Physics-constrained Gaussian process model for prediction of hydrodynamic interactions between wave energy converters in an array
Physics-constrained Gaussian process model for prediction of hydrodynamic interactions between wave energy converters in an array
复制标题
用于预测阵列中波浪能转换器之间的流体动力相互作用的物理约束高斯过程模型
DOI：
10.1016/j.apm.2023.03.003
复制
发表时间：
2023
影响因子：
5
通讯作者：
Tom, Nathan
中科院分区：
文献类型：
--
作者：
Li, Min;Jia, Gaofeng;Mahmoud, Hussam;Yu, Yi-Hsiang;Tom, Nathan
为了提高波浪发电场的效率并实现最大发电量，需要仔细设计波浪能转换器（WEC）阵列中的布局，以便能够积极地利用水动力相互作用。为此，需要计算不同布局下WEC阵列的水动力特性。然而，使用数值模型的这种计算通常需要显著的计算成本，特别是对于大型WEC阵列。为了解决计算的挑战，物理约束的高斯过程（GP）模型，提出了取代原来昂贵的数值模型和预测的水动力特性的WEC的任何阵列布局。通过探索WEC阵列（即，输入）和不同的流体动力学特性（即，输出），我们总结了一组物理约束/特征，包括不变性，对称性和可加性。然后，通过设计物理约束核，将关于输入-输出关系的先验知识直接嵌入到构造的GP模型中。特别是，双和不变的核心是第一次开发，将不变性和对称性的功能，然后添加剂的核心开发，将添加剂的功能的问题。然后将不变核和可加核结合起来构造物理约束GP模型。与标准GP模型相比，所提出的物理约束GP模型需要较少的训练数据来实现预测流体动力学特性的期望精度，并且也不易受到维数灾难（即，对于大阵列具有良好的可伸缩性）。所提出的方法的效率，准确性和可扩展性证明通过应用程序来预测不同尺寸和布局的WEC阵列的流体动力学特性。
To improve the efficiency of wave farms and achieve maximum power generation, the layout of wave energy converters (WECs) in an array needs to be carefully designed so that the hydrodynamic interactions can be positively exploited. For this, the hydrodynamic characteristics of the WEC array in different layouts need to be calculated. However, such calculations using numerical models usually entail significant computational cost, especially for large arrays of WECs. To address the computational challenge, a physics-constrained Gaussian process (GP) model is proposed to replace the original expensive numerical model and predict the hydrodynamic characteristics of the WECs for any array layout. By exploring the relationship between the WEC array (i.e., the input) and different hydrodynamic characteristics (i.e., the output), we summarize a set of physical constraints/features, including invariance, symmetry, and additivity. This prior knowledge about the input-output relationship is then directly embedded in the constructed GP model through the design of physics-constrained kernels. In particular, a double-sum invariant kernel is first developed to incorporate the invariance and symmetry features, and then an additive kernel is developed to incorporate the additive feature of the problem. The invariant kernel and the additive kernel are then integrated to construct the physics-constrained GP model. Compared to the standard GP model, the proposed physics-constrained GP models require less training data to achieve the desired accuracy in predicting the hydrodynamic characteristics and are also less vulnerable to the curse of dimensionality (i.e., good scalability for large arrays) due to the use of an additive kernel. The efficiency, accuracy, and scalability of the proposed approach are demonstrated through an application to predict the hydrodynamic characteristics for WEC arrays of different sizes and layouts.