Towards Multidisciplinary Wind Turbine Design using High-Fidelity Methods

Imiela, Manfred; Wienke, Felix; Rautmann, Christof; Willberg, Christian; Hilmer, Philipp; Krumme, Alexander

doi:10.2514/6.2015-1462

Cited by 11 publications

(10 citation statements)

References 11 publications

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“…In case II, only the time-averaged velocity fluctuation u ′ i in main flow (x-) direction is used, while case III considers the entire definition of Equation (6). It has to be remembered that in cases II and III, the turbulence BC are not consistent with the turbulence model and the associated constants.…”

Section: Inflow Conditions Of the Les-rans Abl Profilementioning

confidence: 99%

“…Since then, RANS computations have become the working horse in industrial context because of its comparatively low costs. The multiple fields of application in wind energy range from the analysis of wake characteristics, interaction between terrain and HAWT, transition behaviour, and aeroelastic effects . Large eddy simulations (LES) are nowadays used to analyse the interaction between the atmospheric boundary layer (ABL) or terrain and the HAWT wake .…”

Section: Introductionmentioning

confidence: 99%

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Development of an interface to introduce stationary LES data to the URANS solver THETA for HAWT performance prediction

Länger-Möller¹

2019

Wind Energy

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The impact of different sheared velocity profiles on the performance prediction of a horizontal axis wind turbine in the atmospheric boundary layer is investigated. Firstly, the wall roughness in the analytical logarithmic description of the atmospheric boundary layer is varied to obtain different velocity profiles. Subsequently, it is proposed to replace the analytical logarithmic description of the atmospheric boundary layer by the time-averaged velocity data of a precursor large eddy simulation (LES) and to reconstruct the turbulence of the velocity fluctuations. The LES data are introduced as inflow condition through a LES-RANS interface in a one-way coupling approach. Three different methods to reconstruct URANS turbulence values out of the velocity fluctuations are investigated. It is shown that the reconstruction method has an impact on the development of the velocity profile, turbulent kinetic energy, and the turbulent dissipation during the transport through the URANS domain. The different inflow data, which the horizontal axis wind turbine experiences, are responsible for changes in the overall rotor thrust (up to 2.7%) and rotor torque (up to 2.4%). Conversely, the induction factors and effective angles of attack hardly change and can well be compared with a blade element momentum method. Finally, the results of both approaches to prescribe the atmospheric boundary layer are compared. The thrust and power coefficients, and wake recovery are close to each other. Simulations are carried out on an industrial 900 kW wind turbine with the incompressible URANS solver THETA. KEYWORDSatmospheric boundary layer, horizontal axis wind turbine, incompressible URANS solver, LES-RANS interface, logarithmic velocity profile, turbulence model boundary condition, turbulence reconstruction INTRODUCTIONComputational fluid dynamic (CFD) tools that are used to analyse the aerodynamic behaviour of horizontal axis wind turbines (HAWT) can be classified in three categories. In the field of HAWT, the first CFD computation with a Reynolds-averaged Navier-Stokes (RANS) solver dates back to 1998. 1 Since then, RANS computations have become the working horse in industrial context because of its comparatively low costs. The multiple fields of application in wind energy range from the analysis of wake characteristics, 2 interaction between terrain and HAWT, 3 transition behaviour, 4 and aeroelastic effects. 5,6 Large eddy simulations (LES) are nowadays used to analyse the interaction between the atmospheric boundary layer (ABL) or terrain and the HAWT wake. 7-9 During the simulation, the HAWT is modelled by an actuator line model, while the grid spacing of the LES grid reaches up to one rotor diameter. The third approach comprises all variations of detached eddy simulations (DES) that are usually used to link the scales between the LES and the RANS for specific wind turbine configurations, for example, the analysis of unsteady loads during stall in rotor-upwind configurations 10 or standstill. 5 In order to improve the performance a...

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Section: Inflow Conditions Of the Les-rans Abl Profilementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an interface to introduce stationary LES data to the URANS solver THETA for HAWT performance prediction

Länger-Möller¹

2019

Wind Energy

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“…However, such an optimization includes many constraints that make the problem highly difficult and complex. Studies by Yu and Kwon [8] and Imiela et al [9] are a few examples in the literature for the multidisciplinary analysis of wind turbines using high fidelity methods. A common approach to simplify the complexity of such a design is to use low fidelity methods on one or more branches of the multidisciplinary research.…”

Section: Introductionmentioning

confidence: 99%

A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades

Kaya

2019

Sustainability

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Computational fluid dynamics (CFD) is a powerful tool to estimate accurately the aerodynamic loads on wind turbine blades at the expense of high requirements like the duration of computation. Such requirements grow in the case of blade shape optimization in which several analyses are needed. A fast and reliable way to mimic the CFD solutions is to use surrogate models. In this study, a machine learning technique, the support vector regression (SVR) method based on a set of CFD solutions, is used as the surrogate model. CFD solutions are calculated by solving the Reynolds-averaged Navier–Stokes equation with the k-epsilon turbulence model using a commercial solver. The support vector regression model is then trained to give a functional relationship between the spanwise twist distribution and the generated torque. The smooth twist distribution is defined using a three-node cubic spline with four parameters in total. The optimum twist is determined for two baseline blade cases: the National Renewable Energy Laboratory (NREL) Phase II and Phase VI rotor blades. In the optimization process, extremum points that give the maximum torque are easily determined since the SVR gives an analytical model. Results show that it is possible to increase the torque generated by the NREL VI blade more than 10% just by redistributing the spanwise twist without carrying out a full geometry optimization of the blade shape with many shape-defining parameters. The increase in torque for the NREL II case is much higher.

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“…Instead, compressible as well as incompressible computational fluid dynamics (CFD) solvers were applied to wind turbines without validating the assumption of incompressible flows and its limits by examining pressure distributions of blade sections. Among the compressible CFD solvers is Deutsches Zentrum fur Luft‐ und Raumfahrt (DLR) solver TAU, presented by Imiela and Wienke, ONERA's structured flow solver elsA, which was presented by Lecanu and Le Pape, OVERFLOW, which was used in many studies presented by, for example, Duque et al. , Stone et al.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the NREL phase VI experiment with the incompressible CFD solver THETA

2017

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DLR's incompressible flow solver THETA is introduced for wind turbine applications on the isolated NREL phase VI Unsteady Aerodynamic Experiment rotor. The optimization of parameter settings for the prediction of attached and separated flows on the rotor is performed, including time step size, spatial discretization scheme, turbulence models and Chimera overset grid technique. Afterwards, a systematic study on the pressure distributions, rotor thrust and rotor torque for experimental series S, I and J is performed. The accuracy of results for wind velocities between 7 and 25 m s 1 , covering attached, partly separated and deep stalled flow conditions, is discussed. While good to excellent agreement between THETA sectional pressure distributions and the experimental reference data is achieved in attached flow and deep stall configurations, more efforts are needed to predict partly separated flow cases with comparable accuracy. A code-to-code comparison with DLR's compressible flow solver TAU enabled the quantification of differences in pressure prediction because of the use of incompressible and compressible flow solvers.

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Towards Multidisciplinary Wind Turbine Design using High-Fidelity Methods

Cited by 11 publications

References 11 publications

Development of an interface to introduce stationary LES data to the URANS solver THETA for HAWT performance prediction

Development of an interface to introduce stationary LES data to the URANS solver THETA for HAWT performance prediction

A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades

Investigation of the NREL phase VI experiment with the incompressible CFD solver THETA

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