Search Results (359)

Cavitation is an important factor limiting the performance and durability of hydraulic turbines. Computational fluid dynamics (CFD) provides access to local pressure and vapour-volume-fraction (VVF) fields, but its computational cost can limit the assessment of broad operating envelopes. This study develops and evaluates a CFD-conditioned Random Forest surrogate for voxel-level cavitation assessment in two hydraulically different machines: a pump-as-turbine and a Francis turbine. CFD-derived pressure and VVF fields were converted into a common voxel representation and used to define operational labels for cavitating and intensely cavitating regions. Several pressure- and VVF-based quantities were evaluated as candidate Stage-1 regression targets. The final Stage-1 representation retained three predicted VVF descriptors: voxel mean, 95th percentile and standard deviation. These predictions, together with spatial, operating-condition and neighbourhood features, were subsequently used by Stage 2 to classify cavitation occurrence and intensity. The final classifier does not require true voxel-level CFD descriptors at inference time. Complete CFD operating cases were held out during grouped validation to prevent leakage between spatially correlated voxels. The results show that the surrogate can reproduce the principal CFD-derived cavitation patterns for previously unseen operating cases within the represented machine-specific envelopes. However, strict zero-shot transfer between the two machines produced poor performance, demonstrating that the fitted model is not geometry-independent and requires target-machine adaptation. The proposed framework should therefore be interpreted as a rapid CFD-based screening tool for prioritising operating conditions and spatial regions requiring further numerical or experimental investigation, rather than as a replacement for detailed CFD or experimentally validated cavitation monitoring. Full article

Dendrobium polysaccharide is an important indicator for evaluating the quality of Dendrobium huoshanense. To improve the prediction accuracy of polysaccharide content, this study proposes an improved Artificial Lemming Algorithm (IALA) optimized BP neural network model. In IALA, a periodic mutation strategy and a fast hybrid opposition learning strategy (FHOBL) are introduced to enhance population diversity, improve global search ability, and avoid premature convergence. The proposed IALA was first evaluated on CEC2017 and CEC2020 benchmark functions. Experimental results show that IALA achieves better or competitive performance compared with seven other algorithms in terms of mean fitness, best fitness, and standard deviation. Statistical tests, including Wilcoxon rank-sum and Friedman tests, further verify the significant superiority and robustness of IALA. Then, IALA was used to optimize the initial weights and thresholds of BP neural networks for Dendrobium polysaccharide content prediction. The results show that IALA-BP achieves the best overall prediction performance, with an R² of 0.8731, RMSE of 2.1581, and MSE of 4.6683. Compared with standard BP and other optimized BP models, IALA-BP provides more accurate and stable prediction results. Therefore, the proposed IALA-BP model is effective for rapid prediction of Dendrobium polysaccharide content. Full article

The Kangaroo Escape Optimization Technique (KET) combines escape and safe-area searches, but its fixed stage allocation, restricted guidance range, limited refinement of low-ranked individuals, and insufficient use of population-state information can reduce its performance on complex problems. This study develops a Multi-Strategy Kangaroo Escape Optimization Technique (MSKET) through iteration-dependent stage switching, population-proportion-based candidate guidance, selective greedy DE/rand-to-best/1 refinement, and beta-distribution opposition-based global-best guidance. The contribution lies in assigning established mechanisms to specific KET limitations and coordinating them across different stages and population subsets. MSKET was evaluated through 30 independent runs on the 100-dimensional CEC2017 and CEC2020 suites. It ranked first on 22 of 29 CEC2017 functions and 8 of 10 CEC2020 functions, with the best average rank on both suites. Ablation, diversity, and nonparametric statistical analyses further supported the observed performance gains. MSKET also achieved the best overall repeated-run results on the piston–lever and three-bar truss design problems. For near-infrared prediction, MSKET-BP obtained an R² of 0.86440, an RMSE of 2.1377, and a MAPE of 5.0083% on the testing set. These results indicate improved search and prediction performance within the examined tasks, although at a higher computational cost than KET. Full article

Developing Cu-based composites with excellent electrical and mechanical properties under multiphysics-coupled service conditions remains challenging. Novel Cu-Nb-Gr composites were fabricated by high-energy ball milling and High-pressure Multi-field Assisted Rapid Sintering. Experiments combined with computational fluid dynamics (CFD) and finite element method (FEM) simulations were used to investigate how Gr regulates material performance. The incorporation of 3 vol.% Gr promoted the formation of a semi-continuous Gr-rich structure coupled with an Nb-rich load-bearing structure. Under arc erosion, the semi-continuous Gr-rich structure provided efficient heat-conduction pathways, reducing the peak temperature and metal-vapor recoil force, while the Nb-rich load-bearing structure suppressed liquid–metal spattering and stabilized the molten pool. Simultaneously, Gr dynamically spread to form a continuous solid-lubricating film during sliding friction, significantly reducing the coefficient of friction and interfacial shear stress. Furthermore, under compressive loading, the semi-continuous Gr-rich structure coupled with the Nb-rich load-bearing structure alleviated interfacial elastic–modulus mismatch and extreme stress concentration, limiting macroscopic plastic deformation of the matrix. Consequently, Cu-Nb-3Gr achieved a favorable balance of arc-erosion resistance, wear resistance, and compressive performance, providing a new strategy for improving conventional Cu-based composites. Full article

