Vital to the progression of the wave energy industry is wave energy converter optimization which often relies on frequency-domain evaluations, utilized for efficiency as compared to time-domain. Despite being an integral factor in the resultant solution, the frequency array is rarely described in such studies. This study shows the impacts of the frequency array components and illustrates a general process by which to select a proper frequency array. The main factors to consider are the range and number of frequencies. Furthermore, this study introduces irregular wave phase realizations and suggests the importance of optimizing the system for multiple random sets of wave component phase. Ultimately, the importance of proper selection of both the frequency array and wave phase realizations to the optimization solution is demonstrated for the Pioneer WEC using WecOptTool.

Researchers are exploring adding wave energy converters to existing oceanographic buoys to provide a predictable source of renewable power. A ”pitch resonator” power take-off system has been developed that generates power using a geared flywheel system designed to match resonance with the pitching motion of the buoy. However, the novelty of the concept leaves researchers uncertain about various design aspects of the system. This work presents a novel design study of a pitch resonator to inform design decisions for an upcoming deployment of the system. The assessment uses control co-design via WecOptTool to optimize control trajectories for maximal electrical power production while varying five design parameters of the pitch resonator. Given the large search space of the problem, the control trajectories are optimized within a Monte Carlo analysis to identify optimal designs, followed by parameter sweeps around the optimum to identify trends between the design parameters. The gear ratio between the pitch resonator spring and flywheel are found to be the most sensitive design variables to power performance. The assessment also finds similar power generation for various sizes of resonator components, suggesting that correctly designing for optimal control trajectories at resonance is more critical to the design than component sizing.

A “bolt-on” wave energy converter is designed to provide power for sensors on an existing oceanographic buoy. The narrow-banded pitch/roll response of the target oceanographic buoy lends itself to a tuned-resonator design, for which we suggest a novel “pitch resonator” wave energy converter concept. Using a pseudo-spectral method, the performance of the proposed wave energy converter is modeled in the range of sea states expected to be present at the target deployment location to study the effect of flywheel inertia on performance. The results show that the system can marginally meet the desired power demands, but suggest that related design concepts may be worth consideration.

The open-source WecOptTool was developed to make wave energy converter (WEC) control co-design accessible. WecOptTool is based on the pseudo-spectral method which is capable of efficiently dealing with any linear or nonlinear constraints and nonlinear dynamics by solving the WEC optimal control problem in the time domain using a gradient based optimization algorithm. This work1 presents a control co-optimization study of the AquaHarmonics Inc. heaving point absorber WEC sized for ocean deployment to solve practical industry design problems. Components such as the specific type of generator, the hull shape, and the displaced volume are pre-determined. We co-optimize the WEC’s mass versus mooring line pretension in conjunction with the controller. The optimization is subject to the power-take-off (PTO) dynamics and the rated constraints of the components. In particular, the continuous torque rating is implemented as an explicit constraint, a novel approach for WEC optimization. The PTO dynamics are incorporated into the optimization algorithm via a combination of first principle methods (linear drivetrain model) and empirical efficiency maps (electrical generator) represented as a power loss map. This is a practical method applicable to a variety of PTO architectures and transferable to other WECs. A discussion between using an efficiency coefficient versus a power loss map and their implication for the optimization method is presented. This application of WecOptTool represents a real world WEC by combining simplified models with empirical efficiency data. The WEC, as a dynamically coupled, oscillatory system, requires consideration of the time trajectory dependent power loss for optimizing the average electrical power. This objective function, the modelling approach, and the realistic loss terms makes the common practice of artificially penalizing the reactive power needless.

Vertical-axis wind turbines (VAWTs) have a long history, with a wide variety of turbine archetypes that have been designed and tested since the 1970s. While few utility-scale VAWTs currently exist, the placement of the generator near the turbine base could make VAWTs advantageous over tradition horizontal-axis wind turbines for floating offshore wind applications via reduced platform costs and improved scaling potential. However, there are currently few numerical design and analysis tools available for VAWTs. One existing engineering toolset for aero-hydro-servo-elastic simulation of VAWTs is the Offshore Wind ENergy Simulator (OWENS), but its current modeling capability for floating systems is non-standard and not ideal. This article describes how OWENS has been coupled to several OpenFAST modules to update and improve modeling of floating offshore VAWTs and discusses the verification of these new capabilities and features. The results of the coupled OWENS verification test agree well with a parallel OpenFAST simulation, validating the new modeling and simulation capabilities in OWENS for floating VAWT applications. These developments will enable the design and optimization of floating offshore VAWTs in the future.

