the transition zone under internal pressure. Based on elastic-plastic theory, this study employs numerical analysis to investigate buckling instability, incorporating geometric nonlinearity, material nonlinearity, and initial geometric defects. The results show that the critical buckling load is highly sensitive to initial defects: a reduction in local shell thickness from 5 to 3 mm causes a 54.8% decrease in the critical buckling load. Over the studied thickness range, the critical buckling load increases approximately linearly with head thickness. To mitigate buckling, two improvement schemes were proposed and compared: longitudinal stiffening plates and a circumferential stiffening ring. The circumferential stiffening ring reduces the peak stress by 60.3%, whereas the longitudinal plates achieve only a 17.4% reduction. Thus, the ring significantly enhances the critical buckling load and effectively suppresses wrinkling in the transition zone. This study provides quantitative benchmarks for defect tolerance and design improvement of torispherical heads and similar thin-shell structures.
Servomechanisms in industry are complicated mechatronic systems. To acquire the desired performances of servomechanism, a holistic controller parameters design and optimization method is proposed by this paper. An accurate servomechanism model is established and the servomechanism performance analyses are investigated. Then, according to the operating requirements of servomechanism, design constraints for controller parameters are developed. Moreover, the instability problem induced by great variations of modeling elements is also considered and the robust stability is proposed to solve this problem. The nonlinear objective design is adopted to obtain the designed controller parameters. To counteract modeling errors and uncertainties, it is indispensable for the online optimization of controller parameters and a critical robust stability space is generated to ensure optimizing controller parameters online successfully. To validate the effectiveness and feasibility of this proposed method, an open servomechanism platform was set up. The experimental and simulated results show that the tracking error, peak error and performance indicators were reduced significantly with the optimized controller parameters under different working conditions and the desired performances can be obtained. Moreover, this method overcomes the drawbacks of conventional controller parameters optimization methods, and can satisfy different operating requirements.
Multi-AUV swarms in maritime ISR missions must achieve complete area coverage while performing angle-constrained, multi-round beacon inspections, which are structurally conflicting objectives. Standard MADDPG struggles with this coupling and scalability because its centralized Critic input grows with agent count, causing unstable training and poor transfer. This paper proposes Attention-MADDPG, a unified framework for scalable multi-AUV coverage and dynamic beacon inspection. A Dual-Mode Decision Architecture combines grid memory with the RL policy, switching between coverage exploration and geometric inspection. A multi-head self-attention Critic learns sparse inter-agent interactions, suppressing irrelevant peer information and reducing communication overhead. A bounded composite Grade function rewards coverage, inspection correctness, alignment, efficiency, and cooperative safety. Experiments show that Attention-MADDPG outperforms MATD3, MADDPG, IDDPG, FDSAC, and FTC + CBF in convergence and task Grade. A policy trained with N = 3 transfers zero-shot to N = 8, achieving 100% coverage, completing all 152 beacon inspections within 52.37 min, and recording zero cooperative failures. Tests with N in {5, 10, 15} show stable scalability and about 80% fewer active links than full-topology MADDPG at N = 15. Robustness tests maintain 100% inspection accuracy across eight angle offsets and unchanged performance under sensor noise and packet loss up to 20%.
This study employs Principal Component Analysis (PCA) for comprehensive performance evaluation and establishes computational models for each indicator by addressing the correlation among multiple self-propelled hard-hose traveler indicators. The parameter configuration included the sprinkler model, nozzle diameter, rotation angle, and adjacent path spacing. The comprehensive evaluation system encompassed the average application depth, uniformity of water distribution ( CU value), total energy consumption, and irrigation capacity. The average application depth was positively correlated with total energy consumption and negatively correlated with the CU value and irrigation capacity. The strongest correlation was observed between the average water application rate and the irrigation capacity. Two principal components with eigenvalues of 2.046 and 1.023 were selected, accounting for a cumulative contribution rate of 76.735%. The first principal component was primarily determined by the average application depth and irrigation capacity. In contrast, the second principal component was mainly influenced by the CU value and total energy consumption. The optimal parameter configuration scheme is determined based on the comprehensive scores, ranking, and test validation as a dual-sprinkler arrangement (30PY 2 impact sprinkler) with a nozzle diameter of 10.5 × 5.0 mm, a sprinkler rotation angle of α = 0°, β = 90°, γ = 90°, and an adjacent path spacing of 1.5 R. The study provides guidance for the operation and management of self-propelled hard hose traveler in the field.
