Reliability analysis with Metamodel Line Sampling

Depina, Ivan; Le, Thi Minh Hue; Fenton, Gordon A.; Eiksund, Gudmund

doi:10.1016/j.strusafe.2015.12.005

Cited by 86 publications

(24 citation statements)

References 19 publications

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“…As suggested in [48], the RBDO and RO algorithms implementing sampling-based reliability methods can be organized into three groups; applications of metamodels, decoupling approaches, and enhanced reliability approaches. A metamodel is commonly a regression or a classification model constructed as an approximation of the performance function (e.g., [15]). A metamodel is applied within RO or RBDO algorithms to reduce the computa-tional demands resulting from computationally complex models of engineering structures.…”

Section: Short Literature Reviewmentioning

confidence: 99%

Coupling the cross-entropy with the line sampling method for risk-based design optimization

Depina

Papaioannou²,

Straub³

et al. 2016

Struct Multidisc Optim

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An algorithm for risk-based optimization (RO) of engineering systems is proposed, which couples the Cross-entropy (CE) optimization method with the Line Sampling (LS) reliability method. The CE-LS algorithm relies on the CE method to optimize the total cost of a system that is composed of the design and operation cost (e.g., production cost) and the expected failure cost (i.e., failure risk). Guided by the random search of the CE method, the algorithm proceeds iteratively to update a set of random search distributions such that the optimal or near-optimal solution is likely to occur. The LS-based failure probability estimates are required to evaluate the failure risk. Throughout the optimization process, the coupling relies on a local weighted average approximation of the probability of failure to reduce the computational demands associated with RO. As the CE-LS algorithm proceeds to locate a region of design parameters with near-optimal solutions, the local weighted average approximation of the probability of failure is refined. The adaptive refinement procedure is repeatedly applied until convergence criteria with respect to both the optimization and the approximation of the failure probability are satisfied. The performance of the proposed optimization heuristic is examined empiri- cally on several RO problems, including the design of a monopile foundation for offshore wind turbines.

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Section: Short Literature Reviewmentioning

confidence: 99%

Coupling the cross-entropy with the line sampling method for risk-based design optimization

Depina

Papaioannou²,

Straub³

et al. 2016

Struct Multidisc Optim

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

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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“…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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Reliability analysis with Metamodel Line Sampling

Cited by 86 publications

References 19 publications

Coupling the cross-entropy with the line sampling method for risk-based design optimization

Coupling the cross-entropy with the line sampling method for risk-based design optimization

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

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