Bossanyi, E. A.: GH Bladed Theory Manual, GH & Partners Ltd, 56–58, 2003. a
Branlard, E.: pyfast, GitHub [code],
https://github.com/OpenFAST/python-toolbox (last access: 1 November 2023), 2023. a
Branlard, E., Jonkman, J., Dana, S., and Doubrawa, P.: A Digital Twin Based on OpenFAST Linearizations for Real-Time Load and Fatigue Estimation of Land-Based Turbines, J. Phys.: Conf. Ser., 1618, 022030,
https://doi.org/10.1088/1742-6596/1618/2/022030, 2020.
a
Branlard, E., Jonkman, J., Brown, C., and Zhang, J.: A digital twin solution for floating offshore wind turbines validated using a full-scale prototype, Wind Energ. Sci., 9, 1–24,
https://doi.org/10.5194/wes-9-1-2024, 2024.
a
Crestaux, T., Le Maítre, O., and Martinez, J.-M.: Polynomial Chaos Expansion for Sensitivity Analysis, Reliabil. Eng. Syst. Safe., 94, 1161–1172,
https://doi.org/10.1016/j.ress.2008.10.008, 2009.
a
Dadras Eslamlou, A. and Huang, S.: Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review, Buildings, 12, 2067,
https://doi.org/10.3390/buildings12122067, 2022.
a
De Kooning, J. D. M., Stockman, K., De Maeyer, J., Jarquin-Laguna, A., and Vandevelde, L.: Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load, Processes, 9, 2224,
https://doi.org/10.3390/pr9122224, 2021.
a
de N Santos, F., D'Antuono, P., Robbelein, K., Noppe, N., Weijtjens, W., and Devriendt, C.: Long-Term Fatigue Estimation on Offshore Wind Turbines Interface Loads through Loss Function Physics-Guided Learning of Neural Networks, Renew. Energy, 205, 461–474,
https://doi.org/10.1016/j.renene.2023.01.093, 2023.
a,
b
Dimitrov, N.: Surrogate Models for Parameterized Representation of Wake-induced Loads in Wind Farms, Wind Energy, 22, 1371–1389,
https://doi.org/10.1002/we.2362, 2019.
a,
b,
c,
d,
e,
f,
g
Dimitrov, N. and Göçmen, T.: Virtual Sensors for Wind Turbines with Machine Learning-Based Time Series Models, Wind Energy, 25, 1626–1645,
https://doi.org/10.1002/we.2762, 2022.
a,
b
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From Wind to Loads: Wind Turbine Site-Specific Load Estimation with Surrogate Models Trained on High-Fidelity Load Databases, Wind Energ. Sci., 3, 767–790,
https://doi.org/10.5194/wes-3-767-2018, 2018.
a
Duthé, G., Santos, F. D. N., Abdallah, I., Réthore, P.-É., Weijtjens, W., Chatzi, E., and Devriendt, C.: Local Flow and Loads Estimation on Wake-Affected Wind Turbines Using Graph Neural Networks and PyWake, J. Phys.: Conf. Ser., 2505, 012014,
https://doi.org/10.1088/1742-6596/2505/1/012014, 2023.
a
Errandonea, I., Beltrán, S., and Arrizabalaga, S.: Digital Twin for Maintenance: A Literature Review, Comput. Indust., 123, 103316,
https://doi.org/10.1016/j.compind.2020.103316, 2020.
a
Fahim, M., Sharma, V., Cao, T.-V., Canberk, B., and Duong, T. Q.: Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines, IEEE Access, 10, 14184–14194,
https://doi.org/10.1109/ACCESS.2022.3147602, 2022.
a
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A.: Deep Learning for Time Series Classification: A Review, Data Min. Knowl. Discov., 33, 917–963,
https://doi.org/10.1007/s10618-019-00619-1, 2019.
a
Fluck, M. and Crawford, C.: A Fast Stochastic Solution Method for the Blade Element Momentum Equations for Long-Term Load Assessment, Wind Energy, 21, 115–128, 2018.
