Mooring system design optimization using a surrogate assisted multi-objective genetic algorithm

Abstract

This article presents a novel framework for the multi-objective optimization of offshore renewable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a floating offshore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the offshore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally efficient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of optimal solutions characterizing how design changes impact the trade-off between these two competing objectives.

Hull geometry optimisation of wave energy converters: On the choice of the optimisation algorithm and the geometry definition

“…These systems have been extensively studied and optimised, and some studies exist which simultaneously optimise geometry and PTO-parameters, such as the one previously mentioned by Gilloteaux et al [7]. Due to their potential impact on system dynamics and structural loads mooring lines have also been optimised, for example, in [13].…”

“…13: Mean Best Fitness (MBF) and Success Rate (SR) for all algorithms applied to objective function f 3 = − P /V for a surging, heaving and pitching device.…”

Development of a Multi-Objective Optimization Tool for Screening Designs of Taut Synthetic Mooring Systems to Minimize Mooring Component Cost and Footprint

“…This includes checking the tension both at the chain fairlead and in the synthetic section to ensure that the line tension is below the line MBS with the appropriate ABS/IEC factors of safety, as well as ensuring that the tension in the synthetic section does not go slack. The constraint violations for the tension in the chain leader are given by Equation ( 8) and the constraint violation for the maximum tension in the synthetic segment is given by Equation (9). The last of the constraints to be checked is the minimum synthetic tension requirement given in Equation (10).…”

“…Another approach that has been attempted is to train a surrogate-model using many time domain simulations [8][9][10]. With this approach potential mooring systems throughout the design space are simulated in the time-domain.…”

Review of Wave Energy Converter and Design of Mooring System

“…This methodology examines the positioning of the anchors along with the length, diameter, and material of the mooring cables to reduce the tension in the cables, mooring system costs, and the risk of fatigue into the system at the same time-the multi-objective strategy used for these objectives instead of limiting to a single target optimization problem. Pillai et al [198] proposed a new method for multi-objective optimization of mooring systems that used a randomized forest-based surrogate methodology combined with the genetic algorithm-minimizing mooring system costs as well as cumulative fatigue risk. Li et al [199] introduced an integrated methodology for optimizing mooring system design.…”