The collection of seabed ore particles is a core technology of exploiting deep sea mineral resources, with wall-attached jet collection technology based on Coandă-effect being considered as a nodule collection method with engineering application potential. Based on the experimentally verified CFD-DEM numerical simulation, the optimization of geometric parameters of the collection device is conducted to improve pick-up efficiency. The influences of three geometric parameters, i.e., the ratio of the curvature radius of the convex curved wall to the diameter of the nodule particle R/d, the tangential radian of the jet θ, and the ratio of the thickness of the jet to the diameter of the nodule b/d on the critical unconditional jet flow rate q₀, are investigated and compared. The nodule collection characteristics are revealed through an analysis of the flow field characteristics. The results show that b/d has the greatest influence on the pick-up efficiency, followed by R/d, while θ has the least. The performance of nodule collection is optimal when R/d=9, θ=1.05 rad, and b/d=0.26 in contrast conditions. This research provides technical support for designing and developing the Coandă-effect-based collection devices.

This paper investigates vortex-induced vibration (VIV) characteristics of double free-hanging water transmission pipes, which are crucial for temperature difference energy harvesting platforms. Compared to a single pipe, double pipes could offer higher transport efficiency and cost-effectiveness. In this paper, model experiments were conducted to analyze VIV characteristics of the double free-hanging pipes and a method for identifying vortex-induced loads for large displacements and small deformations was proposed. A comparative analysis of the VIV characteristics of double free-hanging pipe and the single pipe was performed. The findings show that VIV displacement amplitudes of double free-hanging pipe are similar at low flow velocities but differ with those of single pipe at high velocities. The double free-hanging pipe is more prone to instability in VIV, including traveling waves and multi-frequency responses. The VIV frequencies of double free-hanging pipe can be predicted by the same Strouhal number as that of the single pipe. Additionally, a significant difference in the added mass coefficient affects natural wet frequency adjustment for VIV resonance.

The study of the dynamic response of a horizontal axis twin wind turbine in tandem arrangement is crucial for ensuring the structural safety of the wind turbine. Based on the computational fluid dynamics (CFD) method, the characteristics of the wake flow field of the downstream turbine, located in the near wake region of the upstream turbine, are analyzed. The time course curves of the aerodynamic loads on the twin turbines are obtained. Structural dynamics and finite element numerical methods are then used to analyze the wind-driven dynamic effects of the upstream and downstream turbine structures. It is found that the wake velocity deficit in the near wake region is significant, causing a reduction in thrust and torque of the downstream turbine by 54.94% and 91.89% respectively. Additionly, the wake turbulence increases cyclic fluctuation of aerodynamic load on the downstream turbine. While the aerodynamic load volatility has a small effect on the dynamic response of the downstream wind turbine, the overall dynamic response is weaker, and the displacement of the downstream wind turbine tower top in the thrust direction is reduced by 50.79%. The results provide technical references for the analysis of aerodynamic response of wind turbine cluster structures in offshore wind farms.

During the block assembly, large curved thin plates (such as outer plates) undergo deformation due to the force of gravity when they are placed on the jigs, which affects the accuracy and quality of the block assembly in shipbuilding. In order to predict the deformation of these large curved thin plates within a given jig layout, this paper introduces a Transformer-based surrogate model with two-stage Latin hypercube sampling (TSM-TLHS). Primarily, compared to traditional approaches, the two-stage Latin hypercube sampling (TLHS) method enables direct sampling of irregularly shaped thin plates. Simultaneously, this paper uses a Transformer-based surrogate model (TSM) incorporating multi-head attention modules and positional encoding to comprehensively consider the impact of jig positions and corresponding node displacements on thin plate deformation. Real case results demonstrate that the prediction error of this TSM-TLHS method is only 61 μm, meeting the on-site assembly precision requirements for predicting plate deformation. This facilitates timely anti-deformation compensation by block in shipyards, ensuring assembly quality.

As a key component in the subsea production system for oil and gas exploitation, a marine umbilical consists of optical cables, electrical cables, steel tubes, and filler bodies. The difference of materials and dimensions between the components leads to a great difference in their mechanical properties, and the different layouts cause a large gap in the performance of an umbilical. Considering the compactness, balance, and heat source dispersion of the cross-section, a multi-objective optimization model is established in this paper. Based on the quasi-physical algorithm, the layout design of cross-section of an umbilical containing equal-diameter components is conducted. Due to the mutual constraints between functional components, the optimized cross-section will have large gap. In order to meet the requirement of dense cross-sectional layout in the umbilical cable design specification, a strategy for automatically filling filler bodies based on image recognition is introduced, in combination with the layout optimization process. Finally, taking an umbilical as an example, the filling strategy is utilized to complete the design of cross-sectional filler bodies after obtaining the optimal layout through the quasi-physical algorithm. The algorithm is validated by comparison with the initial cross-sectional layout, demonstrating its effectiveness as a reference for the design of cross-sectional filler bodies of umbilicals.

