There exists a special bidirectionally sheared flow field due to solitons in the South China Sea. An experimental study of the mean drag force of a flexible riser undergoing vortex-induced vibration under bidirectionally sheared flow is conducted. Modal analysis and the beam theory are applied to processing the strain signal measured in the experiment to obtain the mean drag force. The initial displacement, mean drag force, and mean drag coefficient are investigated, and the amplification of the mean drag coefficient is confirmed under bidirectionally sheared flow with comparable amplification to linear shear flow. Meanwhile, a unique phenomenon that the shear force reaches an extreme value in the center of the riser under bidirectionally sheared flow is found in the experiment. The shear force coefficient is proposed and fitted to obtain an empirical formula for the extreme value of the central shear force of the flexible riser under bidirectionally sheared flow, which will provide external load input for the design of the riser in the South China Sea.

A model predictive control (MPC) method based on the vector field guidance law is proposed to improve the effectiveness of path following and collision avoidance for ships. First, the path following and collision-avoidance problems are transformed into heading-control problems by the vector field guidance law. Then, the first-order Nomoto response model is adopted as the ship dynamic model for the model predictive control. Considering the input limitation of the rudder angle, the disturbance observer is introduced to compensate the model error and the environmental disturbances. The stability of the designed path following control system is verified by the Lyapunov theory. Finally, a collision avoidance strategy based on the vector field guidance law is designed to enable the ship to avoid collision autonomously in the process of path following. The simulation results indicate that the proposed methods can make the ship track the target path accurately and realize collision avoidance under the impacts of wave disturbances.

Physics-informed neural networks (PINN) are applied to the reconstruction of the ship propeller wake field. First, the principle and basic framework of PINN were introduced. Then, the Burgers equation was selected to verify the feasibility of PINN in solving partial differential equations. After that, the propeller of KVLCC2 in open water is simulated using computational fluid dynamics (CFD) software STAR CCM+, and the flow field information of the KVLCC2 propeller is obtained. Based on the simulated flow field information data, the training sample set was constructed to train PINN. The trained PINN was used to infer the approximate solution of the governing equation at any time and space. Finally, the velocity and pressure distribution obtained by PINN were compared with the velocity and pressure distribution simulated by STAR CCM+. The results validate the reliability of PINN in propeller wake field reconstruction, which can be concluded that PINN can be applied to the reconstruction of the ship propeller wake field.

Considering that the research on resonant three-dimensional swirling waves in vertical cylindrical tanks is relatively rare, this paper has built a set of tank sloshing model test device to study the first-mode and second-mode resonant three-dimensional swirling waves in vertical cylindrical tanks. It is found that when the external excitation frequency is equal to the first mode natural frequency of the free surface movement, three typical waveforms, planar wave, breaking wave, and swirling wave, appear in the cylindrical tank. When the external excitation frequency is close to the first mode natural frequency of the free surface movement, the modulation wave, namely beat phenomenon, appears in the cylindrical tank. When the external excitation frequency is equal to the second mode natural frequency of the free surface movement, three typical waveforms, the second mode wave, the second mode swirling wave, and the irregular wave, appear in the cylindrical tank. The research results in this paper can provide reference for offshore engineering designers to design cylindrical tanks of floating platforms.

Due to the tip clearance of the pump-jet propulsor (PJP), the flow field characteristics in the PJP are more complicated. To explore the influence of scale effects on the tip clearance flow field of PJP, the unsteady Reynolds-averaged Navier-Stokes (URANS) equation and the SST k-ω turbulence model are used. The computational domain is discretized by structured grid, and the sliding grid is used to deal with the relative motion between the rotor and stationary components. The feasibility of the numerical method is verified by grid uncertainty analysis, and the numerical results are in good agreement with the model-scale PJP experimental data. The open water performance of the three scale PJP models is numerically calculated and analyzed from the perspective of vorticity field and pressure field. The results show that the efficiency of the full-scale PJP model will be improved under all the advance coefficients, the vorticity collapse of the full-scale PJP is earlier, the intensity is lower, the pressure coefficient of the TLV vortex core center is smaller, and the fluctuating pressure amplitude between the tip clearance is lower.