a,
b
Ge, R., Huang, F., Jin, C., and Yuan, Y.: Escaping From Saddle Points – Online Stochastic Gradient for Tensor Decomposition, arXiv [preprint],
https://doi.org/10.48550/arXiv.1503.02101, 2015.
a
Göçmen, T., van der Laan, P., Réthoré, P.-E., Diaz, A. P., Larsen, G. C., and Ott, S.: Wind Turbine Wake Models Developed at the Technical University of Denmark: A Review, Renew. Sustain. Energ. Rev., 60, 752–769,
https://doi.org/10.1016/j.rser.2016.01.113, 2016.
a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press,
http://www.deeplearningbook.org (last access: 25 October 2024), 2016.
a,
b,
c,
d,
e
Haghi, R. and Crawford, C.: Surrogate Mode
ls for the Blade Element Momentum Aerodynamic Model Using Non-Intrusive Polynomial Chaos Expansions, Wind Energ. Sci., 7, 1289–1304,
https://doi.org/10.5194/wes-7-1289-2022, 2022.
a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, arXiv [preprint],
https://doi.org/10.48550/arXiv.1512.03385, 2015.
a
International Electrotechnical Commission: IEC 61400-1: Wind Energy Generation Systems – Part 1: Design Requirements, Standard, International Electrotechnical Commission, 2019.
a,
b,
c,
d,
e
Ishihara, T. and Qian, G.-W.: A New Gaussian-based Analytical Wake Model for Wind Turbines Considering Ambient Turbulence Intensities and Thrust Coefficient Effects, J. Wind Eng. Indust. Aerodynam., 177, 275–292,
https://doi.org/10.1016/j.jweia.2018.04.010, 2018.
a,
b,
c,
d,
e
Jonkman, B., Mudafort, R. M., Platt, A., Branlard, E., Sprague, M., Jonkman, J., HaymanConsulting, Hall, M., Vijayakumar, G., Buhl, M., Ross, H., Bortolotti, P., marco, Ananthan, S., Michael, S., Rood, J., rdamiani, nrmendoza, sinolonghai, pschuenemann, Slaughter, D., ashesh2512, kshaler, Housner, S., psakievich, Bendl, K., Carmo, L., Quon, E., mattrphillips, and Kusuno, N.: OpenFAST/openfast: OpenFAST v3.3.0, Zenodo [code],
https://doi.org/10.5281/zenodo.7262094, 2022.
a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA,
https://doi.org/10.2172/947422, 2009.
a,
b
Jonkman, J. M. and Buhl Jr., M. L.: FAST User's Guide,
https://www.nrel.gov/docs/fy06osti/38230.pdf (last access: 25 October 2024(, 2005.
a,
b,
c
Kucherenko, S., Albrecht, D., and Saltelli, A.: Exploring Multi-Dimensional Spaces: A Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques, arXiv [preprint],
https://doi.org/10.48550/arXiv.1505.02350, 2015.
a
Larsen, T. J. and Hansen, A. M.: How 2 HAWC2, the User's Manual, Risø National Laboratory, ISBN 978-87-550-3583-6,
https://orbit.dtu.dk/files/7703110/ris_r_1597.pdf (last access: 29 October 2024), 2007. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep Learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
a
Leshno, M., Lin, V. Y., Pinkus, A., and Schocken, S.: Multilayer Feedforward Networks with a Nonpolynomial Activation Function Can Approximate Any Function, Neural Networks, 6, 861–867,
https://doi.org/10.1016/S0893-6080(05)80131-5, 1993.
a
Liu, Y., Zhang, J.-M., Min, Y.-T., Yu, Y., Lin, C., and Hu, Z.-Z.: A Digital Twin-Based Framework for Simulation and Monitoring Analysis of Floating Wind Turbine Structures, Ocean Eng., 283, 115009,
https://doi.org/10.1016/j.oceaneng.2023.115009, 2023.
a
Mikołajczyk, A. and Grochowski, M.: Data Augmentation for Improving Deep Learning in Image Classification Problem, in: 2018 International Interdisciplinary PhD Workshop (IIPhDW), 9–12 May 2018, Swinemünde, Poland, 117–122,
https://doi.org/10.1109/IIPHDW.2018.8388338, 2018.
