用贝叶斯动态模态分解实现船舶运动实时预测与不确定性评估
Bayesian dynamic mode decomposition for real-time ship motion digital twinning
- 基于贝叶斯框架改进动态模态分解，适应实时数据流
- 在五倍波浪遭遇周期内保持高精度预测，提升确定性模型表现
- 可为海军数字孪生提供可信度评估，适合海上安全决策
数字孪生被视为新一代产品设计、运行与维护变革的关键技术，旨在提供可靠及时的预测以支持全生命周期决策。在船舶领域，海浪中船体运动的数字孪生尤为关键，关乎设计与运行安全。本文提出一种贝叶斯扩展的汉克尔动态模态分解方法，用于船舶运动的即时预报，作为海军数字孪生的预测工具。该算法满足数字孪生所有要求：能根据物理系统实时传入的数据更新模型，仅需少量数据，实现实时预测并估计预测可靠性。在傅汝德数0.33、横摇-斜浪状态7不规则波下，针对5415M船模的航向保持性能进行测试，使用三种不同CFD求解器的数据。结果表明，预测在长达五倍波浪遭遇周期内仍保持良好精度，且贝叶斯形式优于确定性预测；同时发现预测不确定性与准确度之间存在关联。
原文摘要 · Abstract (English)
Digital twins are widely considered enablers of groundbreaking changes in the development, operation, and maintenance of novel generations of products. They are meant to provide reliable and timely predictions to inform decisions along the entire product life cycle. One of their most interesting applications in the naval field is the digital twinning of ship performances in waves, a crucial aspect in design and operation safety. In this paper, a Bayesian extension of the Hankel dynamic mode decomposition method is proposed for ship motion's nowcasting as a prediction tool for naval digital twins. The proposed algorithm meets all the requirements for formulations devoted to digital twinning, being able to adapt the resulting models with the data incoming from the physical system, using a limited amount of data, producing real-time predictions, and estimating their reliability. Results are presented and discussed for the course-keeping of the 5415M model in beam-quartering sea state 7 irregular waves at Fr = 0.33, using data from three different CFD solvers. The results show predictions keeping good accuracy levels up to five wave encounter periods, with the Bayesian formulation improving the deterministic forecasts. In addition, a connection between the predicted uncertainty and prediction accuracy is found.
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