To enhance the accuracy of finite element model simulation, a model updating method based on Bayesian theory is proposed, and the updating efficiency is improved by integrating improved Markov chain Monte Carlo (MCMC) algorithm and surrogate model. A radial basis function (RBF) surrogate model is constructed using the parameters to be updated as inputs and the finite element model modal responses as outputs. Whale optimization algorithm (WOA) is introduced into the MCMC algorithm and the uncertain parameters are updated. Finally, a numerical study on a simply supported beam and an experimental study on a three-story steel frame are conducted to verify the accuracy of the proposed method. The results show that WOA can significantly improve the stability and convergence speed of the MCMC algorithm, the updating efficiency can be improved by 13.9% at most, and the maximum frequency errors of the simply supported beam model and the three-story steel frame model updated by the WO-MH algorithm are 0.009% and 2.41%, respectively. The proposed model updating method can effectively enhance the simulation accuracy of the finite element model under both two-dimensional and eight-dimensional inputs, which provides technical reference for lean simulation and optimal design of building structures.

The effect of tension stiffening allows SG-laminated tempered glass to preserve specific post-fracture mechanical performance, which is significantly influenced by crack morphology, including fragment density and distribution. Accurately evaluating and predicting the post-fracture performance of laminated glass is essential for balancing cost and performance. However, limited work has addressed how crack morphology affects the stiffening behavior of laminated glass after fracture. In this paper, a series of random-cracked tensile tests on SG-laminated tempered glass is performed. The results reveal that the fragment density influences the post-fracture performance of SG-laminated glass by affecting the stress trajectory, and the tensile strength and equivalent stiffness decrease linearly and exponentially, respectively, with increasing fragment density. Afterwards, according to the experimental results, a hypothesis of the effective bonding is proposed and an improved finite element model is then developed. The proposed model accurately predicts the post-fracture tensile performance of laminated glass. Finally, the impact of fragment offset on the stiffening effect is discussed.

To develop a probability distribution model of peak ground acceleration, 255365 ground motion recordings are collected from 500 stations to create initial statistical samples of peak ground acceleration. First, the generalized extreme value distribution is employed as the probability model for peak ground acceleration. The effectiveness of the maximum likelihood estimation method and the linear moment estimation method, commonly used for estimating parameters of the extreme value distribution model, is assessed using the proposed generalized extreme value distribution model. Then, a method is proposed to determine the minimum required sample length when establishing a generalized extreme value distribution model based on the asymptotic normality of the maximum likelihood estimation. The analysis indicates that the data sample size should not be less than 120 when constructing the generalized extreme value distribution model for peak ground acceleration in seismic events. Statistical analysis is conducted on seismic peak ground acceleration data samples which meet the sample size requirement. It is observed that the model parameters converge to a relatively narrow range as the sample size increases. Ultimately, probability statistical models for measured peak ground acceleration and seismic hazard calculation formulas for different types of sites are established.

Understanding the influence of the contact area between supercooled water and surface on the nucleation is critical for engineering applications such as anti-icing surface design and the supercooling preservation. However, the effect of contact area between supercooled water and surface on the nucleation still remains unclear, especially when the contact area is large. Therefore, the influence of the contact area on the freezing temperature of supercooled water inside the hose was experimentally studied by changing the length of silicone and polyvinyl chloride (PVC)-reinforced hoses. The results show that variations in contact area significantly affect the freezing temperature of supercooled water. Based on experimentally observed trends, a predictive model for contact area-supercooling relationship was developed. Furthermore, the area term in the classical nucleation theory (CNT) was modified using the experimental data. The modified nucleation rate predictions align well with the experimental findings regarding the influence of increasing contact area on nucleation. Notably, the effect of contact area on nucleation rate was found to be nonlinear, contrary to the commonly assumed linear relationship. The research methodology and the proposed model in this paper provide a solid foundation for future applications of supercooled water in engineering, as well as for further investigation into the role of contact area in nucleation phenomena.

In the upgrading and automation of aeroengine gearbox assembly line, the introduction of new equipment such as zero-point positioning systems and auxiliary alignment systems has significantly improved production efficiency. However, it has also brought about an increase in systematic errors during multi-process machining. To mitigate the adverse impact of these new systematic errors on machining accuracy, this paper investigates error modeling and precision optimization methods for the alignment system under uncertainty. First, it develops an assembly deviation analysis model, considering the differences between the master and sub-discs in the zero-point positioning system. It then designs a Kolmogorov-Smirnov (K-S) test to verify the accuracy of the model, based on which, the accuracy loss patterns during multi-sub-master disc to master disc interchange processes is analyzed. Afterwards, it defines a precision loss quantification function, incorporating both output distribution deviations and tolerance violation rates. By optimizing the bases structure of the alignment system, the precision loss is reduced from 11.53% to 2.33%. Additionally, the difference in radial runout distribution caused by misalignment between machining and positioning references is reduced from 0.117 to 0.039. These improvements significantly improve the performance of the zero-point clamping and alignment system, providing essential theoretical support for the analysis and optimization of clamping and positioning precision in automated production lines.

