Hydrodynamic performance and multi-objective optimization of multi-cylinder floating point absorber wave energy converter

This study aims to analyze the hydrodynamic performance and conduct multi-objective optimization of a Multi-Cylinder Floating Point Absorber Wave Energy Converter (WEC) to enhance wave energy utilization at Pelabuhanratu, West Java, Indonesia. The research focuses on three key geometrical parameters of the truncated cone and cylindrical floater: outer radius, bottom radius, and draft, with wave data serving as boundary conditions for the Design of Experiments (DoE). Optimization was carried out using Response Surface Methodology (RSM), Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), and Multi-Criteria Decision Making (MCDM) to balance high Capture Width Ratio (CWR) and low cost, accounting for surge, heave, and pitch motions. The study reveals that the outer radius significantly affects the response, with a p-value of 0.0151, indicating a statistically significant effect at the 5% significance level. The best hydrodynamic simulation from DoE produced an optimal CWR of 0.707 at 50.707 USD. Meanwhile, ANN-MOGA optimization achieved the highest CWR of 0.865 with a cost of 53.179 USD, improving efficiency by 22.3% with only a 4.9% cost increase. This demonstrates that ANN-MOGA offers superior efficiency in balancing performance and cost compared to other optimization methods. • This study optimizes a Multi-Cylinder Floating Point Absorber Wave Energy Converter for enhanced wave energy use at Pelabuhanratu. • The research examines key geometrical parameters: outer radius, bottom radius, and draft, using wave data for DoE. • Optimization methods like RSM, ANN, MOGA, and MCDM balance high Capture Width Ratio (CWR) and low cost. • The outer radius strongly affects performance with a p-value of 0.0151. • ANN-MOGA outperforms other methods in balancing performance and cost.