Inerter is widely used in the field of vibration control. In order to study the influence of inerter on the vibration characteristics of the three-degree-of-freedom (3-DOF)single-layer vibration system (SLVS), this paper establishes and analyzes a novel dynamic model considering the translation and two-order torsion of the vibration system. Based on the derived dynamic equations of the 3-DOF SLVS incorporating an inerter, theoretical values of the three natural frequencies are obtained. This paper conducts simulation calculations and experimental verification. Simulation results yield natural frequency values with deviations within 2.5% of the theoretical values. Meanwhile, the experimental system for testing the natural frequency of zero-input response is established, and the natural frequency values are obtained through experimental tests, and the deviations from the theoretical values are within 5%. Furthermore, the accuracy of the new dynamic model is verified. And the influence law of inerter on the vibration characteristics of the 3-DOF SLVS is obtained.
This study investigates a hybrid water–air cooling system for Li-Ion batteries using Computational Fluid Dynamics (CFD) simulations performed in ANSYS Fluent. The proposed system utilizes a single radiator and pump to cool both the engine and battery, thereby simplifying the overall thermal management architecture. Water cooling with a mass flow rate of 0.0002 kg/s is employed and a variable-speed air-cooling fan is activated based on battery temperature to provide adaptive thermal control. The results demonstrated that both the maximum battery temperature ( T max ) and temperature difference (Δ T ) decreased with increasing water flow rate. At 80% depth of discharge (DOD), increasing the water flow rate from 8 × 10 −4 to 3 × 10 −3 kg/s reduced T max by 11.33 K and Δ T by 9.40 K, indicating significant improvements in heat removal and temperature uniformity and hybrid cooling system achieved a cooling efficiency of 5.08%, outperforming the 3.87% reported in previous studies. Overall, the system effectively reduced thermal stress, improved temperature distribution, and maintained battery temperatures within the desired operating range. These findings demonstrate the proposed hybrid cooling strategy as an efficient and practical thermal management solution for HEV lithium-ion batteries.
To address the issue of feature point localization errors in the automated assembly of high-voltage transmission tower cylinders, this paper proposes a relative pose estimation method based on outlier detection. The method first employs the PNP algorithm to compute an initial estimate of the relative pose between the camera and the tower cylinder. Subsequently, through reprojection error analysis, Z-score criteria are applied to detect and eliminate anomalous matching points, thereby constructing a highly reliable matching point set. Finally, PNP is re-solved based on the optimized point set to obtain precise relative pose estimation results. Experimental results demonstrate that this method effectively suppresses random errors caused by misdetected feature points, significantly improving the accuracy and stability of pose estimation. The mean angular error of the rotation matrix estimation around the X/Y axes does not exceed 3.07°, while the mean translation vector estimation error is below 3.10 mm. This research contributes to ensuring the precision and stability of automated assembly for high-voltage transmission tower cylinders.
Weld component durability is essential in industrial production processes, where unidentified defects can cause serious operational failures. Automated weld flaw identification through deep learning technologies provides a dependable solution that eliminates the need for human inspection. This research introduces a new deep learning framework that integrates AlexNet with Gated Recurrent Unit (GRU) Network and Greylag Goose Optimization (GGO) method to achieve effective hyperparameter tuning. The proposed framework tests its performance through the RIAWELC dataset which contains 24,407 weld radiograph images divided into four categories: porosity, crack, lack of penetration, and no defect. The weld images receive quality improvement through advanced preprocessing techniques, which use median filtering, Contrast Limited Adaptive Histogram Equalization, and data augmentation methods. AlexNet extracts discriminative spatial features from visual data while GRU learns the contextual relationships among these features. The GGO technique enables the suggested framework to achieve its optimal performance through the GGO method which optimizes critical hyperparameters such as learning rate, dropout, batch size, kernel size, and GRU hidden units. The experimental results demonstrate that the suggested framework outperforms all existing methods by achieving 99.3% accuracy in binary classification and 98.9% average accuracy in multi-class classification.
To enhance the efficiency and robustness of flexible polishing for aeroengine blades, this study proposes a coupling-effect-based optimization algorithm to determine optimal process parameter ranges. Using Central Composite Design (CCD), quadratic response surface models were developed to correlate surface roughness ( R a ) and residual stress with spindle speed ( n ), compression depth ( a p ), feed speed ( v w ), and abrasive mesh number ( M ). ANOVA confirmed the models’ statistical significance and superior goodness-of-fit. A Multi-Objective Genetic Algorithm (MOGA) was then employed to derive the Pareto optimal set, identifying parameters that satisfy engineering requirements ( R a < 0.4 μm) while minimizing residual stress. Based on response surface-based tolerance analysis, the optimal operational windows were established: spindle speed n ∈ [6773.6, 8000] r/min, compression depth a p ∈ [1.016, 1.6] mm, feed speed v w ∈ [100, 268.6356] mm/min, and abrasive mesh number M ∈ [496.4032, 800] #. Unlike conventional single-point estimates, this approach provides practical operational windows that significantly improve production efficiency and process robustness for flexible polishing applications.