To address the problems of the Electromagnetic Wave Propagation Algorithm (EMWPA)—insufficient initial-population coverage, an imbalance between exploration and exploitation, and a tendency to fall into local optima—in high-dimensional complex optimization problems, this paper proposes a boosted electromagnetic wave propagation optimization algorithm, BEMWPA. First, a cubic chaotic map is introduced in the population-initialization stage to enhance the uniformity of the initial-solution distribution and the search-space coverage. Second, nonlinear phase modulation is applied to the electric- and magnetic-field driving terms, and a differentiated probabilistic switching mechanism is constructed to improve the dynamic coordination between global exploration and local exploitation. Furthermore, a Beta-distribution opposition-based learning strategy is introduced to enhance the algorithm’s ability to escape local optima by generating high-quality opposite candidate solutions. To verify the effectiveness of the proposed algorithm, systematic comparative experiments are conducted on the CEC2017 benchmark function set, and BEMWPA is combined with rapidly-exploring random tree (RRT) and applied to path planning of a welding manipulator in complex multi-workpiece scenarios. For a three-dimensional welding scenario containing 12 workpieces, 12 closed weld seams, and multiple obstacle constraints, BEMWPA-RRT reduces the initial inter-seam transfer path length of RRT from 586.00 mm to 479.11 mm, representing a relative reduction of 18.24%, and the complete end-effector path length is reduced from 2974.00 mm to 2867.11 mm, representing a relative reduction of 3.59%. Meanwhile, the optimized transfer path length is only 1.59 mm longer than the obstacle-free ideal transfer length of 477.52 mm, indicating that the proposed method can approach the geometric lower bound of this scenario while satisfying the obstacle-avoidance constraints. Kinematic verification on a seven-degrees-of-freedom welding manipulator further shows that the optimized Cartesian-space path can be converted into a continuously executable joint-space trajectory, providing an effective method for offline welding path planning of complex multi-workpiece tasks. Full article

This study presents a cost-effective approach for detecting internal leakage faults in hydraulic cylinders by leveraging features extracted from existing control signals, specifically the PID valve input. The key innovation lies in eliminating the need for additional sensors or hardware modifications, making the method suitable for low-cost real-time implementation. Several low-complexity, time-domain features were identified and extracted from the control signal, which reflect changes in system behavior due to internal leakage. These features are designed for edge computing platforms, enabling practical deployment in industrial environments. The proposed method is particularly applicable to systems with known loads and consistent duty cycles, such as hydraulic presses, where deviations in control signal behavior can reliably indicate leakage. However, limitations arise when applied to systems with stochastic or highly variable loading, such as mobile machinery, where external disturbances can obscure leakage effects. This approach enables early fault detection and condition monitoring in hydraulic systems without increasing system complexity or cost. It provides a foundation for predictive maintenance strategies in both stationary and mobile hydraulic equipment, contributing to improved reliability and reduced downtime. Full article

To overcome challenges associated with Cloud Drift Optimization (CDO), primarily involving early stagnation and the imbalance between global exploration and local exploitation in tackling high-dimensional, complex optimization tasks, an improved variant named Chaotic Random Cloud Drift Optimization (CRCDO) is proposed in this study. First, a Kent chaotic initialization mapping is adopted to generate diverse initial solutions. Second, the parameter sensitivity is adjusted to appropriately strengthen the local exploitation capability of the algorithm. Third, a centroid opposition-based learning strategy is introduced to generate mutated clouds, which enables the algorithm to escape local optima. Owing to the cooperative effect of these three strategies, CRCDO achieves faster convergence and higher computational accuracy while maintaining dynamic stability. To evaluate its performance, CRCDO is compared with eleven well-established optimization algorithms on the CEC2017 suites. The experimental results demonstrate that CRCDO exhibits superior optimization performance on a wide range of complex problems. Furthermore, the proposed algorithm has been successfully applied to three representative engineering design problems, including robot gripper design, welded beam design, and compression spring design, as well as a regression task for predicting praeruptorin A and praeruptorin B in Peucedanum praeruptorum. The results show that CRCDO achieves faster convergence, higher solution accuracy, and greater stability on the benchmark problems, while its engineering and prediction applications demonstrate robustness and practical applicability across different problem classes. Full article

