Floating Wind Turbine Mooring Optimization Utilizing Multi-Objective Evolutionary Algorithm
Ajie B. K. Pribadi, Gael Verao Fernandez, Luca Donatini, Evert Lataire (Ghent University)
An open-source Python library for multi-objective optimization, namely Pymoo has been utilized to perform design optimization of a mooring system for the floating wind turbine (FWT) DeepCwind OC4 platform. Time-domain simulation is performed using the adapted version of MoorDyn, open-source lumped-mass based mooring dynamic solver. Modifications are done to include the contributions of waves and current on line elements via the Morison Equation. Hydrodynamics of a rigid body is included in the Adapted-MoorDyn using Cummins’ equation and Morison drag. Finding the minimum platform’s peak surge motion and the minimum cost of mooring lines are the objectives of the optimization study. Two constraints are set for the evaluation: i) peak tension is less than 60% of the mooring line’s minimum breaking load divided (or a safety factor of 1.67) and ii) peak surge motion is less than the results of the initial configuration. The optimization study is set to terminate after 16 generations to which each generation contains 32 individuals (i.e., total 512 mooring configurations). New population of the mooring configurations is generated utilizing Pymoo’s Non-dominated Sorting Genetic Algorithm (NSGA-II). The evaluation of the objectives and constraints are based on the Adapted-MoorDyn simulation results. Three pareto front solutions are obtained as the results of the optimization study. Solution 2 has the total mooring lines cost seven times less than the initial configuration while reducing peak surge motion by 16 %. On the other hand, Solution 1 reduced the peak surge motion by 1.8 times while the total cost is reduced by five times. The computational time spent for this study is 0.04 second per 1 second of simulation time performed on a 12th Gen Intel® Core™ i7-12700 H utilizing 16 threads.
