Pierre-Elouan Réthoré

A CFD model of the wake of an offshore wind farm: using a prescribed wake inflow

An CFD model of the wake of an offshore wind farm, expanding existing measurements is proposed. The method is based on solving the Navier Stokes equation in a large domain downstream an offshore wind farm. The inflow of the domain is estimated using existing met mast measurements from both free stream and directly in-wake conditions. A comparison between the simulation results and measurements from a met mast are presented and the shortcomings of the methods are discussed.

Comparison of OpenFOAM and EllipSys3D for neutral atmospheric flow over complex terrain

The flow solvers OpenFOAM and EllipSys3D are compared in the case of neutral atmospheric flow over terrain using the test cases of Askervein and Bolund hills. Both solvers are run using the steady-state Reynolds-averaged Navier–Stokes k– turbulence model. One of the main modeling differences between the two solvers is the wall-function approach. The OpenFOAM v.1.7.1 uses a Nikuradse’s sand roughness model, while EllipSys3D uses a model based on the atmospheric roughness length. It is found that Nikuradse’s model introduces an error dependent on the near-wall cell height. To mitigate this error the near-wall cells should be at least 10 times larger than the surface roughness. It is nonetheless possible to obtain very similar results between EllipSys3D and OpenFOAM v.1.7.1. The more recent OpenFOAM v.2.2.1, which includes the atmospheric roughness length wall-function approach, has also been tested and compared to the results of OpenFOAM v.1.7.1 and EllipSys3D. The numerical results obtained using the same wall-modeling approach in both EllipSys3D and OpenFOAM v.2.1.1 proved to be almost identical. Two meshing strategies are investigated using HypGrid and SnappyHexMesh. The performance of OpenFOAM on SnappyHexMesh-based low-aspect-ratio unstructured meshes is found to be almost an order of magnitude faster than on HypGrid-based structured and high-aspect-ratio meshes. However, proper control of boundary layer resolution is found to be very difficult when the SnappyHexMesh tool is utilized for grid generation purposes. The OpenFOAM is generally found to be 2–6 times slower than EllipSys3D in achieving numerical results of the same order of accuracy on similar or identical computational meshes, when utilization of EllipSys3D default grid sequencing procedures is included.

Roadmap to the multidisciplinary design analysis and optimisation of wind energy systems

A research agenda is described to further encourage the application of Multidisciplinary Design Analysis and Optimisation (MDAO) methodologies to wind energy systems. As a group of researchers closely collaborating within the International Energy Agency (IEA) Wind Task 37 for Wind Energy Systems Engineering: Integrated Research, Design and Development, we have identi_ed challenges that will be encountered by users building an MDAO framework. This roadmap comprises 17 research questions and activities recognised to belong to three research directions: model _delity, system scope and workow architecture. It is foreseen that sensible answers to all these questions will enable to more easily apply MDAO in the wind energy domain. Beyond the agenda, this work also promotes the use of systems engineering to design, analyse and optimise wind turbines and wind farms, to complement existing compartmentalised research and design paradigms.

How Many Model Evaluations Are Required To Predict The AEP Of A Wind Power Plant

Wind farm flow models have advanced considerably with the use of large eddy simulations (LES) and Reynolds averaged Navier-Stokes (RANS) computations. The main limitation of these techniques is their high computational time requirements; which makes their use for wind farm annual energy production (AEP) predictions expensive. The objective of the present paper is to minimize the number of model evaluations required to capture the wind power plants AEP using stationary wind farm flow models. Polynomial chaos techniques are proposed based on arbitrary Weibull distributed wind speed and Von Misses distributed wind direction. The correlation between wind direction and wind speed are captured by defining Weibull-parameters as functions of wind direction. In order to evaluate the accuracy of these methods the expectation and variance of the wind farm power distributions are compared against the traditional binning method with trapezoidal and Simpsons integration rules.The wind farm flow model used in this study is the semi-empirical wake model developed by Larsen [1]. Three test cases are studied: a single turbine, a simple and a real offshore wind power plant. A reduced number of model evaluations for a general wind power plant is proposed based on the convergence of the present method for each case.

Modelling Wind Turbine Inflow: The Induction Zone

A wind turbine decelerates the wind in front of its rotor by extracting kinetic energy. The wind speed reduction is maximal at the rotor and negligible more than five rotor radii upfront. By measuring wind speed this far from the rotor, the turbine’s performance is determined without any rotor bias. However, the measured wind speed decorrelates from the one interacting with the rotor especially in wind farms and mountainous terrain. This is exacerbated by the ever growing rotors, as the physical distance to the measurement location grows equally. Decorrelation is mitigated by measuring closer to the rotor, but requires exact knowledge of the flow deceleration to estimate the available, undisturbed kinetic energy. Thus this thesis explores, mostly numerically, any wind turbine or environmental dependencies of this deceleration. The computational fluid dynamics model (CFD) employed is validated with velocity measurements from lidars upstream of an operational turbine. A new stochastic validation methodology in combination with extensive uncertainty quantification and propagation allows validating the CFD model under these realistic conditions for an area covering the majority of the decelerating flow upstream. This is the first validation of its kind and it demonstrates the advantage of including uncertainties in the process. The flow behaviour upstream of a single rotor is largely insensitive to specific rotor designs and operating conditions. In fact the rotor thrust coefficient is the single most significant parameter. Exploiting this singular dependency, a fast semi-empirical model is devised that accurately predicts the velocity deficit upstream of a single turbine. Near-rotor measurements in combination with this model are able to retrieve the kinetic energy available to the turbine in flat terrain. Complex terrain and multiple turbines are more demanding, though, as they enhance non-linear interactions.

Self-Similarity and helical symmetry in vortex generator flow simulations

According to experimental observations, the vortices generated by vortex generators have previously been observed to be self-similar for both the axial (uz) and azimuthal (uθ) velocity profiles. Further, the measured vortices have been observed to obey the criteria for helical symmetry. This is a powerful result, since it reduces the highly complex flow to merely four parameters. In the present work, corresponding computer simulations using Reynolds- Averaged Navier-Stokes equations have been carried out and compared to the experimental observations. The main objective of this study is to investigate how well the simulations can reproduce the physics of the flow and if the same analytical model can be applied. Using this model, parametric studies can be significantly reduced and, further, reliable simulations can substantially reduce the costs of the parametric studies themselves.