James T. Allison

Saeid Bayat, Yong Hoon Lee, James T. Allison

Floating offshore wind turbine (FOWT) systems involve several coupled physical analysis disciplines, including aeroelasticity, multi-body structural dynamics, hydrodynamics, and controls. Conventionally, physical structure (plant) and control design decisions are treated as two separate problems, and generally, control design is performed after the plant design is complete. However, this sequential design approach cannot fully capitalize upon the synergy between plant and control design decisions. These conventional design practices produce suboptimal designs, especially in cases with strong coupling between plant and control design decisions. Control co-design (CCD) is a holistic design approach that accounts fully for plant-control design coupling by optimizing these decisions simultaneously. CCD is especially advantageous for system design problems with complex interactions between physics disciplines, which is the case for FOWT systems. This paper presents and demonstrates a nested CCD approach using open-loop optimal control (OLOC) for a simplified reduced-order model that simulates FOWT dynamic behavior. This simplified model is helpful for optimization studies due to its computational efficiency, but is still sufficiently rich enough to capture important multidisciplinary physics couplings and plant-control design coupling associated with a horizontal-axis FOWT system with a spar buoy floating platform. The CCD result shows an improvement in the objective function, annual energy production (AEP), compared to the baseline design by more than eleven percent. Optimization studies at this fidelity level can provide system design engineers with insights into design directions that leverage design coupling to improve performance. These studies also provide a template for future more detailed turbine CCD optimization studies that utilize higher fidelity models and design representations.

Elena Fernandez Bravo, Sunil Tamang, Yong Hoon Lee et al.

This work presents a design coupling analysis (DCA) framework to investigate the interactions among control and plant design variables in floating offshore wind turbine (FOWT) and to support the formulation of tractable control co-design (CCD) optimization strategies. DCA provides quantitative information that reveals the relationships and dependencies among design variables and to objective function, enabling improved design variable selection, identification of dominant variables that drive system interactions, and informed selection of optimization solution strategies. However, applying DCA to complex systems is challenging because the models used to describe their dynamics are computationally expensive, and constructing DCA information requires exhaustive model evaluations and optimizations. Here, a surrogate model of the FOWT system is employed to make the repeated model evaluations required for DCA computationally feasible. Using this framework, the bidirectional couplings between control and plant design variables, as well as the couplings among plant design variables, are estimated. The results reveal strong interactions among various design variables, and identify the most influential plant design variables affecting system performance. These insights guide the development of two DCA-based optimization strategies for large CCD problems: a sequential decomposition approach that preserves dominant design variable couplings while reducing problem size at each stage, and a reduced dimensional optimization approach that focuses on collectively the most influential variables. The results demonstrate that these strategies significantly reduce computational complexity while achieving solutions comparable to those obtained through full simultaneous optimization, demonstrating the value of DCA for understanding and solving complex design problems.