This study proposes an integrated finite element methodology for evaluating and redesigning three critical subsystems of an XCMG XC740K skid-steer loader: the excavation attachment, the arm–bucket charging system, and the ROPS/FOPS operator protection cab. The components were reconstructed by reverse engineering and 3D scanning, modeled in CAD, and simulated in ANSYS Workbench/Mechanical under load cases derived from hydraulic parameters, soil–tool interaction, and international safety standards. The novelty of the work lies in applying a single FEM-based workflow to three interacting subsystems of the same compact machine, rather than optimizing isolated components independently. The original configuration showed critical effort concentrations in the cab and charging system. Localized geometric reinforcements and the use of high-strength and wear-resistant steels improved stiffness and safety margins in the excavation bucket, loading bucket, and ROPS/FOPS cab. However, the arm–quick coupler region remained the controlling weak point of the loading assembly, indicating the need for further redesign. The proposed approach provides a transferable computational framework for identifying structural vulnerabilities and prioritizing redesign actions in compact earthmoving machinery. Because the study is numerical, future experimental validation is required before certification or field implementation. Full article

To tackle the drawbacks inherent in the Chinese Pangolin Optimization (CPO) algorithm, such as uneven population initialization distribution and a tendency to fall into local optimal solutions, this paper proposes an ACDCPO algorithm that integrates boundary-adaptive contraction initialization, Cauchy inverse cumulative distribution mutation, and dynamic opposition-based learning strategies, which effectively enhances the uniformity of population distribution, improves the ability to jump out of local optimum, and strengthens the adaptive coordination between exploration and exploitation. To validate its performance, the proposed ACDCPO is compared with nine representative algorithms using the CEC2017 and CEC2022 test functions. The results verify that ACDCPO achieves remarkably higher convergence precision and stability than the comparative algorithms. In four typical engineering optimization tasks, ACDCPO shows strong constraint handling ability and engineering adaptability. In addition, based on near-infrared spectrum data, the ACDCPO algorithm optimized the BP network model for the moisture content prediction of Dendrobium huoshanense, and the coefficient of determination (R²) reached 91.211%, which verified the effectiveness of the method in practical applications. Full article

With the development of electrification and green technologies in construction machinery, servo pump-controlled technology has shown great potential in electric loaders due to its high efficiency and energy-saving characteristics. However, the complex coupling among multiple parameters makes it difficult to simultaneously optimize dynamic response and energy efficiency. To address this issue, a multi-objective parameter optimization method for servo pump-controlled units based on Sobol sensitivity analysis and the NSGA-II algorithm is proposed. First, an electro-hydraulic coupling model of the servo pump-controlled unit is established. Subsequently, Sobol global sensitivity analysis is employed to identify sensitive parameters affecting system performance, and a multi-objective optimization model is constructed with dynamic performance and energy efficiency as optimization objectives. Finally, the NSGA-II algorithm is adopted for coordinated parameter optimization, and the results are validated through MATLAB/Simulink simulations and experiments. Results show that the optimized system achieves improved dynamic response, stability, and energy efficiency, demonstrating the effectiveness of the proposed method for the parameter optimization of electric loader servo pump-controlled systems. Full article

Swirling-flow instabilities in hydraulic turbine diffusers constitute a major operational challenge, particularly when Francis turbines operate under part-load conditions. Over the past decades, numerous control strategies have been proposed to mitigate the instabilities associated with swirling flows. This study presents a comprehensive numerical analysis of a passive flow-control technique based on an adjustable diaphragm device, referred to as IRiS. The primary objectives are to attenuate swirling-flow instabilities and to enhance energy recovery within the draft tube. Three-dimensional unsteady flow simulations were performed for multiple IRiS configurations, characterized by different shutter area ratios. The results indicate that the IRiS device can reduce pressure pulsation amplitudes by up to 60% while simultaneously improving pressure recovery. However, the simulations also show that hydraulic losses may increase at part-load operation, depending on the selected IRiS shutter opening. Overall, the findings support the applicability of this passive control concept for both new and rehabilitated Francis turbines operating under off-design conditions, far from the best efficiency point. Full article

