Aerodynamic Design Optimization of Wind Turbine Airfoils under Aleatory and Epistemic Uncertainty

Caboni, Marco; Minisci, Edmondo; Riccardi, Annalisa

doi:10.1088/1742-6596/1037/4/042011

Cited by 13 publications

(8 citation statements)

References 2 publications

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“…Furthermore, these parameters were implemented as scaling variables with means of 1.0, such that the actual variables as input to the simulations were a product of the deterministic values and the respective stochastic scaling parameters in θ q . The hyperparameters of the Gaussian process model were fit using Bayesian global optimization methods (expected improvement) (Mockus, 1975;Jones et al, 1998;Brochu et al, 2010). The noise standard deviation was taken to be non-zero and also fit during this procedure, even though the simulation outputs used in the fitting are in a certain sense noise-free.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Design optimization of structures subject to probabilistic problem variables and parameters, sometimes called optimization under uncertainty, is in general a large field of research at the intersection of two larger fields, optimization and probabilistic design. One main distinction is between robust design optimization (RBO) (Ben-Tal et al, 2009;Zang et al, 2005) and reliability-based design optimization (RBDO) (Choi et al, 2007;Valdebenito and Schuëller, 2010a). The main difference between the two methods is that in RBDO the design is optimized normally but under specific probabilistic limits on structural performance (probability of failure), whereas in RBO the basic idea is to minimize the variance of a probabilistic objective function in order that the obtained (mean) solution is robust with respect to the uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…A substantial amount of the literature for both structural reliability analyses and RBDO of offshore wind turbines (OWTs) has focused on aspects other than support structure design. Areas such as blade design (Ronold et al, 1999;Toft and Sørensen, 2011;Dimitrov, 2013;Hu et al, 2016;Caboni et al, 2018), foundation design (Yoon et al, 2014;Carswell et al, 2015;Depina et al, 2016Depina et al, , 2017Haj et al, 2019;Velarde et al, 2019), component design (Kostandyan and Sørensen, 2011;Rafsanjani et al, 2017;Lee et al, 2014;Li et al, 2017), system/wind farm aspects (Sørensen et al, 2008), inspection and maintenance planning, and probabilistic tuning/optimization of safety factors (Sørensen and Tarp-Johansen, 2005;Márquez-Domínguez and Sørensen, 2012;Veldkamp, 2008) have all been studied. As for support structure design specifically, though most structural analyses of OWTs remain deterministic, there has been a number of stud-ies incorporating reliability-based (or otherwise probabilistic) approaches.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

Stieng

Muskulus

2020

Wind Energ. Sci.

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Abstract. The need for cost-effective support structure designs for offshore wind turbines has led to continued interest in the development of design optimization methods. So far, almost no studies have considered the effect of uncertainty, and hence probabilistic constraints, on the support structure design optimization problem. In this work, we present a general methodology that implements recent developments in gradient-based design optimization, in particular the use of analytical gradients, within the context of reliability-based design optimization methods. Gradient-based optimization is typically more efficient and has more well-defined convergence properties than gradient-free methods, making this the preferred paradigm for reliability-based optimization where possible. By an assumed factorization of the uncertain response into a design-independent, probabilistic part and a design-dependent but completely deterministic part, it is possible to computationally decouple the reliability analysis from the design optimization. Furthermore, this decoupling makes no further assumption about the functional nature of the stochastic response, meaning that high-fidelity surrogate modeling through Gaussian process regression of the probabilistic part can be performed while using analytical gradient-based methods for the design optimization. We apply this methodology to several different cases based around a uniform cantilever beam and the OC3 Monopile and different loading and constraint scenarios. The results demonstrate the viability of the approach in terms of obtaining reliable, optimal support structure designs and furthermore show that in practice only a limited amount of additional computational effort is required compared to deterministic design optimization. While there are some limitations in the applied cases, and some further refinement might be necessary for applications to high-fidelity design scenarios, the demonstrated capabilities of the proposed methodology show that efficient reliability-based optimization for offshore wind turbine support structures is feasible.

