[论文解读] Multifidelity digital twin for real-time monitoring of structural dynamics in aquaculture net cages

Eirini Katsidoniotaki, Biao Su+2 more|arXiv (Cornell University)|Jun 6, 2024

Water Quality Monitoring Technologies被引用 5

一句话总结

该论文开发了一个使用非线性自回归高斯过程（NARGP）的多保真度代理建模框架，用于构建实时监测养殖网箱动力学的数字孪生，在全尺度的 SINTEF ACE 养场验证，并且将 GP-PCA 代理与图卷积网络（GCN）进行比较。

ABSTRACT

As the global population grows and climate change intensifies, sustainable food production is critical. Marine aquaculture offers a viable solution, providing a sustainable protein source. However, the industry's expansion requires novel technologies for remote management and autonomous operations. Digital twin technology can advance the aquaculture industry, but its adoption has been limited. Fish net cages, which are flexible floating structures, are critical yet vulnerable components of aquaculture farms. Exposed to harsh and dynamic marine environments, the cages experience significant loads and risk damage, leading to fish escapes, environmental impacts, and financial losses. We propose a multifidelity surrogate modeling framework for integration into a digital twin for real-time monitoring of aquaculture net cage structural dynamics under stochastic marine conditions. Central to this framework is the nonlinear autoregressive Gaussian process method, which learns complex, nonlinear cross-correlations between models of varying fidelity. It combines low-fidelity simulation data with a small set of high-fidelity field sensor measurements, which offer the real dynamics but are costly and spatially sparse. Validated at the SINTEF ACE fish farm in Norway, our digital twin receives online metocean data and accurately predicts net cage displacements and mooring line loads, aligning closely with field measurements. The proposed framework is beneficial where application-specific data are scarce, offering rapid predictions and real-time system representation. The developed digital twin prevents potential damages by assessing structural integrity and facilitates remote operations with unmanned underwater vehicles. Our work also compares GP and GCNs for predicting net cage deformation, highlighting the latter's effectiveness in complex structural applications.

研究动机与目标

• 激励在恶劣海洋环境下的海上养殖网箱的实时监控和自主运营需求。
• 提出一个融合低保真仿真与稀缺高保真现场数据的多保真度代理建模框架。
• 在全尺度养鱼场实现并验证数字孪生，以预测笼体位移和系泊载荷。
• 评估非线性自回归高斯过程（NARGP）是否能够高效融合不同保真度水平实现实时预测。

提出的方法

• 使用 NARGP 学习低保真仿真与高保真传感数据之间的非线性互相关系。
• 从 FhSim 仿真训练低保真代理，将电流映射到系泊载荷和笼体位移。
• 应用 PCA 降低网箱变形输出用于 GP 建模，然后重构完整变形。
• 在带实时海洋气象输入的递归多保真框架中融合低保真和高保真 GP 模型。
• 将带 PCA 预处理的高斯过程代理与图卷积网络（GCNs）在网箱拓扑预测方面进行比较。
• 评估实现即时数字孪生操作的计算效率。

实验结果

研究问题

• RQ1 多保真代理框架是否能够在随机海洋气象条件下准确预测网箱位移和系泊载荷？
• RQ2 图卷积网络是否相较基于高斯过程的代理在预测复杂网箱变形拓扑方面更具优势？
• RQ3 以有限高保真数据和大量低保真仿真实现实时数字孪生的性能是否可行？
• RQ4 海洋气象数据的整合如何在全尺度养殖场中提升基于代理的预测？

主要发现

• 多保真 GP 模型显著提升了系泊线载荷的预测，相对于低保真模型。
• 多保真框架的预测与未见现场测量的系泊载荷高度一致。
• 对于网箱位移，多保真方法在随时间与传感数据的一致性上优于低保真 GP。
• GCNs 可以有效处理网箱的复杂拓扑，并在变形预测方面与基于 GP 的代理相竞争。
• 该工作流实现实时监控，支持远程自主操作，减少传感器部署与维护。