Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

Guth, Stephen; Sapsis, Themistoklis P.

doi:10.1016/j.probengmech.2021.103162

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Throughout their operational lifespan, OWTs are exposed to stochastic and extreme environmental loads (e.g., wind and waves) that can affect their lifetime. [4][5][6][7][8][9] Accurately capturing the aero-hydro-servo-elastic loads on OWT structures is essential. Specifically, precise analysis of the ultimate limit state (ULS) and fatigue limit state (FLS) enables less conservative designs and, consequently, cost reduction.…”

Section: Introductionmentioning

confidence: 99%

“…However, a significant challenge faced by the offshore wind industry is to ensure the reliability and survivability of these structures while simultaneously reducing costs. Throughout their operational lifespan, OWTs are exposed to stochastic and extreme environmental loads (e.g., wind and waves) that can affect their lifetime 4–9 . Accurately capturing the aero‐hydro‐servo‐elastic loads on OWT structures is essential.…”

Section: Introductionmentioning

confidence: 99%

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

Guth,

Katsidoniotaki,

Sapsis

2023

Wind Energy

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guth,

Katsidoniotaki,

Sapsis

2023

Wind Energy

Self Cite

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“…At shallow and medium water depths, most offshore wind turbine applications use fixed-bottom foundations [7,32], which include structures with monopile, tripod and jacket type foundations. Across their working lifetime, these structures are exposed to stochastic and extreme sea states which cause severe loads that impact the fatigue life [12,14,25,38,47,58]. To implement current best practices, such as designing for a reliable operation lifetime of 25 years [45,46], engineers require prior knowledge of the long-term load statistics on critical structural components (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Guth

Katsidoniotaki

Sapsis

2023

Preprint

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For many design applications in offshore engineering, including offshore wind turbine foundations, engineers need accurate statistics for kinematic and dynamic quantities, such as hydrodynamic forces, whose statistics depend on the stochastic sea surface elevation. Nonlinear phenomena in the wave–structure interaction require high-fidelity simulations to be analyzed accurately. However, accurate quantification of statistics requires a massive number of simulations, and the computational cost is prohibitively expensive. To avoid that cost, this study presents a machine learning framework to develop a reliable surrogate model that minimizes the need for computationally expensive numerical simulations, which is implemented for the monopile foundation of an offshore wind turbine. This framework consists of two parts. The first focuses on dimensionality reduction of stochastic irregular wave episodes and the resulting hydrodynamic force time series. The second of the framework focuses on the development of a Gaussian process regression surrogate model which learns a mapping between the wave episode and the force-on-structure. This surrogate uses a Bayesian active learning method that sequentially samples the wave episodes likely to contribute to the accurate prediction of extreme hydrodynamic forces in order to design subsequent CFD numerical simulations. Additionally, the study implements a spectrum transfer technique to combine CFD results from quiescent and extreme waves. The principal advantage of this framework is that the trained surrogate model is orders of magnitude faster to evaluate than the classical modeling methods, while built-in uncertainty quantification capabilities allows for efficient sampling of the parameter using with the CFD tools traditionally employed.

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Analytic Methods for Estimating the Effects of Stochastic Intermittent Loading on Fatigue-Crack Nucleation

Guth

Sapsis

2021

NODYCON Conference Proceedings Series

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Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

Cited by 4 publications

References 17 publications

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

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

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

Analytic Methods for Estimating the Effects of Stochastic Intermittent Loading on Fatigue-Crack Nucleation

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