To solve the problem of cold protection for marine equipment against cold caused by the harsh polar environment, electrical tracing is commonly used in engineering. In this paper, numerical simulation and experimental methods are used to analyze the influencing factors of thermal balance of electric heat tracing tread plate components in typical polar environment with wind speed ranging from 0 to 40 m/s and temperature ranging from -40 ℃ to 0 ℃. Based on numerical simulation and experimental test, the convective heat transfer coefficient of electric heating tread plate components at different wind speeds and temperatures is obtained. The results show that increasing wind speed and decreasing temperature will increase the convective heat transfer coefficient of the tread plate. Wind speed is the main factor affecting the heat transfer of the tread plate, while temperature has little effect. Finally, based on the experimental data and the classical theory of convective heat transfer of plate components, a prediction model of convective heat transfer coefficient of electric heating tread plate components is established, and the correctness of the model is verified by numerical simulation.

Recoil control for deepwater drilling riser system after emergency disconnection is a necessary technique in deepwater oil exploration. However, some dynamic parameters of the riser system are uncertain and difficult to measure, which poses severe challenges to the riser recoil control. Therefore, an intelligent riser recoil adaptive control method considering system uncertainties is established. Based on the nominal state-space expression of recoil control and closed-loop system stability, the modified control input considering model uncertainties is derived. The radial basis function (RBF) neural network is adopted to approximate model uncertainties, and the weight adaptive law satisfying Lyapunov stability is selected to realize dynamic compensation of uncertainties in control inputs. The results show that the proposed method is applicable to the actual recoil control valve with adjustment speed limit. The uncertainties of tensioner stiffness, damping, mud discharge friction, and riser buoyancy loads have a certain effect on recoil dynamic response and control performance. The RBF adaptive control method can effectively reduce the initial recoil oscillation height and reduce the risk of recoil bottoming. The findings can effectively solve the problem of recoil control without accurate system parameters in the engineering background.

The application of high-strength steel in extremely cold polar regions can reduce steel consumption and save the cost of fabrication, transportation, and installation of steel structures in the harsh low-temperature environment. In order to study the mechanical properties of HG785 high-strength steel under polar low-temperature conditions, uniaxial tensile tests were conducted on high-strength steel coupons by considering two thicknesses and five low-temperature cases. It was found that the elastic modulus, yield strength, and ultimate tensile strength of HG785 high-strength steel in polar low-temperature environment are higher than those at an ambient temperature of 25 ℃. All tensile coupon specimens failed by traditional necking in a ductile manner without brittle failure tendency. Based on the test results, accurate prediction models for mechanical properties of HG785 high-strength steel in polar low-temperature environment were established by the best subset regression analysis. This will facilitate the application of high-strength steel in the design of structural members, joints, and systems in an efficient manner, and provide theoretical support for the promotion of high-strength steel structures in polar low-temperature regions.

In order to achieve high-precision pixel-level detection of multiple surface diseases in metro shield tunnels, a semantic segmentation model SU-ResNet++ based on deep learning is proposed. First, the encoder SE-ResNet50 based on residual unit and attention mechanism is designed and pre-trained, using as the backbone network of U-Net++ to design a new neural network model. Then, through original data collection, data preprocessing, and manual annotation, a shield tunnel surface multiple diseases dataset with 4 500 pictures is constructed. Finally, the proposed method is trained, verified, and tested on a dataset, and applied to practical engineering detection, achieving high-precision pixel-level diseases semantic segmentation. The experimental results indicate that the proposed SU-ResNet++ algorithm is applicable to the detection of shield tunnel disease data, and can automatically and accurately identify the disease category and form. Compared with the traditional semantic segmentation models, its disease identification precision is significantly improved, which meets the practical engineering requirements.

Comprehensive geophysical prospection is an effective technique for karst exploration, but its prediction results usually suffer from significant artificial influence and fuzzy boundaries of karst caves. Based on the machine learning technology, a Gaussian mixture model is used to classify the exploration data of high-density electrical method and data of surface wave method respectively. Then, a Category-boundary algorithm is proposed to further subdivide the classification results, which improves the accuracy of Gaussian mixture model classification. Finally, the classification fusion rules are formulated based on expert experience and geological exploration data. Under the organic combination of survey data-driven and engineering geological knowledge guidance, a new set of high-precision classification and fusion methods is proposed for comprehensive geophysical exploration. By applying this new method to the karst exploration project in southern Zhejiang, a karst cave prediction is made with clearer boundaries. Compared with the actual drilling information, cave prediction and drilling information are highly consistent, which verifies the effectiveness of the method proposed.