We present a direct, optimization-based framework for tuning proportional–integral–derivative (PID) controllers on general nonlinear systems. The method treats the PID gains as decision variables in a finite-horizon optimal control problem and enforces the full closed-loop dynamics using multiple-shooting combined with collocation on finite elements. The derivative action is incorporated algebraically, eliminating auxiliary derivative states and avoiding local linearization or explicit decoupling. The resulting sparse nonlinear program supplies exact sensitivities with respect to the gains, enabling fast convergence with large-scale interior-point or SQP solvers. Case studies on an unstable first-order plant, an inverted pendulum on a moving cart, and a nonlinear quadruple (four-tank) system demonstrate accurate tracking and robustness to cross-couplings while using only PID structure. The approach is simple to implement, scalable, and compatible with standard MATLAB toolchains.
Sensorless control technology for switched reluctance motors (SRMs) is a critical research direction aimed at enhancing system reliability and reducing costs. This paper provides a systematic review of sensorless control strategies across different speed ranges. In the low-speed region, the focus is on pulse voltage injection methods, which extract rotor position information through high-frequency signal excitation, and unsaturated region flux linkage modeling approaches that estimate position by leveraging the linear relationship between flux linkage and phase current. In the medium-to-high-speed region, the review covers rotor-characteristic-position-based detection methods, position observer techniques, and the application of intelligent algorithms including neural networks and fuzzy logic in position estimation. Research indicates that while each method demonstrates strong adaptability under specific operating conditions, achieving high-precision estimation across the entire speed range and enhancing parameter robustness remain key challenges for future studies. Development trends suggest that deep integration of hybrid strategies and intelligent algorithms will further advance the performance of sensorless control systems.
This study optimizes fused deposition modeling (FDM) parameters to resolve the trade-off between carbon emission reduction and ultimate tensile strength (UTS). Using polylactic acid (PLA), a Taguchi L 16 orthogonal array was employed to evaluate five factors: layer height, nozzle temperature, bed temperature, printing speed, and infill rate. Analysis of variance (ANOVA) and response surface methodology (RSM) identified infill rate and layer height as the most significant factors. To address the inherent conflict between environmental impact and mechanical performance, gray relational analysis (GRA) was implemented for multi-objective optimization. The optimal configuration (0.15 mm layer height, 200 °C nozzle temperature, 70 °C bed temperature, 50 mm/s speed, and 30% infill rate) increased UTS by 18.69% relative to the minimum-emission setting, and reduced carbon emissions by 15.88% compared to the maximum-strength setting. This framework provides a critical reference for achieving low-carbon, high-efficiency additive manufacturing in industrial applications.
This study investigates how the interplay between fiber alignment and matrix ductility governs the tensile performance and failure modes of uniaxial fique-fiber composites. Two approaches were evaluated: using a flexible resin with higher failure strain and applying mechanical treatment to the fique fabrics. The baseline composite-manufactured with untreated fibers and a rigid polyester resin-exhibited premature failure driven by multiple matrix cracks. Replacing the resin with a more flexible matrix increased the tensile strength by 28% and suppressed matrix fragmentation, resulting in a single-fracture failure mode, as confirmed by computed tomography (CT). In contrast, mechanical treatment improved fiber alignment, raising both strength and stiffness by 76%. Although the treated composite still exhibited matrix fragmentation, it achieved a tensile strength of 111 MPa and an elastic modulus of 6.1 GPa, among the highest reported for commercially available fique fabrics. Additionally, in situ CT analyses during tensile loading revealed how matrix cracking governs the nonlinear mechanical response. These results demonstrate that controlling the failure mode and using standardized fique fabrics enables the development of natural-fiber composites with mechanical performance comparable to high-end commercial systems.
Vehicle axle vertical characteristics strongly affect ride comfort and handling, but the influence of individual components is not well quantified. It remains unclear to what extent individual components contribute to the stiffness, friction, and damping characteristics of a vehicle axle. This study aims to quantify the contribution of individual components to the axle characteristics, with particular emphasis on axle damping. To address this question, a MacPherson strut axle was tested on an axle test rig in 19 configurations. In addition to the intact axle, components were removed, deliberately degraded, or mechanically decoupled to prevent force transmission between selected components. The axle was excited quasi-statically and dynamically, using both single-sided excitation and harmonic in-phase excitation of both wheel carriers. For each axle configuration, stiffness, damping, and friction were evaluated. The results show that removing either the main springs or the anti-roll bar substantially reduced axle friction, indicating that these components introduce preload within the axle assembly that increases friction. In addition, a sand-contaminated lower ball joint significantly increased friction in the linear evaluation region. The damping analysis revealed that draining the shock absorbers of oil reduced axle damping by approximately 80% relative to the intact axle for all investigated excitations.
