基于代理模型的概率不规则波浪中波能转换器场的几何、控制与布局联合优化

提高波能转换器（WEC）场性能的一个有前景的方向是利用称为控制共设计（CCD）的系统级集成方法。WEC场CCD问题可能涉及与几何属性、控制参数和场布局相关的决策变量。然而，求解 resulting optimization problem requires the estimation of hydrodynamic coefficients through numerical methods such as multiple scattering (MS)， computationally prohibitive. To mitigate this computational bottleneck, we construct data-driven surrogate models (SMs) using artificial neural networks in combination with concepts from many-body expansion. The resulting SMs, developed using an active learning strategy known as query by committee, are validated through a variety of methods to ensure acceptable performance in estimating the hydrodynamic coefficients, (energy-related) objective function, and decision variables. To rectify inherent errors in SMs, a hybrid optimization strategy is devised. It involves solving an optimization problem with a genetic algorithm and SMs to generate a starting point that will be used with a gradient-based optimizer and MS. The effectiveness of the proposed approach is demonstrated by solving a series of optimization problems with increasing levels of integration. For a layout optimization study, the framework offers a 91-fold increase in computational efficiency compared to MS. Previously unexplored investigations of much further complexity are also performed, leading to a concurrent geometry, control, and layout optimization of WEC devices in probabilistic irregular waves. The scalability of the method is evaluated by increasing the farm size to include 25 devices. The results indicate promising directions toward a practical framework for integrated WEC farm design with more tractable computational demands.