The present paper aims to establish a systematic robust optimization framework for the hydrodynamic performance of marine current turbines against uncertain conditions. To this end, we developed a novel robustness criterion based on the cumulative distribution function and employed an XFOIL-based blade element momentum method to predict turbine performance. The non-intrusive polynomial chaos expansion was also applied for uncertainty propagation and probabilistic computations. Initially, a set of sensitivity analysis studies was conducted on a model turbine to determine the most influential parameters. The results indicated that uncertainties have a significant impact on turbine performance, even under controlled experimental conditions. In the next stage, we applied deterministic and robust optimization approaches to optimize a real-world marine turbine design under practical conditions. We utilized a hybrid evolutionary optimization algorithm based on the Genetic Algorithm and Particle Swarm Optimization. Additionally, the novel robustness criterion was introduced to address the issues associated with conventional criteria. The robust optimization was performed independently using three criteria: the worst-case scenario, the mean value penalty, and the novel criterion. By investigating the probabilistic characteristics of the performance in the deterministic and robust optimum turbines, we demonstrated that the power generation capacity of the deterministic optimum is expectedly higher than that of the robust optimum in deterministic hypothetical conditions. However, during turbine operation in real conditions, which are accompanied by large uncertainties, the optimum design achieved using the new robustness criterion is more likely to produce greater power. Furthermore, the power variations of the turbine are more limited, indicating increased reliability and stability of the optimized design. |
