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During the towing of semisubmersible platforms, waves impact and superpose in front of the platform to form a ridge shaped “water ridge”, which protrudes near the platform and produces a large slamming pressure. The water ridges occur frequently in the towing conditions of semisubmersible platforms. The wave–slamming on the braces and columns of platform is aggravated due to the water ridges, particularly in rough sea conditions. The effect of water ridges is usually ignored in slamming pressure analysis, which is used to check the structural strengths of the braces and columns. In this paper, the characteristics of the water ridge at the braces of a semisubmersible platform are studied by experimental tests and numerical simulations. In addition, the sensitivity of the water ridge to the wave height and period is studied. The numerical simulations are conducted by a Computational Fluid Dynamics (CFD) method, and their accuracy is validated based on experimental tests. The characteristics of the water ridge and slamming pressure on the braces and columns are studied in different wave conditions based on the validated numerical model. It is found that the wave extrusion is the main reason of water ridge. The wave–slamming pressure caused by the water ridge has an approximately linear increase with the wave height and is sensitive to the wave period. With the increase of the wave period, the wave–slamming pressure on the brace and column of the platform increases first and then decreases. The maximum wave–slamming pressure is found when the wave period is 10 s and the slamming pressure reduces rapidly with an increase of wave period.

Ye, Q.; Jin, W.-L., and Bai, Y., 2020. Time-domain structure analysis of a typical semisubmersible drilling rig. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 118–123. Coconut Creek (Florida), ISSN 0749-0208. This paper evaluates time-domain structure analysis approaches of a semisubmersible drilling rig using a nonlinear finite element method. Hydrodynamic random wave loads are calculated by WVTDUT, which was developed by a research group from the Dalian University of Technology. Time-domain structure analysis of a semisubmersible platform is very time consuming; simplified time-domain analysis approaches are therefore proposed to improve efficiency using equivalent time series. Validity of the approach is tested by a concrete example and simulation results are discussed in detail. The work of this paper can lead to a better understanding of structure behavior of a typical semisubmersible platform under characteristic wave loads. Performing structure analyses in time domain increases the accuracy and reliability of structure performance and the present study may be a benchmark study on time-domain structure analysis of a semisubmersible drilling rig.

In this paper, the multi-body coupled dynamic characteristics of a semisubmersible platform and an HYSY 229 barge were investigated. First, coupled hydrodynamic analysis of the HYSY 229 barge and the semisubmersible platform was performed. Relevant hydrodynamic parameters were obtained using the retardation function method of three-dimensional frequency-domain potential flow theory. The results of the hydrodynamic analysis were highly consistent with the test findings, verifying the accuracy of the multifloating hydrodynamic coupling analysis, and key hydrodynamic parameters were solved for different water depths and the coupling effect. According to the obtained results, the hydrodynamic influence was the largest in shallow waters when the coupling effect was considered. Furthermore, the coupled motion equation combined with viscous damping, fender system, and mooring system was established, and the hydrodynamics, floating body motion, and dynamic response of the fender system were analyzed. Motion analysis revealed good agreement among the surge, sway, and yaw motions of the two floating bodies. However, when the wave period reached 10 s, the motion of the two floating bodies showed severe shock, and a relative motion was also observed. Therefore, excessive constraints should be added between the two floating bodies during construction to ensure construction safety. The numerical analysis and model test results of the semisubmersible platform and HYSY 229 barge at a water depth of 42 m and sea conditions of 0°, 45°, and 90° were in good agreement, and the error was less than 5%. The maximum movement of the HYSY 229 barge reached 2.61 m in the sway direction, whereas that of the semisubmersible platform was 2.11 m. During construction, excessive constraints should be added between the two floating bodies to limit their relative movement and ensure construction safety.

An innovative Bayesian motion-based wave inference method is derived and assessed in this work. The evaluation of the accuracy of the proposed prior distribution has been carried out using the results obtained during a dedicated experimental campaign with a scale model an Oil and Gas (O&G) semisubmersible platform. As for the Bayesian statistical inference approaches, the features of the proposed novel prior distribution, as well as the hypotheses adopted, are discussed. It has been found that significant improvements can be obtained if the new approach is adopted to estimate the sea conditions from measured vessel motions. Finally, it is possible to highlight a substantial reduction of the computing time when the sea conditions are estimated by means of the improved Bayesian method, if compared with the conventional approaches for motion-based wave inference.

