  A promising direction towards improving the performance of wave energy
converter (WEC) farms is to leverage a system-level integrated approach known
as control co-design (CCD). A WEC farm CCD problem may entail decision
variables associated with the geometric attributes, control parameters, and
layout of the farm. However, solving the resulting optimization problem, which
requires the estimation of hydrodynamic coefficients through numerical methods
such as multiple scattering (MS), is 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.