This study presents a numerical analysis of a low-specific-speed Pelton turbine using the open-source Lagrangian code DualSPHysics. The numerical results were compared with experimental data. The main objective was to determine whether the applied numerical approach yielded reproducible results and provided insight into momentum transfer and water movement in the jet, runner, and casing. The influence of numerical parameters, such as particle size, kernel and smoothing length coefficients, and shifting value, on the simulation results was tested. As a result, an optimal particle size formulation is suggested. Furthermore, we established connections for two numerical parameters in DualSPHysics, the “smoothing length coefficient” and the “shifting”, to improve fluid flow behaviour and the resulting torque without modifying the physical parameters. In addition, we investigated deviations from the optimal achievable torque and improvements in fluid behaviour using these numerical parameters. We discussed the effect of the bucket disturbance on the jet from the particle simulation, alongside the similarity law simulation and the actual prototype’s measurement results. Identical simulations of the physical properties of the operation points were compared in momentum. Full article

To address the inherent limitations of the Dhole Optimization Algorithm (DOA)—limited exploration range, insufficient population diversity, and slow convergence—this paper proposes a Modified Dhole Optimization Algorithm (MDOA) integrating a Beta distribution-based opposition learning strategy, a DE/rand-to-best/1 differential mutation mechanism, and nonlinear parameter control. MDOA is evaluated on 41 CEC2017 and CEC2022 benchmark functions, outperforming 11 state-of-the-art algorithms in convergence speed, accuracy, and robustness. It is then applied to five engineering optimization problems: compression spring design, speed reducer weight minimization, rolling bearing optimization, tubular column design, and moisture content prediction of Dendrobium huoshanense using near-infrared spectroscopy with a BP neural network. The MDOA-BP model reduces MAE, RMSE, MSE, and MAPE by 27.5%, 27.8%, 47.6%, and 31.0%, respectively, while increasing $R^2$ from 0.8339 to 0.9130, achieving the best results among all comparison models. These results demonstrate that MDOA is a highly effective and robust optimizer for complex constrained engineering and high-dimensional optimization tasks. Full article

Hilly and mountainous regions are strategically vital for national food security. However, due to complex topographical constraints, their agricultural mechanization levels remain severely underdeveloped. This creates a critical bottleneck in agricultural modernization. Conventional agricultural machinery faces multifaceted challenges in terrain adaptability, operational efficiency, and safety assurance when deployed in these environments, necessitating the urgent development of specialized chassis with enhanced trafficability and stability. Following a systematic literature review of key technologies, including power transmission systems, traveling and support mechanisms, leveling control, and navigation tracking, this study reveals that current chassis technology is advancing toward intelligentization, enhanced efficiency, environmental sustainability, and improved terrain adaptability. The analysis demonstrates that multiple technological pathways, encompassing mechanical, hydraulic, and electric drives, are exhibiting convergent and complementary trends. Future research and development should prioritize the following areas: integrated intelligent coordinated control architectures, green and sustainable power system innovation, modular and reconfigurable platform design, and the establishment of collaborative frameworks among industry, academia, research institutions, and application sectors. Comprehensive standardization systems are also needed. These strategic directions are essential for comprehensively elevating agricultural mechanization levels and maximizing developmental benefits in hilly and mountainous regions. Full article

Electro-hydraulic servo systems are widely used in industrial machinery and automation due to their high power density and fast dynamic response; however, their achievable positioning accuracy is often limited by nonlinear friction effects. In many robust control strategies, including sliding mode control and its multi-surface variants, friction is commonly treated as a lumped bounded disturbance. This simplification neglects the dynamic and operating condition-dependent nature of friction, leaving the practical value of explicit friction compensation insufficiently clarified, especially for electro-hydraulic actuators operating near their bandwidth limits. This paper presents an experimental evaluation of LuGre-based dynamic friction compensation integrated into a multi-surface sliding mode control framework for electro-hydraulic actuators. Rather than proposing a new control methodology, the study focuses on clarifying, from a control-oriented mechanical engineering perspective, how friction compensation influences closed-loop tracking performance under different operating regimes. The proposed scheme is implemented on a laboratory-scale electro-hydraulic test bench and evaluated using step and sinusoidal reference motions over a wide range of excitation frequencies, from low-speed operation to the practical bandwidth limit of the actuator. Comparative experiments with a conventional proportional–integral–derivative controller and a multi-surface sliding mode controller without friction compensation are conducted to isolate the effect of explicit friction modeling. The experimental results reveal a strongly frequency-dependent influence of friction on tracking performance. At low excitation frequencies (e.g., 0.1 Hz), friction compensation provides only marginal improvement in root mean square (RMS) tracking errors. In contrast, as the excitation frequency approaches the actuator bandwidth limit (1 Hz), explicit LuGre-based friction compensation reduces the relative RMS tracking error by approximately 57% compared with the baseline MSSM controller and by up to 82% relative to a conventional PID controller. These results demonstrate that the effectiveness of friction compensation is highly dependent on operating conditions, providing experimentally grounded guidance for the design of control strategies for bandwidth-limited electro-hydraulic machines. Full article