Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling

Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationall...

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Vydáno v:	Wind energy (Chichester, England) Ročník 27; číslo 1; s. 75 - 100
Hlavní autoři:	Guth, Stephen, Katsidoniotaki, Eirini, Sapsis, Themistoklis P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bognor Regis John Wiley & Sons, Inc 01.01.2024 Wiley
Témata:	active sampling Bayesian analysis Computational fluid dynamics Computer applications Computing costs Constraint modelling Data reduction Fluid dynamics Foundations Gaussian process heavy tails and extreme events Hydrodynamics Learning algorithms Machine learning Mathematical models Offshore Offshore engineering Offshore structures optimal experimental design reduced order modeling Sampling Simulation Statistical analysis Statistical models Statistics Turbines Uncertainty wave episodes Wind power Wind turbines
ISSN:	1095-4244, 1099-1824, 1099-1824
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Abstract	Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods.
AbstractList	Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods. Abstract Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods.
Author	Sapsis, Themistoklis P. Guth, Stephen Katsidoniotaki, Eirini
Author_xml	– sequence: 1 givenname: Stephen orcidid: 0000-0002-7434-5031 surname: Guth fullname: Guth, Stephen email: sguth@mit.edu organization: Massachusetts Institute of Technology – sequence: 2 givenname: Eirini orcidid: 0000-0001-5096-3559 surname: Katsidoniotaki fullname: Katsidoniotaki, Eirini organization: Centre of Natural Hazards and Disaster Science (CNDS) – sequence: 3 givenname: Themistoklis P. orcidid: 0000-0003-0302-0691 surname: Sapsis fullname: Sapsis, Themistoklis P. organization: Massachusetts Institute of Technology
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Snippet	Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due... Abstract Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine...
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SubjectTerms	active sampling Bayesian analysis Computational fluid dynamics Computer applications Computing costs Constraint modelling Data reduction Fluid dynamics Foundations Gaussian process heavy tails and extreme events Hydrodynamics Learning algorithms Machine learning Mathematical models Offshore Offshore engineering Offshore structures optimal experimental design reduced order modeling Sampling Simulation Statistical analysis Statistical models Statistics Turbines Uncertainty wave episodes Wind power Wind turbines
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Title	Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling
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