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Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

Stieng

Muskulus

2020

Wind Energ. Sci.

View full text Add to dashboard Cite

show abstract

“…Methods to derive cost effective, optimal support structure designs -balancing minimal use of materials (and potentially other cost driving design A substantial amount of the literature for both structural reliability analyses and RBDO of offshore wind turbines (OWTs) has focused on aspects other than support structure design. Areas such as, e.g., blade design (Ronold et al, 1999;Toft and Sørensen, 20 2011;Dimitrov, 2013;Hu et al, 2016;Caboni et al, 2018), foundation design (Yoon et al, 2014;Carswell et al, 2015;Depina et al, 2016;Haja et al, 2019;Depina et al, 2017;Velarde et al, 2019), component design (Kostandyan and Sørensen, 2011;Rafsanjani et al, 2017;Lee et al, 2014;Li et al, 2017), system/wind farm aspects (Sørensen et al, 2008), inspection and maintenance planning and probabilistic tuning/optimization of safety factors (Sørensen and Tarp-Johansen, 2005; Márquez-Domínguez and Veldkamp, 2008) have all been studied. As for support structure design specifically, though 25 most structural analyses of OWTs remain deterministic, there has been a number of studies incorporating reliability-based (or otherwise probabilistic) approaches.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

Stieng¹,

Muskulus²

2019

Preprint

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Abstract. The need for cost effective support structure designs for offshore wind turbines has led to continued interest in the development of design optimization methods. So far, almost no studies have considered the effect of uncertainty, and hence probabilistic constraints, on the support structure design optimization problem. In this work, we present a general methodology that implements recent developments in gradient-based design optimization, in particular the use of analytical gradients, within the context of reliability-based design optimization methods. By an assumed factorization of the uncertain response into a design-independent, probabilistic part and a design-dependent, but completely deterministic part, it is possible to computationally decouple the reliability analysis from the design optimization. Furthermore, this decoupling makes no further assumption about the functional nature of the stochastic response, meaning that high fidelity surrogate modeling through Gaussian process regression of the probabilistic part can be performed while using analytical gradient-based methods for the design optimization. We apply this methodology to several different cases based around a uniform cantilever beam and the OC3 Monopile and different loading and constraints scenarios. The results demonstrate the viability of the approach in terms of obtaining reliable, optimal support structure designs and furthermore show that in practice only a limited amount of additional computational effort is required compared to deterministic design optimization. While there are some limitations in the applied cases, and some further refinement might be necessary for applications to high fidelity design scenarios, the demonstrated capabilities of the proposed methodology show that efficient reliability-based optimization for offshore wind turbine support structures is feasible.

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“…Apart from bootstrapping, other techniques such as evidence theory (i.e. Dempster-Shafer theory) for wind turbine airfoil design (Caboni et al, 2018) and Bayesian inference for finite element modeling of structural systems (Caicedo and Zárate, 2011) have also been used to evaluate epistemic uncertainties. These methods may be considered in future developments of the proposed framework.…”

Section: A52 Trainingmentioning

confidence: 99%

Performance-based wind engineering framework for vertical structures subjected to nonstationary wind loads

Le¹

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Regarded as a rational analytical approach that enables more risk-informed decision making, performance-based engineering (PBE) has been widely endorsed by the wind engineering community for the design and analysis of vibrationsensitive structures such as tall buildings. Accomplished through modular stages that delineate a structure's capacity to satisfy performance objectives, the uncertainties, error propagation, and random properties of the structural system, the environmental hazard, and the estimation of losses incurred from damages can be systematically examined with great detail. This multi-faceted, holistic approach vi Robert W. Bordewieck Endowed Engineering Fund provided by the College of Engineering of Northeastern University are gratefully acknowledged. Any opinions, findings and conclusions or recommendations are those of the Author and do not necessarily reflect the views of the NSF, MathWorks Inc.

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Aerodynamic Design Optimization of Wind Turbine Airfoils under Aleatory and Epistemic Uncertainty

Cited by 13 publications

References 2 publications

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

Performance-based wind engineering framework for vertical structures subjected to nonstationary wind loads

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