TY - GEN
T1 - Sparse incomplete LU-decomposition for wave farm designs under realistic conditions
AU - Arbonès, Dídac Rodríguez
AU - Sergiienko, Nataliia Y.
AU - Ding, Boyin
AU - Krause, Oswin
AU - Igel, Christian
AU - Wagner, Markus
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Multi-objective optimisation
KW - Ocean wave energy
KW - Simulation speed-up
KW - Wave energy converter array
UR - http://www.scopus.com/inward/record.url?scp=85053630637&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99253-2_41
DO - 10.1007/978-3-319-99253-2_41
M3 - Article in proceedings
AN - SCOPUS:85053630637
SN - 9783319992525
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 512
EP - 524
BT - Parallel Problem Solving from Nature – PPSN XV
PB - Springer
T2 - 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018
Y2 - 8 September 2018 through 12 September 2018
ER -