Predicting the occurrence of the vortex ring state for floating offshore wind turbines

The local aerodynamic loading on floating offshore wind turbines (FOWTs) is more complex than on bottom-fixed wind turbines due to the platform motions. In particular, the FOWT rotor may start to interact with its own wake and enter a so-called vortex ring state (VRS). However, it is still unclear when, and to what extent, the VRS may happen to floating offshore wind turbines. In this paper, we quantitatively predict the VRS using Wolkovitch's criterion during the operating conditions of different FOWTs simulated by FAST. The results show that the type of floating foundation has a significant influence on the aerodynamic performance of the rotor. Also, the probability of occurrence of VRS is bigger for the floating platforms that are more sensitive to wave excitations.Wind Energ

The aerodynamics of floating offshore wind turbines in different working states during surge motion

The rotor of floating offshore wind turbines with platform motions may undergo different working states during its operation, e.g. from windmill working state to vortex ring and propeller working state. In this paper, an aerodynamic model based on a free wake vortex method is used to simulate the rotor undergoing surge motion. The associated change of working states of the rotor is evaluated quantitatively and visually. The results show that during a full cycle of the surge motion of the floating platform, the rotor experiences alternative onset of the windmill state, vortex ring state, and propeller state, while the later two occur only during the downwind motion of the rotor. The aerodynamic load change corresponding to different working states of the rotor indicates that the vortex ring state is the most unstable phase of the three.Wind Energ

Comparative analysis of different criteria for the prediction of vortex ring state of floating offshore wind turbines

The wind condition around floating offshore wind turbines (FOWTs) can be largely different from that developed around bottom-mounted wind turbines due to the platform motions. The existing literature identifies four working state of FOWTs, one of them being the vortex ring state (VRS) which may occur as the rotor moves in its own wake. It is potentially a problem that influences the aerodynamic performance and lifetime of FOWTs. It is still unclear when, and to what extent, does the VRS happen to floating offshore wind turbines. The aim of this paper is to quantitatively predict the occurrence of VRS during the operation of FOWTs. Three different criteria are used and compared: the axial induction factor, Wolkovitch's criterion and Peters’ criterion. The results show that the VRS phenomena may occur for a large range of operating conditions and can be correlated with the minima in the relative wind speed normal to the rotor plane. Also, the probability of occurrence of VRS is smaller for the floating platforms that exhibit the least motions such as the TLP. Finally, Wolkovitch's criterion seems to be the most suitable one for the VRS prediction, while Peters criterion indicates the initial aerodynamic change and is thus suitable for early warning of VRS.Wind Energ

Steady-state aeroelasticity of a ram-air wing for airborne wind energy applications

In this paper we present a computational approach to simulate the steady-state aeroelastic deformation of a ram-air kite for airborne wind energy applications. The approach is based on a computational fluid dynamics (CFD) solver that is two-way coupled with a finite element (FE) solver. All components of the framework, including the meshing tools and the coupling library, are available in open source. The flow around the wing is described by the steady-state Reynolds-averaged Navier-Stokes (RANS) equations closed by an SST turbulence model. The FE model of the cellular membrane structure includes a wrinkling model and uses dynamic relaxation to find the deformed steady-state shape. Each simulation comprises four distinct steps: (1) generating the FE mesh of the design geometry, (2) pre-inflation of the wing, applying a uniform pressure on the inside, (3) generating the CFD mesh around the pre-inflated wing, and (4) activating the exterior flow and two-way coupling iterations. We first present results for the aerodynamics of the pre-inflated rigid ram-air wing and compare these to similar results for a leading edge inflatable (LEI) tube kite. Both wings are characterized by a high anhedral angle and low aspect ratio which induce spanwise flows that reduce the aerodynamic performance. The comparison shows a better performance for the LEI wing which can be attributed to its higher aspect ratio. The aeroelastic deformation of the ram-air wing further improves the aerodynamic performance, primarily because of the increasing camber which in turn increases the lift force. A competing aeroelastic phenomenon is the formation of bumps near the leading edge which increase the drag.Wind Energ

Analysis the vortex ring state and propeller state of floating offshore wind turbines and verification of their prediction criteria by comparing with a CFD model

In our previous study, the vortex ring state (VRS) prediction criteria were introduced from helicopter's realm and applied to floating offshore wind turbines (FOWTs). The existence of the VRS on FOWTs was also successfully predicted. However, the prediction criteria we used have not been verified by comparing them with similar studies because of the lack of reference publications — until recently. In this paper, a comparative analysis of the VRS phenomenon of an FOWT is done and aerodynamic performance of the FOWT is evaluated. We compare the VRS results predicted based on the criteria we proposed with a new study about the VRS by means of a computational fluid dynamics (CFD) method. The aerodynamic performance of an FOWT undergoing surge motions is simulated with an in-house code based on a free wake vortex method. Similarities and differences of the two studies are compared and discussed. The propeller state of the rotor is further analyzed to gain a deeper understanding of the working state change of FOWTs as well as to strengthen the research in this area.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Wind Energ