Control co-design has been shown to significantly improve the performance of wave energy converters (WEC). By considering the control and WEC design concurrently, the space searched by the optimization routine is greatly expanded which results in better performing devices. Recently, an open-source WEC co-design code, WecOptTool, was released to perform control co-design research and facilitate its adoption in the community. In this study, we use WecOptTool to perform control co-optimization and uncertainty analysis of the LUPA device. The Laboratory Upgrade Point Absorber (LUPA) is a new open-source laboratory-scale WEC that provides a platform for testing new concepts, innovating control schemes, and validating numerical models. The LUPA can be adjusted to different configurations, including changing the number of bodies, the degrees of freedom (DOF), the float and spar geometry, and the diameter of the drive sprocket pulley in the power take off (PTO) system, as well as providing different control algorithms and input waves. The drive sprocket diameter influences the torque vs speed of the generator, which allows for more flexibility in operating under different wave conditions or with different control schemes. In this study we optimize the drive sprocket diameter, while considering the optimal control algorithm for each potential design, to identify the optimal diameter for electric power production at the PacWave South WEC test site. This case study demonstrates several new capabilities of WecOptTool including a multi-body multi-DOF system and multi-directional irregular waves. The PTO dynamics are modeled using first principle methods for a parametrized model of the mechanical subcomponents in combination with generator model obtained using a power-invariant Park transform. The case-study will be made available to serve as a design tool along the LUPA hardware. Users can readily use this model to perform their own design optimization prior to testing with the physical LUPA device. Finally, we use the automatic differentiation capability of WecOptTool to perform a sensitivity and uncertainty analysis of the LUPA device.

Commercial floating offshore wind projects are expected to emerge in the U.S. by the end of this decade. Currently, however, high costs for the technology limit its commercial viability, and a lack of data regarding system reliability heightens project risk. This work presents an optimization algorithm to examine the tradeoffs between cost and reliability for a floating offshore wind array that uses shared anchoring. Combining a multivariable genetic algorithm with elements of Bayesian optimization, the optimization algorithm selectively increases anchor strengths to minimize the added costs of failure for a large floating wind farm in the Gulf of Maine under survival load conditions. The algorithm uses an evaluation function that computes the probability of mooring system failure, then calculates the expected maintenance costs of a failure via a Monte Carlo method. A cost sensitivity analysis is also performed to compare results for a range of maintenance cost profiles. The results indicate that virtually all of the farm's anchors are strengthened in the minimum cost solution. Anchor strength is increased between 5 and 35% depending on farm location, with anchor strength nearest the export cable being increased the most. The optimal solutions maintain a failure probability of 1.25%, demonstrating the tradeoff point between cost and reliability. System reliability was found to be particularly sensitive to changes in turbine costs and downtime, suggesting further research into floating offshore wind turbine failure modes in extreme loading conditions could be particularly impactful in reducing project uncertainty.

Energy from floating offshore wind could provide substantial power generation if further utilized, but design optimization is required for the concept to achieve financial viability. A method proposed to reduce substructure costs for floating offshore wind turbines is to connect mooring lines from multiple turbines to a single anchor. However, the complex coupling of components in this system presents a reduction in system reliability of the wind array due to the chance of cascading failure in survival load cases. A proposed hypothesis to correct without drastically increasing costs is to strengthen a small number of important anchors significantly more than the rest. A noise-resistant optimization algorithm was developed using elements of genetic algorithms and Bayesian optimization to identify the optimal anchors to strengthen to improve system reliability. A previously developed simulation that evaluates the reliability of a hypothetical floating wind array with multiline anchors in the loading scenario of a 500-year storm was used as an objective function. While the resultant reliability values were uncompetitive compared to slightly strengthening all anchors, analyzed trends showed opportunity for the concept to work if a higher number of anchors are overstrengthened.