AK‐DA: An efficient method for the fatigue assessment of wind turbine structures
Lifetime damage estimation is a complex and demanding task that needs to be performed during the design of offshore wind turbine structures. A general damage analysis framework is proposed by the certification bodies. Therein the total lifetime of the structure is considered as a series of elementar...
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Veröffentlicht in: | Wind energy (Chichester, England) England), 2019-05, Vol.22 (5), p.638-652 |
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description | Lifetime damage estimation is a complex and demanding task that needs to be performed during the design of offshore wind turbine structures. A general damage analysis framework is proposed by the certification bodies. Therein the total lifetime of the structure is considered as a series of elementary situations combining structural and environmental states. For a given structural state, the loading environment is described using statistical parameters such as the wind mean speed at hub height or the peak spectral period of the sea. An estimation of the structural response is to be computed for each of the environmental combinations of parameters, therefore leading to tens of thousands simulations. The cost of a single simulation makes this process often unfeasible for engineers who are usually forced to reduce the number of simulations considering industrial feedback with risks of potential lack of representativity of results. This paper aims at presenting a novel method for the reduction of the simulation costs relative to the long‐term damage estimation (relative to a design load case) and based on the so‐called adaptive Kriging approach. From on a reduced set of observations (multiphysics simulator runs), a Kriging metamodel is here used to approximate the damage model response for all the nonsimulated sets of environmental parameters. The latter are subsequently used to assess the long‐term damage as presented in the standards. The statistical measure of the metamodel error of prediction is used into an iterative structure in order to progressively enrich the design of experiments with informative sets of environmental parameters. This allows us minimizing the global uncertainty of the approximation. The proposed algorithm, hereafter called the AK‐DA for “Adaptive Kriging Damage Assessment,” is illustrated with two industrial cases of fatigue analyses for the NREL 5MW reference monopile structure and its direct application |
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A general damage analysis framework is proposed by the certification bodies. Therein the total lifetime of the structure is considered as a series of elementary situations combining structural and environmental states. For a given structural state, the loading environment is described using statistical parameters such as the wind mean speed at hub height or the peak spectral period of the sea. An estimation of the structural response is to be computed for each of the environmental combinations of parameters, therefore leading to tens of thousands simulations. The cost of a single simulation makes this process often unfeasible for engineers who are usually forced to reduce the number of simulations considering industrial feedback with risks of potential lack of representativity of results. This paper aims at presenting a novel method for the reduction of the simulation costs relative to the long‐term damage estimation (relative to a design load case) and based on the so‐called adaptive Kriging approach. From on a reduced set of observations (multiphysics simulator runs), a Kriging metamodel is here used to approximate the damage model response for all the nonsimulated sets of environmental parameters. The latter are subsequently used to assess the long‐term damage as presented in the standards. The statistical measure of the metamodel error of prediction is used into an iterative structure in order to progressively enrich the design of experiments with informative sets of environmental parameters. This allows us minimizing the global uncertainty of the approximation. 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A general damage analysis framework is proposed by the certification bodies. Therein the total lifetime of the structure is considered as a series of elementary situations combining structural and environmental states. For a given structural state, the loading environment is described using statistical parameters such as the wind mean speed at hub height or the peak spectral period of the sea. An estimation of the structural response is to be computed for each of the environmental combinations of parameters, therefore leading to tens of thousands simulations. The cost of a single simulation makes this process often unfeasible for engineers who are usually forced to reduce the number of simulations considering industrial feedback with risks of potential lack of representativity of results. This paper aims at presenting a novel method for the reduction of the simulation costs relative to the long‐term damage estimation (relative to a design load case) and based on the so‐called adaptive Kriging approach. From on a reduced set of observations (multiphysics simulator runs), a Kriging metamodel is here used to approximate the damage model response for all the nonsimulated sets of environmental parameters. The latter are subsequently used to assess the long‐term damage as presented in the standards. The statistical measure of the metamodel error of prediction is used into an iterative structure in order to progressively enrich the design of experiments with informative sets of environmental parameters. This allows us minimizing the global uncertainty of the approximation. 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This paper aims at presenting a novel method for the reduction of the simulation costs relative to the long‐term damage estimation (relative to a design load case) and based on the so‐called adaptive Kriging approach. From on a reduced set of observations (multiphysics simulator runs), a Kriging metamodel is here used to approximate the damage model response for all the nonsimulated sets of environmental parameters. The latter are subsequently used to assess the long‐term damage as presented in the standards. The statistical measure of the metamodel error of prediction is used into an iterative structure in order to progressively enrich the design of experiments with informative sets of environmental parameters. This allows us minimizing the global uncertainty of the approximation. The proposed algorithm, hereafter called the AK‐DA for “Adaptive Kriging Damage Assessment,” is illustrated with two industrial cases of fatigue analyses for the NREL 5MW reference monopile structure and its direct application</abstract><cop>Bognor Regis</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/we.2312</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-3843-7788</orcidid><orcidid>https://orcid.org/0000-0002-0623-9245</orcidid><orcidid>https://orcid.org/0000-0002-1943-0778</orcidid></addata></record> |
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subjects | adaptive design of experiments Algorithms Computer simulation Crack propagation Damage assessment Design Design of experiments Engineering Sciences Error analysis fatigue prediction Iterative methods Kriging interpolation Kriging metamodel Offshore engineering Offshore operations Offshore structures Parameter uncertainty Statistics Structural damage Turbines Wind damage Wind power wind turbine Wind turbines |
title | AK‐DA: An efficient method for the fatigue assessment of wind turbine structures |
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