A neural-network-based prediction of motion responses of a semi-submersible is presented here. The fully connected neural networks and the long–short-term memory networks were employed to establish the neural networks for motion predictions. The effects of network architectures and time steps were investigated in depth. The predicted results were compared with the measurements and the predictions based on other conventional methods. The results demonstrate that the neural-network-based approach could offer a fast and accurate prediction of heave, roll, and pitch responses of a semi-submersible. This provides a promising alternative for evaluations of hydrodynamic performances of new designs and monitoring of the dynamic behavior of in-service floating structures.

A novel semi-submersible platform is proposed for 5 MW wind turbines. This concept focuses on an integrated system formed by combining porous shells with a semi-submersible platform. A coupled aerodynamic–hydrodynamic–mooring analysis of the new system is performed. The motion responses of the novel platform system and the traditional platform are compared. The differences in hydrodynamic performance between the two platforms are also evaluated. The influence of the geometric parameters (porosity, diameter, and wall thickness) of porous shells on the motion response behavior of the new system is studied. Overall, the new semi-submersible platform exhibits superior stability in terms of pitch and heave degrees of freedom, demonstrating minimal effects on the motion response in the surge degree of freedom.

Recently, more wind turbine systems have been installed in deep waters far from the coast. Several concepts of floating wind turbine systems (FWTS) have been developed, among which, the semi-submersible platform—due to its applicability in different water depths, good hydrodynamic performance, and facility in the installation process—constitutes the most explored technology compared to the others. However, a significant obstacle to the industrialization of this technology is the design of a cost-effective FWTS, which can be achieved by optimizing the geometry, size, and weight of the floating platform, together with the mooring system. This is only possible by selecting a method capable of accurately analyzing the FWTS-coupled hydro–aero–structural dynamics at each design stage. Accordingly, this paper provides a detailed overview of the most commonly coupled numerical and physical methods—including their basic assumptions, formulations, limitations, and costs used for analyzing the dynamics of FWTS, mainly those supported by a semi-submersible—to assist in the choice of the most suitable method at each design phase of the FWTS. Finally, this article discusses possible future research directions to address the challenges in modeling FWTS dynamics that persist to date.

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units.

Floating offshore wind turbines (FOWTs) still face many challenges in improving platform stability. A fully submersible FOWT platform with inclined side columns is designed to tackle the current technical bottleneck of the FOWT platform, combining the structural characteristics of the semi-submersible and Spar platform. An integrated numerical model of FOWT is established considering the fully coupled effect, and the hydrodynamic performance of the novel FOWT, the semi-submersible FOWT, and the Spar FOWT are compared and analyzed under different wave incidence angles and wave frequencies, as well as the blade and tower dynamic response of the three FOWTs under the coupling effect of wind, wave, and current. The results show that the novel floating platform can significantly optimize the hydrodynamic performance and has a better recovery ability after being subjected to external loads. The novel floating platform can significantly reduce the heave peak and its corresponding wave frequency compared to the semi-submersible platform, reducing the possibility of heave resonance. FOWT operation should ensure positive wave inflow as far as possible to avoid excessive wave forces in the lateral direction. Both blade and tower dynamic response are affected by rotor rotation and tower vibration to varying degrees, while tower dynamic response is mainly affected by platform motion. This study suggests that the application of the novel FOWT concept is feasible and can be an alternative in offshore wind exploitation in deep water.

The working condition of the floating platform will be affected by wind and waves in the marine environment. Therefore, it is of great importance to carry out real-time prediction research on the mooring load for ensuring the normal operation of the floating platform. Current researches have focused on the real-time prediction of mooring load using the machine learning method, but most of the studies are about the application and generalization analysis of different models. There are few studies on the influence of data distribution characteristics on prediction accuracy. In view of the above problems, this paper investigates the effect of data skewness on the prediction performance for the deep learning model. The long short-term memory (LSTM) neural network is applied to construct the mooring load prediction model. The numerical simulation datasets of the deep water semi-submersible platform are employed in model training and data analysis. The prediction performance of the model is preliminarily verified based on the simulation results. Meanwhile, the distribution characteristics of mooring load data under different sea states are analyzed and a skewness processing method based on the Box-Cox Transformation (BCT) is proposed. The effect of data skewness on prediction accuracy is further investigated. The comparison results indicate that reducing the mooring load data skewness can effectively improve the prediction accuracy of LSTM model.