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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Sparse incomplete LU-decomposition for wave farm designs under realistic conditions. / Arbonès, Dídac Rodríguez; Sergiienko, Nataliia Y.; Ding, Boyin; Krause, Oswin; Igel, Christian; Wagner, Markus.

Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, 2018, Proceedings. Springer, 2018. s. 512-524 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11101 LNCS).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Arbonès, DR, Sergiienko, NY, Ding, B, Krause, O, Igel, C & Wagner, M 2018, Sparse incomplete LU-decomposition for wave farm designs under realistic conditions. i Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, 2018, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11101 LNCS, s. 512-524, Coimbra, Portugal, 08/09/2018. https://doi.org/10.1007/978-3-319-99253-2_41

APA

Arbonès, D. R., Sergiienko, N. Y., Ding, B., Krause, O., Igel, C., & Wagner, M. (2018). Sparse incomplete LU-decomposition for wave farm designs under realistic conditions. I Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, 2018, Proceedings (s. 512-524). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind. 11101 LNCS https://doi.org/10.1007/978-3-319-99253-2_41

Vancouver

Arbonès DR, Sergiienko NY, Ding B, Krause O, Igel C, Wagner M. Sparse incomplete LU-decomposition for wave farm designs under realistic conditions. I Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, 2018, Proceedings. Springer. 2018. s. 512-524. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11101 LNCS). https://doi.org/10.1007/978-3-319-99253-2_41

Author

Arbonès, Dídac Rodríguez ; Sergiienko, Nataliia Y. ; Ding, Boyin ; Krause, Oswin ; Igel, Christian ; Wagner, Markus. / Sparse incomplete LU-decomposition for wave farm designs under realistic conditions. Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, 2018, Proceedings. Springer, 2018. s. 512-524 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11101 LNCS).

Bibtex

@inproceedings{b8e0e72c110d4e5da2255f71ba91e7cf,
title = "Sparse incomplete LU-decomposition for wave farm designs under realistic conditions",
abstract = "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.",
keywords = "Multi-objective optimisation, Ocean wave energy, Simulation speed-up, Wave energy converter array",
author = "Arbon{\`e}s, {D{\'i}dac Rodr{\'i}guez} and Sergiienko, {Nataliia Y.} and Boyin Ding and Oswin Krause and Christian Igel and Markus Wagner",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-99253-2_41",
language = "English",
isbn = "9783319992525",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "512--524",
booktitle = "Parallel Problem Solving from Nature – PPSN XV",

}

RIS

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

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

ER -

ID: 203377660