a
Movsessian, A., Schedat, M., and Faber, T.: Feature Selection Techniques for Modelling Tower Fatigue Loads of a Wind Turbine with Neural Networks, Wind Energ. Sci., 6, 539–554,
https://doi.org/10.5194/wes-6-539-2021, 2021.
a
Moynihan, B., Tronci, E. M., Hughes, M. C., Moaveni, B., and Hines, E.: Virtual Sensing via Gaussian Process for Bending Moment Response Prediction of an Offshore Wind Turbine Using SCADA Data, Renew. Energy, 227, 120466,
https://doi.org/10.1016/j.renene.2024.120466, 2024.
a
Mylonas, C., Abdallah, I., and Chatzi, E.: Conditional Variational Autoencoders for Probabilistic Wind Turbine Blade Fatigue Estimation Using Supervisory, Control, and Data Acquisition Data, Wind Energy, 24, 1122–1139,
https://doi.org/10.1002/we.2621, 2021.
a,
b
Nispel, A., Ekwaro-Osire, S., Dias, J. P., and Cunha, A.: Probabilistic Design and Uncertainty Quantification of the Structure of a Monopile Offshore Wind Turbine, in: Volume 13: Safety Engineering, Risk, and Reliability Analysis, American Society of Mechanical Engineers, Salt Lake City, Utah, USA, p. V013T13A021, ISBN 978-0-7918-8350-1,
https://doi.org/10.1115/IMECE2019-11862, 2019.
a
Pedersen, M. M., van der Laan, P., Friis-Møller, M., Rinker, J., and Réthoré, P.-E.: PyWake 2.5.0: An open-source wind farm simulation tool,
https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake (last access: 29 October 2024), 2023. a
Raissi, M., Perdikaris, P., and Karniadakis, G. E.: Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations, J. Comput. Phys., 378, 686–707,
https://doi.org/10.1016/j.jcp.2018.10.045, 2019.
a
Ransley, E. J., Brown, S. A., Edwards, E. C., Tosdevin, T., Monk, K., Reynolds, A. M., Greaves, D., and Hann, M. R.: Real-Time Hybrid Testing of a Floating Offshore Wind Turbine Using a Surrogate-Based Aerodynamic Emulator, ASME Open J. Eng., 2, 021017,
https://doi.org/10.1115/1.4056963, 2023.
a
Renardy, M., Joslyn, L. R., Millar, J. A., and Kirschner, D. E.: To Sobol or Not to Sobol? The Effects of Sampling Schemes in Systems Biology Applications, Math. Biosci., 337, 108593,
https://doi.org/10.1016/j.mbs.2021.108593, 2021.
a
Sanderse, B., van der Pijl, S., and Koren, B.: Review of Computational Fluid Dynamics for Wind Turbine Wake Aerodynamics, Wind Energy, 14, 799–819,
https://doi.org/10.1002/we.458, 2011.
a
Scarselli, F., Gori, M., Ah Chung Tsoi, Hagenbuchner, M., and Monfardini, G.: The Graph Neural Network Model, IEEE T. Neural Netw., 20, 61–80,
https://doi.org/10.1109/TNN.2008.2005605, 2009.
a
Schröder, L., Krasimirov Dimitrov, N., Verelst, D. R., and Sørensen, J. A.: Wind Turbine Site-Specific Load Estimation Using Artificial Neural Networks Calibrated by Means of High-Fidelity Load Simulations, J. Phys.: Conf. Ser., 1037, 062027,
https://doi.org/10.1088/1742-6596/1037/6/062027, 2018.
a,
b,
c
Schröder, L., Dimitrov, N. K., and Verelst, D. R.: A Surrogate Model Approach for Associating Wind Farm Load Variations with Turbine Failures, Wind Energ. Sci., 5, 1007–1022,
https://doi.org/10.5194/wes-5-1007-2020, 2020.
a,
b,
c,
d
Schröder, L., Dimitrov, N. K., Verelst, D. R., and Sørensen, J. A.: Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring, Energies, 15, 558,
https://doi.org/10.3390/en15020558, 2022.
