Sparse incomplete LU-decomposition for wave farm designs under realistic conditions

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Wave energy is a widely available but still largely unexploited energy source, which has not yet reached full commercial development. A common design for a wave energy converter is called a point absorber (or buoy), which either floats on the surface or just below the surface of the water. Since a single buoy can only capture a limited amount of energy, large-scale wave energy production requires the deployment of buoys in large numbers called arrays. However, the efficiency of these arrays is affected by highly complex constructive and destructive intra-buoy interactions. We tackle the multi-objective variant of the buoy placement problem: we are taking into account the highly complex interactions of the buoys, while optimising critical design aspects: the energy yield, the necessary area, and the cable length needed to connect all buoys – while considering realistic wave conditions for the first time, i.e., a real wave spectrum and waves from multiple directions. To make the problem computationally feasible, we use sparse incomplete LU decomposition for solving systems of equations, and caching of integral computations. For the optimisation, we employ modern multi-objective solvers that are customised to the buoy placement problems. We analyse the wave field of final solutions to confirm the quality of the achieved layouts.

TitelParallel Problem Solving from Nature – PPSN XV : 15th International Conference, 2018, Proceedings
Antal sider13
Publikationsdato1 jan. 2018
ISBN (Trykt)9783319992525
StatusUdgivet - 1 jan. 2018
Begivenhed15th International Conference on Parallel Problem Solving from Nature, PPSN 2018 - Coimbra, Portugal
Varighed: 8 sep. 201812 sep. 2018


Konference15th International Conference on Parallel Problem Solving from Nature, PPSN 2018
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind11101 LNCS

ID: 203377660