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<p>The joint probabilistic modeling of mean wind speed, significant wave height, and spectral peak period is crucial for accurately evaluating long-term combined wind and wave loads on marine structures. The physical mechanisms governing wave generation and propagation impose a critical constraint between wave height and period, beyond which wave breaking occurs. This constraint significantly influences the joint probability distribution, resulting in an asymmetric dependence structure. To effectively incorporate this constraint, truncated copula models have been proposed and demonstrated to be highly effective in constructing joint distributions of wave parameters. This study integrates C-vine copula framework with the truncated copula model to develop a physically constrained joint probability model for wind and wave parameters. Specifically, methodologies for joint distribution modeling and sampling are developed for two common scenarios: one where the dependence structure is dominated by wave height, and another dominated by wind speed. Using observed met-ocean data, joint distributions are established and compared across different methods. Results show that the proposed approach enhances modeling accuracy and guarantees that all generated samples adhere to the wave-breaking constraint. This study provides a theoretical foundation for accurate assessment of wind and wave loads on marine structures.</p>

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Embedding physical constraints into C-vine copulas for joint probabilistic modeling of wind-wave parameters

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Yupeng Song,
  • Aowei Qing,
  • Jinju Tao

摘要

The joint probabilistic modeling of mean wind speed, significant wave height, and spectral peak period is crucial for accurately evaluating long-term combined wind and wave loads on marine structures. The physical mechanisms governing wave generation and propagation impose a critical constraint between wave height and period, beyond which wave breaking occurs. This constraint significantly influences the joint probability distribution, resulting in an asymmetric dependence structure. To effectively incorporate this constraint, truncated copula models have been proposed and demonstrated to be highly effective in constructing joint distributions of wave parameters. This study integrates C-vine copula framework with the truncated copula model to develop a physically constrained joint probability model for wind and wave parameters. Specifically, methodologies for joint distribution modeling and sampling are developed for two common scenarios: one where the dependence structure is dominated by wave height, and another dominated by wind speed. Using observed met-ocean data, joint distributions are established and compared across different methods. Results show that the proposed approach enhances modeling accuracy and guarantees that all generated samples adhere to the wave-breaking constraint. This study provides a theoretical foundation for accurate assessment of wind and wave loads on marine structures.