Search Results (5)

Within computer-aided geometric design (CAGD), Said–Ball curves are primarily adopted in domains such as 3D object skeleton modeling, vascular structure repair, and path planning, owing to their flexible geometric properties. Techniques for curve degree reduction seek to reduce computational and storage demands while striving to maintain the essential geometric attributes of the original curve. This study presents a novel degree reduction model leveraging Euclidean distance and curvature data, markedly improving the preservation of geometric features throughout the reduction process. To enhance performance further, we propose a multi-strategy enhanced coati optimization algorithm (MSECOA). This algorithm utilizes a good point set combined with opposition-based learning to refine the initial population distribution, employs a fitness–distance equilibrium approach alongside a dynamic spiral search strategy to harmonize global exploration with local exploitation, and integrates an adaptive differential evolution mechanism to boost convergence rates and robustness. Experimental results demonstrate that the MSECOA outperforms nine highly cited agorithms in terms of convergence performance, solution accuracy, and stability. The algorithm exhibits superior behavior on the IEEE CEC2017 and CEC2022 benchmark functions and demonstrates strong practical utility across four engineering optimization problems with constraints. When applied to multi-degree reduction approximation of Said–Ball curves, the algorithm’s effectiveness is substantiated through four reduction cases, highlighting its superior precision and computational efficiency, thus providing a highly effective and accurate solution for complex curve degree reduction tasks. Full article

To improve the reliability of the shearer output shaft in coal seams with gangue, taking the MG400/951-WD shearer model as the research object, a test system for the physical and mechanical properties of coal seam samples containing gangue was established. Based on the coal breaking theory, the impact load of the spiral drum in a coal seam with gangue was simulated. Combined with rigid-flexible coupling virtual prototype technology, a rigid-flexible coupling virtual prototype model of a shearer with an output shaft as the modal neutral file was established. The output shaft is a typical symmetrical part, and it is of great significance to analyze it by using dynamic theory and mechanical reliability theory. The shearer system modal, the stress distribution of output shaft, and vibration characteristics were obtained by dynamic simulation. Based on resonance failure criterion and combined with a neural network, the output shaft stress reliability, vibration reliability, amplitude reliability, and reliability sensitivity were analyzed under relevant failure modes. The state function of the output shaft reliability optimization design was established, and the structural evolution algorithm obtained the optimal design variables. The results show that the maximum stress of the output shaft is reduced by 14.06%, the natural frequency of the output shaft is increased, the amplitude of the output shaft is reduced by 31.13%, and the reliability of the output shaft is improved. The combination of rigid-flexible coupling virtual prototype technology, reliability sensitivity design theory considering correlated failure modes, and structural evolution algorithm provides a more reliable analysis method for the reliability analysis and design of mechanical equipment transmission mechanisms, which can enhance the reliability of the shearer’s cutting unit and improve safety in fully mechanized coal mining faces. The proposed methodology demonstrates broad applicability in the reliability analysis of critical components for mining machinery, exhibiting universal adaptability across various operational scenarios. Full article

Considering the various morphologies of carbon nanotubes (CNTs), it is expected to solve the contradiction between concentration polarization and electrochemical polarization in vanadium redox flow batteries (VRFBs). This paper investigates the structural evolution of CNTs grown on the surface of thermally oxidized carbon cloth (TCC) and their impact on the performance of VRFBs. The morphological results indicate that thermal oxidation treatment forms pores on the surface of the TCC, providing nucleation sites for CNT growth. Spiral-shaped CNTs (TCC@s-CNTs) were formed in a short growth time (1 h), and their high defect density originated from the non-steady-state supply of carbon sources and the dynamic behavior of the catalyst. While 3 h of growth forms a network structure (TCC@n-CNT), the van der Waals force drives the self-assembly of its three-dimensional network. Although the TCC@s-CNT exhibits high catalytic activity due to its high defect density and edge active sites, the performance of VRFBs is more dependent on the three-dimensional conductive network of the TCC@n-CNT. At 240 mA/cm², the energy efficiency (EE) of a VRFB assembled with the TCC@n-CNT reaches 71%, and the capacity retention rate is 15% higher than that of the TCC@s-CNT. This work reveals the synergistic mechanism of CNT morphology regulation on electrode performance and provides theoretical guidance for the design of VRFB electrodes. Full article

Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude. Full article

The enhancement of green animal husbandry has a significant effect on carbon emissions, carbon neutrality, and ecological development. Promoting the production transformation of polluting enterprises has caught the attention of local governments, and breeding companies are faced with either maintaining current practices or green development. This study investigated the evolution of decision-making processes between local governments and breeding companies from the perspective of evolutionary game theory, offering an evolutionarily stable strategy (ESS) for both parties, based on replicator dynamics. Within the model, the static rewards and punishments were framed by conventional environmental protection requirements, and the dynamic rewards and punishments were based on stricter environmental standards. Numerical simulation and sensitivity analyses were then performed. We found that when environmental protection policy was not considered, no ESS emerged in the replicator dynamic system. When tightening of environmental protection policy was considered, the system spirally converged to a stable equilibrium point. Hence, more demanding environmental protection requirements facilitated the development of green animal husbandry. Reasonable reward and punishment mechanisms can achieve the goal of the green development of enterprises under the premise of reducing regulatory costs. This study provides guidance for optimizing government decision-making and promoting the green development of animal husbandry. Full article