Mooring system design optimization using a surrogate assisted multi-objective genetic algorithm. Issue 8 (3rd August 2019)

• Record Type: Journal Article
• Authors: Pillai, Ajit C.; Thies, Philipp R.; Johanning, Lars
• Is Part Of: Engineering optimization. Volume 51:Issue 8(2019)
• Page Start: 1370
• Page End: 1392
• Publication Date: 2019-08-03
• Subjects: Offshore renewable energy -- mooring system design -- surrogate modelling -- multi-objective optimization -- reliability based design optimization; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2018.1519559
• Languages: English
• ISSNs: 0305-215X

Abstract

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.