In order to more accurately predict the lateral deformation of retaining structures caused by foundation pit excavation, this paper adopts support the vector machine model, traditional artificial neural network model, and two kinds of recurrent neural network models considering temporal inputs to establish a prediction model for the maximum lateral deformation of retaining structures in different foundation pits, and for the same foundation pit under different working conditions. The results show that the artificial neural network can update and predict the deformation of the retaining structure in real time based on the measured data of the project, which is helpful for timely planning of the next construction process of the project. In the prediction of lateral deformation of retaining structures under different working conditions, the cyclic neural network model considering temporal inputs is better than the traditional artificial neural network model.

In order to investigate the evolution of pore structures in cementitious materials, a statistical model is proposed by taking the cement paste as a random dispersion system of two-phase medium. Simultaneously, the μic platform is employed to simulate the cement hydration. The results obtained from the simulation and the disperse models are compared with each other, and further analysis on calculation conditions and parameters of the disperse models are conducted. The pore size distribution obtained from polydisperse hard sphere model is very close to the simulation in completely dispersion condition. Taking into account the cross and agglomeration effects of hydrated products, the calculation results of the monodisperse concentric-shell model are more consistent to the simulations. Considering the flocculation of cement particles in initial state, the monodisperse hard model is closer to the simulation results. This paper offers a new insight from the viewpoints of mathematics and physics to understand and describe the pore structures of cementitious materials.

This paper addresses the issue of perching maneuver of unmanned aerial vehicles in wind-disturbed environments, by combining the control-oriented sparse identification of nonlinear dynamics with control (SINDYc) method and the imitation deep reinforcement learning (IDRL) control strategy. The study focuses on the design of control strategies for perching maneuvers. First, a training environment for the perching system is established using domain randomization, which incorporates various wind conditions. Then, the SINDYc method is employed to learn sparse models of the perching system offline under different wind conditions, using historical data and a candidate function library, to effectively identify the wind information. Afterwards, the perching control strategy is trained using an IDRL algorithm within the training environment that encompasses multiple wind conditions, resulting in a control strategy for perching in wind-disturbed scenarios. Finally, numerical simulations are conducted to verify the effectiveness of the proposed perching control strategy in wind-disturbed environments.

To address the issue of multi-body folding wings being subjected to aerodynamic load changes, gravity reversal, and external disturbances during coordinated tilting, an improved active disturbance rejection control (ADRC) strategy based on the combination of hyperbolic tangent function and phase compensation is proposed. Using the Lagrange equation, the aerodynamic load on the unit wing is applied to the center of mass, and the offset of the gravity term with the attitude angle of the body is provided, thereby establishing the dynamic model of the multi-body folding wing. An improved ADRC controller is designed to track the desired angular trajectory of the folding wing, and appropriate controller parameters are selected to suppress joint vibration. The simulation results show that the improved ADRC method can effectively achieve trajectory tracking and joint vibration suppression under variable load conditions, and it demonstrates smaller tracking errors and better dynamic performance and robustness when subjected to external disturbances.

To address the limited endurance of rotorcraft and the restricted flight areas of fixed-wing aircraft, a vertically take-off and landing multi-unit wings tandem configuration is proposed. First, using multi-body kinematics and the lifting-line theory, the aerodynamic characteristics of the centralized deformation are analyzed, and limits of the flight angle of attack during the deformation process are determined. Then, a nonlinear multi-body dynamics model is established using the quasi-Lagrangian equation to comprehensively describe the relative motion characteristics between the unit wings. The flight efficiency of fixed wings and folding wings during the climb process is compared, which verifies the long endurance and good maneuverability of the folding wings. Finally, a modal analysis of the attitude and structural coupling characteristics of the folding wings in steady-level flight is conducted, based on which the cooperative control law of folding and flight is designed to achieve stable control in the process of tilting and folding.

This paper addresses the formation control problem for ground mobile robot formations and proposes a formation transition method based on dynamic density guidance. To achieve different formation transitions, a centroidal Voronoi tessellations (CVT) formation control algorithm is utilized to avoid collisions during the transition process. By leveraging the properties of the CVT algorithm, a dynamic density is generated by constructing a transition density function between the initial formation density function and the desired density function. The CVT algorithm then guides the robots in the formation to move and complete the transition and reconstruction of the formation. The simulation results demonstrate that, compared to using the desired density function directly, this method not only successfully resolves certain formation transition failures but also reduces the average positional error of the formation during the transition process.