Data-driven turbulence modeling for wind turbine wakes under neutral conditions

Currently, the state of the art in wind farm flow physics modeling are Large Eddy Simulations (LES) which resolve a large part of the spectra of the turbulent fluctuations. But this type of model requires extensive computational resources. One wind speed and direction simulation of the Lillgrund wind farm can take between 160k and 3000k processor hours depending on how the turbines are modeled [1, 2]. The next-fidelity model types are Reynolds-Averaged Navier-Stokes (RANS) models which resolve only the mean quantities and model the effect of turbulence fluctuations. These models require about two orders of magnitude less computational time, but generally do not produce accurate predictions of the mean flow field. Proposed modifications made to these models so far do not generalize well and there is room for improvement. Hence, we present the first steps towards using a data-driven approach to aid in deriving new RANS models that generalize well to different turbine types, varying atmospheric stability, and farm layouts. To do so, time-averaged LES data is used to derive corrections to existing RANS models. The approach uses a deterministic symbolic regression method to infer algebraic correction terms to the RANS turbulence transport equations. Optimal correction terms to the RANS equations are derived using a frozen approach where time-averaged flow fields from LES are injected into the RANS equations. The potential of the approach is demonstrated under neutral conditions for multi-turbine constellations at wind-tunnel scale. The results show promise, but more work is necessary to realize the full potential of the approach. Wind EnergyAerodynamic

Aeroelastic validation and Bayesian updating of a downwind wind turbine

Downwind wind turbine blades are subjected to tower wake forcing at every rotation, which can lead to structural fatigue. Accurate characterisation of the unsteady aeroelastic forces in the blade design phase requires detailed representation of the aerodynamics, leading to computationally expensive simulation codes, which lead to intractable uncertainty analysis and Bayesian updating. In this paper, a framework is developed to tackle this problem. Full, detailed aeroelastic model of an experimental wind turbine system based on 3-D Reynolds-averaged Navier-Stokes is developed, considering all structural components including nacelle and tower. This model is validated against experimental measurements of rotating blades, and a detailed aeroelastic characterisation is presented. Aerodynamic forces from prescribed forced-motion simulations are used to train a time-domain autoregressive with exogenous input (ARX) model with a localised forcing term, which provides accurate and cheap aeroelastic forces. Employing ARX, prior uncertainties in the structural and rotational parameters of the wind turbine are introduced and propagated to obtain probabilistic estimates of the aeroelastic characteristics. Finally, the experimental validation data are used in a Bayesian framework to update the structural and rotational parameters of the system and thereby reduce uncertainty in the aeroelastic characteristics.AerodynamicsWind Energ

Classifying Regions of High Model Error Within a Data-Driven RANS Closure: Application to Wind Turbine Wakes

Data-driven Reynolds-averaged Navier–Stokes (RANS) turbulence closures are increasing seen as a viable alternative to general-purpose RANS closures, when LES reference data is available—also in wind-energy. Parsimonious closures with few, simple terms have advantages in terms of stability, interpret-ability, and execution speed. However experience suggests that closure model corrections need be made only in limited regions—e.g. in the near-wake of wind turbines and not in the majority of the flow. A parsimonious model therefore must find a middle ground between precise corrections in the wake, and zero corrections elsewhere. We attempt to resolve this impasse by introducing a classifier to identify regions needing correction, and only fit and apply our model correction there. We observe that such classifier-based models are significantly simpler (with fewer terms) than models without a classifier, and have similar accuracy, but are more prone to instability. We apply our framework to three flows consisting of multiple wind-turbines in neutral conditions with interacting wakes.Wind EnergyAerodynamic

Data-driven RANS closures for wind turbine wakes under neutral conditions

The state-of-the-art in wind-farm flow-physics modeling is Large Eddy Simulation (LES) which makes accurate predictions of most relevant physics, but requires extensive computational resources. The next-fidelity model types are Reynolds-Averaged Navier–Stokes (RANS) which are two orders of magnitude cheaper, but resolve only mean quantities and model the effect of turbulence. They often fail to accurately predict key effects, such as the wake recovery rate. Custom RANS closures designed for wind-farm wakes exist, but so far do not generalize well: there is substantial room for improvement. In this article we present the first steps towards a systematic data-driven approach to deriving new RANS models in the wind-energy setting. Time-averaged LES data is used as ground-truth, and we first derive optimal corrective fields for the turbulence anisotropy tensor and turbulence kinetic energy (t.k.e.) production. These fields, when injected into the RANS equations (with a baseline k–ɛ model) reproduce the LES mean-quantities. Next we build a custom RANS closure from these corrective fields, using a deterministic symbolic regression method to infer algebraic correction as a function of the (resolved) mean-flow. The result is a new RANS closure, customized to the training data. The potential of the approach is demonstrated under neutral atmospheric conditions for multi-turbine constellations at wind-tunnel scale. The results show significantly improved predictions compared to the baseline closure, for both mean velocity and the t.k.e. fields.Wind EnergyAerodynamic

Probabilistic surrogates for floating wind-turbine load emulation

Wind EnergyAerodynamic