From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases
<p>We define and demonstrate a procedure for quick assessment of site-specific lifetime fatigue loads using simplified load mapping functions (surrogate models), trained by means of a database with high-fidelity load simulations. The performance of five surrogate models is assessed by compa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-10-01
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| Series: | Wind Energy Science |
| Online Access: | https://www.wind-energ-sci.net/3/767/2018/wes-3-767-2018.pdf |
| _version_ | 1811323528201371648 |
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| author | N. Dimitrov M. C. Kelly A. Vignaroli J. Berg |
| author_facet | N. Dimitrov M. C. Kelly A. Vignaroli J. Berg |
| author_sort | N. Dimitrov |
| collection | DOAJ |
| description | <p>We define and demonstrate a procedure for quick assessment of site-specific
lifetime fatigue loads using simplified load mapping functions (surrogate
models), trained by means of a database with high-fidelity load simulations.
The performance of five surrogate models is assessed by comparing
site-specific lifetime fatigue load predictions at 10 sites using an
aeroelastic model of the DTU 10 MW reference wind turbine. The surrogate
methods are polynomial chaos expansion, quadratic response surface, universal
Kriging, importance sampling, and nearest-neighbor interpolation. Practical
bounds for the database and calibration are defined via nine environmental
variables, and their relative effects on the fatigue loads are evaluated by
means of Sobol sensitivity indices. Of the surrogate-model methods,
polynomial chaos expansion provides an accurate and robust performance in
prediction of the different site-specific loads. Although the Kriging
approach showed slightly better accuracy, it also demanded more computational
resources.</p> |
| first_indexed | 2024-04-13T13:56:45Z |
| format | Article |
| id | doaj.art-7e37b4c3f7ac48139cf17cbf24d9be2c |
| institution | Directory Open Access Journal |
| issn | 2366-7443 2366-7451 |
| language | English |
| last_indexed | 2024-04-13T13:56:45Z |
| publishDate | 2018-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Wind Energy Science |
| spelling | doaj.art-7e37b4c3f7ac48139cf17cbf24d9be2c2022-12-22T02:44:11ZengCopernicus PublicationsWind Energy Science2366-74432366-74512018-10-01376779010.5194/wes-3-767-2018From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databasesN. Dimitrov0M. C. Kelly1A. Vignaroli2J. Berg3DTU Wind Energy, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, DenmarkDTU Wind Energy, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, DenmarkDTU Wind Energy, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, DenmarkDTU Wind Energy, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark<p>We define and demonstrate a procedure for quick assessment of site-specific lifetime fatigue loads using simplified load mapping functions (surrogate models), trained by means of a database with high-fidelity load simulations. The performance of five surrogate models is assessed by comparing site-specific lifetime fatigue load predictions at 10 sites using an aeroelastic model of the DTU 10 MW reference wind turbine. The surrogate methods are polynomial chaos expansion, quadratic response surface, universal Kriging, importance sampling, and nearest-neighbor interpolation. Practical bounds for the database and calibration are defined via nine environmental variables, and their relative effects on the fatigue loads are evaluated by means of Sobol sensitivity indices. Of the surrogate-model methods, polynomial chaos expansion provides an accurate and robust performance in prediction of the different site-specific loads. Although the Kriging approach showed slightly better accuracy, it also demanded more computational resources.</p>https://www.wind-energ-sci.net/3/767/2018/wes-3-767-2018.pdf |
| spellingShingle | N. Dimitrov M. C. Kelly A. Vignaroli J. Berg From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases Wind Energy Science |
| title | From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases |
| title_full | From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases |
| title_fullStr | From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases |
| title_full_unstemmed | From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases |
| title_short | From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases |
| title_sort | from wind to loads wind turbine site specific load estimation with surrogate models trained on high fidelity load databases |
| url | https://www.wind-energ-sci.net/3/767/2018/wes-3-767-2018.pdf |
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