In order that the flight vehicle group could form the expected formation, the “leader-follower” formation control law is investigated. First, the distributed extended state observer (DESO) is designed such that the followers could estimate the virtual leader’s position and velocity. Then, the expected positions of the followers are calculated based on the observer outputs and the nominal formation configuration. A dynamic surface control-based position tracking control law is designed for the followers to track the expected positions. Based on the Lyapunov theory, the stability of the proposed method is proved, while numerical simulations validate the effectiveness. The DESO could estimate both the virtual leader’s position and velocity via only the position observations. The method proposed guarantees that the orientation of the formation is consistent with the direction of the virtual leader’s velocity.

In this paper, a leader-model-follower matching-based distributed adaptive cooperative control scheme is developed by equivalenting abrupt changing topology to switching topology problem for uncertain heterogeneous multi-agent systems with abrupt changing communication topology to realize leader-follower output consensus. First, local output tracking error is proposed to transform the leader-follower global output consensus problem into the neighboring agents local output consensus problem. Then, the distributed nominal cooperative control design is performed with known system parameters to realize reference model-leader matching and follower-reference model matching, to ensure the leader-follower output consensus with abrupt changing communication topology. Afterwards, the distributed adaptive cooperative controller is studied to realize the asymptotic output tracking of the follower to the leader with unknown parameters under the abrupt change of communication topology. The designed control strategy can ensure the closed loop stabilization of the global agents as well as the leader-follower output consensus with switching topology without relying on global information. Finally, the effectiveness of the designed control scheme is verified by simulation.

The visual assistance driving system for civil aviation aircraft captures information about the surrounding threat scenario using airborne visual sensors, providing pilots with additional information to aid decision-making. However, the threat objects in the airfield obtained by the optical sensors on the airborne differ significantly in scale, and the computing capacity of the onboard platform is limited. Current methods for object detection do not meet the requirements for visual assistance in driving scenarios. To address this issue, a lightweight multi-scale object detection algorithm based on YOLOv5s is proposed. First, the CA-BIFPN feature fusion network is designed by combining the weighted bidirectional feature pyramid network (BIFPN) with the coordinate attention (CA) attention mechanism, which aims to enhance the feature expression of small objects and to improve the capacity of the model to learn multi-scale objects. Then, the GSConv decoupled detection head is designed to improve object detection accuracy by making classification and regression independent. To enhance the detection speed of the network and enable real-time detection of airfield objects, a cross-level partial lightweight neck module is designed to reduce the additional parameters introduced by the decoupled head. A self-built multi-scale airfield object dataset containing real-world and simulated data from airborne visual sensors from a civil aviation aircraft perspective is established to verify the performance of the proposed algorithm. The experiments conducted on this dataset demonstrate that the detection accuracy of the proposed algorithm surpasses that of faster R-CNN, SSD, and other classic multi-scale object detection algorithms like YOLOv6, YOLOv7, and YOLOX. The achieved mean average precision is 71.40%, which is 4.19% higher than that of YOLOv5s. Furthermore, the detection frame rate achieves 71 frame per second on the simulated airborne computing platform, which satisfies the real-time detection requirements.

To solve the problem of inadequate perception of autonomous driving in occlusion and over-the-horizon scenarios, a vehicle-road collaborative perception method based on a dual-stream feature extraction network is proposed to enhance the 3D object detection capabilities of traffic participants. Feature extraction networks for roadside and vehicle-side scenes are tailored based on respective characteristics. Since roadside has rich and sufficient sensing data and computational resources, the Transformer structure is used to extract more sophisticated and advanced feature representations. Due to limited computational capability and high real-time demands of autonomous vehicles, partial convolution (PConv) is employed to enhance computing efficiency, and the Mamba-VSS module is introduced for efficient perception in complex environments. Collaborative perception between vehicle-side and roadside is accomplished through the selective sharing and fusion of critical perceptual information guided by confidence maps. By training and testing on DAIR-V2X dataset, the model size of vehicle-side feature extraction network is obtained to be 8.1 MB, and the IoU thresholds of 0.5 and 0.7 correspond to the average accuracy indexes of 67.67% and 53.74%. The experiment verifies the advantages of this method in detection accuracy and model size, and provides a lower-configuration detection scheme for vehicle-road collaboration.