| de Nolasco Santos, Francisco |
ORCID
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| Publications (5) |
Top | Institute | Publications |
| A1 Publications (3) [show] |
- Hlaing, N.; Morato, P.G.; de Nolasco Santos, F.; Weijtjens, W.; Devriendt, C.; Rigo, P. (2024). Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks. Structural Health Monitoring 23(3): 1641-1663. https://dx.doi.org/10.1177/14759217231186048, more
 de Nolasco Santos, F.; D'Antuono, P.; Robbelein, K.; Noppe, N.; Weijtjens, W.; Devriendt, C. (2023). 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://dx.doi.org/10.1016/j.renene.2023.01.093, more- de Nolasco Santos, F.; Noppe, N.; Weijtjens, W.; Devriendt, C. (2022). Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups. Wind Energy Science 7(1): 299-321. https://dx.doi.org/10.5194/wes-7-299-2022, more
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| Peer reviewed publication [show] |
- de Nolasco Santos, F.; Noppe, N.; Weijtjens, W.; Devriendt, C. (2020). SCADA-based neural network thrust load model for fatigue assessment: cross validation with in-situ measurements. Journal of Physics: Conference Series 1618(2): 022020. https://dx.doi.org/10.1088/1742-6596/1618/2/022020, more
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| Book chapter [show] |
- de Nolasco Santos, F.; Robbelein, K.; D'Antuono, P.; Noppe, N.; Weijtjens, W.; Devriendt, C. (2023). Towards a fleetwide data-driven lifetime assessment methodology of offshore wind support structures based on SCADA and SHM data, in: Rizzo, P. et al. European Workshop on Structural Health Monitoring. EWSHM 2022 - Volume 1. pp. 123-132. https://dx.doi.org/10.1007/978-3-031-07254-3_13, more
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