OC6 Phase I: Investigating the underprediction of low-frequency hydrodynamic loads and responses of a floating wind turbine

Robertson, Amy; Gueydon, Sébastien; Bachynski, Erin Elizabeth; Wang, L; Jonkman, Jason; Alarcón, Daniel; Amet, Ervin; Beardsell, Alec; Bonnet, Paul; Boudet, B; Brun, Cédric; Chen, Z; Féron, Marie; Forbush, Dominic; Galinos, Christos; Galván, Josean; Gilbert, Philippe; Gómez, J.I. Baquero; Harnois, Violette; Haudin, Florence; Hu, Zhiqiang; Dreff, Jean-Baptiste Le; Leimeister, Mareike; Lemmer, Frank; Li, H; McKinnon, Gill; Mendikoa, I.; Moghtadaei, Abdolmajid; Netzband, Stefan; Oh, Sehoon; Pegalajar‐Jurado, Antonio; Nguyen, Minh Quân; Ruehl, Kelley; Schünemann, Paul; Shi, Wei; Shin, Hyunkyoung; Si, Yulin; Surmont, Florian; Trubat, Pau; Qwist, J; Wohlfahrt-Laymann, S

doi:10.1088/1742-6596/1618/3/032033

Cited by 60 publications

(69 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MoWiT is developed by engineers at Fraunhofer IWES. This allows continuous enhancement and extension of the library, including also verification and validation of the code [35,[38][39][40][41]. Thus, different theories and approaches are implemented to represent the aero-hydro-servo-elastic dynamics of a wind turbine system and the degree of detail is refined on and on.…”

Section: Continuous Enhancement and Extensionmentioning

confidence: 99%

Development of a Framework for Wind Turbine Design and Optimization

2021

View full text Add to dashboard Cite

Dimensioning and assessment of a specific wind turbine imply iterative steps for design optimization, as well as load calculations and performance analyses of the system in various environmental conditions. However, due to the complexity of wind turbine systems, fully coupled aero-hydro-servo-elastic codes are indispensable to represent and simulate the non-linear system behavior. To cope with the large number of simulations to be performed during the design process of a wind turbine system, automation of simulation executions and optimization procedures are required. In this paper, such a holistic simulation and optimization framework is presented, by which means iterative simulations within the wind turbine design assessment and development processes can be managed and executed in an automated and high-performance manner. The focus lies on the application to design load case simulations, as well as the realization of automated optimizations. The proper functioning and the high flexibility of the framework tool is shown based on three exemplary optimization tasks.

show abstract

Section: Continuous Enhancement and Extensionmentioning

confidence: 99%

Development of a Framework for Wind Turbine Design and Optimization

2021

View full text Add to dashboard Cite

show abstract

“…With several projects, it has greatly promoted the development of FOWTs. For example, Hywind and DeepCwind in the OC projects (Jonkman and Musial, 2010;Robertson et al, 2014Robertson et al, , 2017Robertson et al, , 2020 and OO-Star Wind Floater (Pegalajar-Jurado et al, 2018), Nautilus steel semisubmersible (Galván et al, 2018), IDEOL concrete floater (Beyer et al, 2015) in LIFES50+ project, etc.…”

Section: Introductionmentioning

confidence: 99%

Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs

Chen

2021

Front. Mar. Sci.

View full text Add to dashboard Cite

Floating offshore wind turbines (FOWTs) still face many challenges on how to better predict the dynamic responses. Artificial intelligence (AI) brings a new solution to overcome these challenges with intelligent strategies. A new AI technology-based method, named SADA, is proposed in this paper for the prediction of dynamic responses of FOWTs. Firstly, the methodology of SADA is introduced with the selection of Key Disciplinary Parameters (KDPs). The AI module in SADA was built in a coupled aero-hydro-servo-elastic in-house program DARwind and the policy decision is provided by the machine learning algorithms deep deterministic policy gradient (DDPG). Secondly, a set of basin experimental results of a Hywind Spar-type FOWT were employed to train the AI module. SADA weights KDPs by DDPG algorithms' actor network and changes their values according to the training feedback of 6DOF motions of Hywind platform through comparing the DARwind simulation results and that of experimental data. Many other dynamic responses that cannot be measured in basin experiment could be predicted in higher accuracy with this intelligent DARwind. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform's motions can be predicted by AI-based DARwind with higher accuracy, for example the maximum error of surge motion is reduced by 21%. This proposed SADA method takes advantage of numerical-experimental method and the machine learning method, which brings a new and promising solution for overcoming the handicap impeding direct use of traditional basin experimental technology in FOWTs design.