Jumping is a viable form of locomotion for lunar surface exploration. However, due to the limited research on the coupling between jumping robots and the lunar surface, applying jumping robots for lunar surface detection remains challenging. Aiming at the load index of 5 kPa for the lunar surface detector, a new energy storage leg configuration of a jumping robot was proposed to realize low load jump with variable initial velocity and direction during take-off. The parameters of energy storage element were optimized to realize near-constant force take-off of the robot, which was validated in a dynamic simulation environment. To enable accurate jumps on the surface of the moon, a lunar soil mechanical property model considering damping characteristics was proposed, a discrete element simulation environment was built to determine the mechanical parameters, with a jumping dynamics model of the lunar surface robot established to verify the model accuracy through discrete element dynamics coupling simulation. Based on this dynamic model, two motion planning algorithms are implemented, confirming the application of the model.

Quenching-partitioning (QP) steel combines ultrahigh strength with good ductility due to the martensitic transformation during plastic deformation. However, the formability of the QP1180 steel remains unclear. In this paper, the ultimate strains of the QP1180 steel under different strain paths are obtained through Nakajima experiment. The effects of the texture evolution and phase transformation on the forming limit of QP1180 steel are analyzed by using a crystal plastic finite element model coupled with the Marciniak-Kuczynski theory (CPFEM-PT-MK). The results show that the ultimate principal strain of QP1180 steel is the lowest under the strain path ζ=0.1, and the established CPFEM-PT-MK model successfully predicts the forming limit of the QP1180 steel sheet. The texture evolutions of the constituent phases in the QP1180 steel are different under various strain paths. According to the simulation, the texture evolutions enhance the forming limit of the QP1180 steel under various strain paths. Without phase transformation, the minimum limited major strain of the QP1180 steel is located at the strain path of ζ=0, which is significantly different from that when phase transformation occurs. Furthermore, the phase transition, related to the specific strain path, does not always enhance the forming limit of the QP1180 steel.

In order to change the undamped state of traditional helical spring, a helical spring with improved damping characteristics is developed by using Fe-Mn alloy. First, the optimal process parameters for Fe-Mn alloy material in manufacturing helical springs is investigated. Then, Fe-Mn alloy helical springs are fabricated and treated with optimized parameters to achieve high damping properties. Finally, the damping properties of Fe-Mn alloy helical spring are studied through the functional principle and analytical model of the helical spring. The results show that the Fe-Mn alloy helical spring exhibits a significant energy dissipation effect compared with the 65Mn helical spring under identical external excitation conditions. Within a specific loading displacement range, the loss factor of Fe-Mn alloy helical spring increases exponentially with the increase of displacement, while its equivalent stiffness decreases linearly, exhibiting pronounced softening characteristics. Specifically, when the equivalent strain amplitude of Fe-Mn alloy helical spring is less than 0.3%, its energy dissipation can be predicted using its torsional strain energy, providing a theoretical basis for spring design. This study provides a new direction for the development and application of vibration isolation products.

Aiming at the problem of abnormal operation of three-phase voltage-type pulse width modulation (PWM) rectifier caused by factors such as voltage transformation and external disturbances, a control strategy is proposed that integrates active disturbance rejection control (ADRC) with equivalent input disturbance (EID) error estimation and a new terminal fuzzy sliding mode current control. By analyzing the underactuated characteristics of the three-phase voltage-type PWM rectifier system, a novel dual-closed-loop controller is constructed. The inner current loop adopts a terminal fuzzy sliding mode control strategy based on EID error estimation, while the outer voltage loop employs an ADRC strategy based on EID error estimation. To enhance controller accuracy and optimize control performance, error estimation is applied to mitigate current chattering and negative sequence current issues inherent in sliding mode control. Furthermore, harmonic components in the voltage loop are effectively suppressed, improving the voltage response speed and ensuring the stable operation of the three-phase voltage-type PWM rectifier under disturbance conditions. Finally, the proposed control strategy is validated through simulation experiments, demonstrating its effectiveness and superiority.

The prediction of pelvic lymph node metastasis (PLNM) based on quantitative preoperative magnetic resonance imaging features of prostate cancer primary tumor is an important reference for treatment planning. However, current prediction methods inadequately capture the heterogeneity within the primary tumor, resulting in a weak correlation between extracted quantitative image features and PLNM prediction. To address the aforementioned issues, an attention-guided multi-task learning network with tumor segmentation as an auxiliary task is proposed for PLNM prediction. First, within the tumor segmentation network, a multi-branch anisotropic large kernel attention module is introduced, where a larger receptive field is obtained through different branches and anisotropic large convolutional kernels, effectively capturing both local and global tumor information. Then, within the PLNM prediction network, a multi-scale feature interaction fusion attention module is introduced to hierarchically fuse and select features from multiple scales. The experimental results on a dataset of 320 cases demonstrate that the area under the precision-recall curve and the area under the receiver operating characteristic curve of the method proposed are (85.44±2.04)% and (91.86±2.18)%, which are superior to state-of-the-art methods and multi-task approaches.