a,
b
Shaler, K., Jasa, J., and Barter, G. E.: Efficient Loads Surrogates for Waked Turbines in an Array, J. Phys.: Conf. Ser., 2265, 032095,
https://doi.org/10.1088/1742-6596/2265/3/032095, 2022.
a
Singh, D., Dwight, R. P., Laugesen, K., Beaudet, L., and Viré, A.: Probabilistic Surrogate Modeling of Offshore Wind-Turbine Loads with Chained Gaussian Processes, J. Phys.: Conf. Ser., 2265, 032070,
https://doi.org/10.1088/1742-6596/2265/3/032070, 2022.
a
Sobol', I. M.: On the Distribution of Points in a Cube and the Approximate Evaluation of Integrals, Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 7, 784–802, 1967. a
Song, M., Moaveni, B., Ebrahimian, H., Hines, E., and Bajric, A.: Joint Parameter-Input Estimation for Digital Twinning of the Block Island Wind Turbine Using Output-Only Measurements, Mech. Syst. Sig. Process., 198, 110425,
https://doi.org/10.1016/j.ymssp.2023.110425, 2023.
a
Song, Z., Hackl, C. M., Anand, A., Thommessen, A., Petzschmann, J., Kamel, O., Braunbehrens, R., Kaifel, A., Roos, C., and Hauptmann, S.: Digital Twins for the Future Power System: An Overview and a Future Perspective, Sustainability, 15, 5259,
https://doi.org/10.3390/su15065259, 2023.
a
Sudret, B.: Uncertainty Propagation and Sensitivity Analysis in Mechanical Models – Contributions to Structural Reliability and Stochastic Spectral Methods, PhD thesis, ETH Zurich,
https://ethz.ch/content/dam/ethz/special-interest/baug/ibk/risk-safety-and-uncertainty-dam/publications/reports/HDRSudret.pdf (last access: 29 October 2024), 2007. a
Thomsen, K.: The Statistical Variation of Wind Turbine Fatigue Loads, no. 1063 in Risø-R, Risø National Laboratory, Roskilde, ISBN 978-87-550-2410-6 978-87-550-2411-3,
https://www.osti.gov/etdeweb/servlets/purl/292704 (last access: 29 October 2024), 1998.
a,
b
van den Bos, L. M. M., Sanderse, B., Blonk, L., Bierbooms, W. A. A. M., and van Bussel, G. J. W.: Efficient Ultimate Load Estimation for Offshore Wind Turbines Using Interpolating Surrogate Models, J. Phys.: Conf. Ser., 1037, 062017,
https://doi.org/10.1088/1742-6596/1037/6/062017, 2018.
a
Veers, P. S.: Three-Dimensional Wind Simulation, Tech. rep., Sandia National Labs., Albuquerque, NM, USA,
https://www.osti.gov/biblio/7102613 (last access: 29 October 2024), 1988.
a,
b,
c
Wang, C., Qiang, X., Xu, M., and Wu, T.: Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation, Symmetry, 14, 1219,
https://doi.org/10.3390/sym14061219, 2022.
a
Westermann, P., Welzel, M., and Evins, R.: Using a Deep Temporal Convolutional Network as a Building Energy Surrogate Model That Spans Multiple Climate Zones, Appl. Energy, 278, 115563,
https://doi.org/10.1016/j.apenergy.2020.115563, 2020.
a
William, M. M., Bonfils, N., Dimitrov, N., and Dou, S.: Wind Farm Parameterization and Turbulent Wind Box Generation, Report, DTU, IFPEN,
https://ifp.hal.science/hal-04033050 (last access: 29 October 2024), 2022.
a,
b,
c,
d
Williams, B. and Cremaschi, S.: Surrogate Model Selection for Design Space Approximation And Surrogatebased Optimization, in: Computer Aided Chemical Engineering, vol. 47, Elsevier, 353–358, ISBN 978-0-12-818597-1,
https://doi.org/10.1016/B978-0-12-818597-1.50056-4, 2019.
a
