Motion responses of a catenary–taut–tendon hybrid moored single module of a semisubmersible-type VLFS over uneven seabed

Wang, Yiting; Xu, Shengwen; Wang, Lei; Wang, Xuefeng

doi:10.1007/s00773-018-0587-6

Cited by 9 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A very large floating structure (VLFS) as a semi-submersible platform with a scale factor of 1:100 has been employed to carry out the free decay tests, regular wave tests, and whitenoise wave tests in the Ocean Engineering Basin at Shanghai Jiao Tong University. These structures are normally used to compensate for land shortages in congested areas (Yiting Wang, 2019), and the present model mimics a real platform, which will be deployed in the Southern China Sea. Considering the size of the basin, which is 50 m × 30 m × 6 m, the scale ratio was adopted to ensure that the model tests accurately represented the real-world conditions while being feasible within the size limitations of the basin.…”

Section: Physical Model Testmentioning

confidence: 99%

Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Majidiyan,

Enshaei,

Howe

et al. 2024

Preprint

View full text Add to dashboard Cite

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. Despite extensive research on the wave buoy analogy (WBA), real-time sea state estimation (SSE) has remained challenging due to the need for adequate data to construct a consistent probability density function of waves. 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 form for subsequent data analysis. To achieve this, an adaptive ship response predictor and deep learning approach have been incorporated to classify seaway data while minimizing network complexity through feature engineering. Specifically, a Convolution Neural Network (CNN) and deep transfer learning have been employed for training purposes. 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 proposed approach for a fully automated system. Furthermore, the deficits with the feature transformation of the existing data-driven SSE models have been discussed. This study provides a foundation for improving online sea state estimation and promoting the seaway acquisition for stationary marine units.

show abstract

Section: Physical Model Testmentioning

confidence: 99%