A symbolic framework for flexible multibody systems applied to horizontal axis wind turbines

Branlard, Emmanuel; Geisler, Jens

doi:10.5194/wes-2021-46

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Code availability. The source code of the digital twin and examples using a generic spar turbine are provided in the GitHub repository (https://github.com/NREL/wtDigiTwin, Branlard, 2023a) and https://doi.org/10.5281/zenodo.8048549 (Branlard, 2023b).…”

Section: Appendix C: Verification Of the Linear Modelsmentioning

confidence: 99%

A digital twin solution for floating offshore wind turbines validated using a full-scale prototype

Branlard,

Jonkman,

Brown

et al. 2024

Wind Energ. Sci.

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Abstract. In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The digital twin is validated using measurement data from the full-scale TetraSpar prototype. We focus on the estimation of the aerodynamic loads, wind speed, and section loads along the tower, with the aim of estimating the fatigue lifetime of the tower. Our digital twin solution integrates (1) a Kalman filter to estimate the structural states based on a linear model of the structure and measurements from the turbine, (2) an aerodynamic estimator, and (3) a physics-based virtual sensing procedure to obtain the loads along the tower. The digital twin relies on a set of measurements that are expected to be available on any existing wind turbine (power, pitch, rotor speed, and tower acceleration) and motion sensors that are likely to be standard measurements for a floating platform (inclinometers and GPS sensors). We explore two different pathways to obtain physics-based models: a suite of dedicated Python tools implemented as part of this work and the OpenFAST linearization feature. In our final version of the digital twin, we use components from both approaches. We perform different numerical experiments to verify the individual models of the digital twin. In this simulation realm, we obtain estimated damage equivalent loads of the tower fore–aft bending moment with an accuracy of approximately 5 % to 10 %. When comparing the digital twin estimations with the measurements from the TetraSpar prototype, the errors increased to 10 %–15 % on average. Overall, the accuracy of the results is promising and demonstrates the possibility of using digital twin solutions to estimate fatigue loads on floating offshore wind turbines. A natural continuation of this work would be to implement the monitoring and diagnostics aspect of the digital twin to inform operation and maintenance decisions. The digital twin solution is provided with examples as part of an open-source repository.

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Section: Appendix C: Verification Of the Linear Modelsmentioning

confidence: 99%

A digital twin solution for floating offshore wind turbines validated using a full-scale prototype

Branlard,

Jonkman,

Brown

et al. 2024

Wind Energ. Sci.

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A digital-twin solution for floating offshore wind turbines validated using a full-scale prototype

Branlard

Jonkman

Brown³

et al. 2023

Preprint

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Abstract. In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The digital twin is validated using measurement data from the full-scale TetraSpar prototype. We focus on the estimation of the aerodynamic loads, wind speed, and section loads along the tower, with the aim at estimating the fatigue life-time of the tower. Our digital twin solution integrates: 1) a Kalman filter to estimate the structural states based on a linear model of the structure and measurements from the turbine, 2) an aerodynamic estimator, and 3) a physics-based virtual sensing procedure to obtain the loads along the tower. The digital twin relies on a set of measurements that are expected to be available on any existing wind turbine (power, pitch, rotor speed, and tower acceleration), and motion sensors that are likely to be standard measurements for a floating platform (inclinometers and GPS sensors). We explore two different pathways to obtain physics-based models: a suite of dedicated Python tools implemented as part of this work, or the OpenFAST linearization feature. In our final version of the digital twin, we use components from both approaches. We perform different numerical experiments to verify the individual models of the digital twin. In this simulation realm, we obtain estimated damage equivalent loads with an accuracy of the order of 5 % to 10 %. When comparing the digital twin estimations with the measurements from the TetraSpar prototype, the errors increased to 10 %–15 % on average. Overall, the accuracy of the results appears promising and demonstrates the possibility to use digital twin solutions to estimate fatigue loads on floating offshore wind turbines. A natural continuation of this work would be to implement the monitoring and diagnostics aspect of the digital twin, to inform operation and maintenance decisions. The digital twin solution is provided with examples as part of an open-source repository.

show abstract

A symbolic framework for flexible multibody systems applied to horizontal axis wind turbines

Cited by 2 publications

References 14 publications

A digital twin solution for floating offshore wind turbines validated using a full-scale prototype

A digital twin solution for floating offshore wind turbines validated using a full-scale prototype

A digital-twin solution for floating offshore wind turbines validated using a full-scale prototype

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