show abstract

“…One major limitation of engineering-level tools commonly used by FOWT designers identified in OC5 and OC6 is the significant underprediction of the low-frequency, nonlinear wave excitation and response of semisubmersible FOWTs [2,3]. Although the nonlinear wave loads are generally much smaller than those from direct (linear) wave excitation, they can potentially trigger large surge and pitch motion in a semisubmersible FOWT, because the surge and pitch resonance frequencies of this type of system are specifically designed to fall in the low-frequency range to avoid direct wave excitation [4][5][6]; therefore, the underprediction of nonlinear wave excitation and the resulting response can have major consequences for the safety and reliability of the FOWT design.…”

Section: Introductionmentioning

confidence: 99%

“…Although the nonlinear wave loads are generally much smaller than those from direct (linear) wave excitation, they can potentially trigger large surge and pitch motion in a semisubmersible FOWT, because the surge and pitch resonance frequencies of this type of system are specifically designed to fall in the low-frequency range to avoid direct wave excitation [4][5][6]; therefore, the underprediction of nonlinear wave excitation and the resulting response can have major consequences for the safety and reliability of the FOWT design. As part of OC6 Phase I, a recent paper by Robertson et al [3] documented the extensive effort made by many institutions worldwide in tuning various engineering-level models to address this particular issue. Some model features, such as the incorporation of full Quadratic Transfer Functions (QTFs) for wave excitation from second-order potential-flow theory and the use of fully nonlinear wave kinematics for the Morison drag computation, have been found to improve results.…”

Section: Introductionmentioning

confidence: 99%

“…The full-scale simulation results were obtained using an OpenFAST model without any aerodynamic/tower effects to be consistent with the experiment. (For more details on the model and experiment, see [2,3,9].) The low-frequency excitation predicted by the OpenFAST model primarily came from the QTFs of second-order potential-flow theory and the Morison drag.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty Assessment of CFD Investigation of the Nonlinear Difference-Frequency Wave Loads on a Semisubmersible FOWT Platform

et al. 2020

View full text Add to dashboard Cite

Current mid-fidelity modeling approaches for floating offshore wind turbines (FOWTs) have been found to underpredict the nonlinear, low-frequency wave excitation and the response of semisubmersible FOWTs. To examine the cause of this underprediction, the OC6 project is using computational fluid dynamics (CFD) tools to investigate the wave loads on the OC5-DeepCwind semisubmersible, with a focus on the nonlinear difference-frequency excitation. This paper focuses on assessing the uncertainty of the CFD predictions from simulations of the semisubmersible in a fixed condition under bichromatic wave loading and on establishing confidence in the results for use in improving mid-fidelity models. The uncertainty for the nonlinear wave excitation is found to be acceptable but larger than that for the wave-frequency excitation, with the spatial discretization error being the dominant contributor. Further, unwanted free waves at the difference frequency have been identified in the CFD solution. A wave-splitting and wave load-correction procedure are presented to remove the contamination from the free waves in the results. A preliminary comparison to second-order potential-flow theory shows that the CFD model predicted significantly higher difference-frequency wave excitations, especially in surge, suggesting that the CFD results can be used to better calibrate the mid-fidelity tools.

show abstract

OC6 Phase I: Investigating the underprediction of low-frequency hydrodynamic loads and responses of a floating wind turbine

Cited by 60 publications

References 5 publications

Development of a Framework for Wind Turbine Design and Optimization

Development of a Framework for Wind Turbine Design and Optimization

Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs

Uncertainty Assessment of CFD Investigation of the Nonlinear Difference-Frequency Wave Loads on a Semisubmersible FOWT Platform

Contact Info

Product

Resources

About