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Reciprocating bending behavior is the main factor leading to the fatigue failure of dynamic power cables. However, in the research on the reciprocating bending behavior of dynamic cables, only the mechanical properties of the armored steel wire has been considered thus far and the copper conductor is disregarded. In this paper, the nonlinear bending properties of multilayer helically wound copper conductors are studied by experiments and numerical simulation. The research can provide accurate data of nonlinear bending mechanical properties of copper conductor for hydrodynamic analysis of dynamic cable fatigue life. Firstly, the nonlinear bending hysteresis curves of copper conductors are obtained by a reciprocating bending experiment of copper conductor. Secondly three-dimensional finite element numerical simulations of the copper conductor's bending behavior were also conducted. The numerical results from the finite element model were compared with the experimental data. Finally, sensitivity analysis of the structural parameters of the copper conductor was performed. The research shows that copper conductor has obvious nonlinear bending hysteresis behavior. The change of friction coefficient and radial extrusion pressure from the conductor's outer sheath layer have obvious effects on the critical sliding curvature and bending hysteresis properties of nonlinear bending properties of copper conductors. • The bending hysteresis behavior of a multilayer helically wound copper conductor in a dynamic power cable is studied. • The bending hysteresis curve of a copper conductor is obtained by prototype tests. • A 3D finite element model is established to study the bending hysteretic behavior of the copper conductor. • The influence of friction coefficients between copper wires and between copper wire and outer sheath layer on the bending hysteretic behavior is discussed. • The influence of the radial extrusion pressure on the bending hysteretic behavior is discussed. [ABSTRACT FROM AUTHOR]

Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. • Nonlinear buckling simulation of the carcass is carried out and verified by experiment. • The modified buckling theory is proposed based on data-driven method with Kriging algorithm. • The influence of the number and distribution of sample points on the prediction results is analyzed. [ABSTRACT FROM AUTHOR]

The prediction of dynamic mooring line tension using the responses of floating structures is of great significance for the safety monitoring of the station-keeping. Due to the mooring line tension usually has the typical low-frequency and wave-frequency characteristics, this paper proposes a method of Low-frequency adds wave-frequency responses (LAWR) to predict the mooring line tension. Firstly, the optimal Long-short term memory (LSTM) neural network structure is determined through conducting the parameter sensitivity analysis on the performance of the LSTM model. Secondly, the low-frequency and wave-frequency tension are predicted using the corresponding low-frequency and wave-frequency responses as the input data, respectively. Then, the total mooring line tension is predicted by superposing the low-frequency tension and wave-frequency tension. Finally, the feasibility of the LAWR method is verified by considering the different data relevance between the training sets and validating sets, including the sea state condition contains different current direction, wave height and spectral peak frequency. The method proposed in this paper could further improve the prediction accuracy of mooring line tension more than 30%, which has great engineering significance. • A method of Low-frequency adds wave-frequency responses (LAWR) to predict the mooring line tension. • The proposed LAWR method could improve about 30% prediction accuracy. • The influence factors are analyzed to obtain the optimal neural network structure. [ABSTRACT FROM AUTHOR]

The parallel active motion compensated gangway of offshore wind service operation vessel (SOV) is an emerging technology to control the large motion during the transfer process of staff and equipment. The low frequency responses of surge, sway, and yaw motion of SOV are controlled by the DP system, and the other corresponding residual motion responses of the SOV are compensated by the active parallel platform. The modeling and controller design is proposed considering the coupled SOV and the active parallel platform in time domain. The Cummins equation is solved to obtain the motion responses of SOV, and the state-space model is used to replace the convolution part through the system identification method for the fast solution of control algorithm, which is verified by the Bode diagram. The lower surface of active parallel platform is fixed on the SOV, and its responses are obtained by filtering and coordinating transformation of SOV motion responses. The length of each electric cylinder is calculated in real-time by the inverse kinematics of active parallel platform to achieve the motion requirements of upper surface, and the limiting significant wave height of the SOV is determined. • The modeling and controller design is proposed considering the coupled SOV and the active parallel platform in time domain. • The state-space model and inverse kinematics is used to realize the fast and stable motion compensation. • The limiting significant wave height of the SOV is determined by using the established model and controller. [ABSTRACT FROM AUTHOR]

The acceleration at the tower top and the force on the tower root are the critical indicators for the structural safety of floating wind turbine (FOWT) towers. However, the corresponding monitoring sensors are prone to failure due to the harsh marine environment. Based on the coupling relationship between the tower force and the floating foundation motion responses, a method for identifying the tower top acceleration and the tower root force is proposed based on deep learning technology. Firstly, a 10 MW FOWT model is numerically simulated to obtain the corresponding responses through OpenFAST. Secondly, a multi-layer perception (MLP) model is constructed and trained. The process of determining the optimal sampling frequency is presented. Finally, the single-state and multi-state cases are studied to verify the feasibility of the proposed method. The results show that the proposed method has shown excellent performance in identifying the tower top acceleration and tower root force, which demonstrates its great promise in the field of smart health monitoring technology. • A DL method of identifying the tower responses is proposed based on the coupling mechanism of force and motion of FOWTs. • The proposed method has a well performance under the single-state and multi-state case. • The determining process of the appropriate sampling frequency is presented. [ABSTRACT FROM AUTHOR]