On Stochastic Reduced-Order and LES-based Models of Offshore Wind Turbine Wakes

Mostafa Bakhoday Paskyabi,Joachim Reuder,Maria Krutova,Omar El Guernaoui,Finn Gunnar Nielsen

doi:10.1088/1742-6596/1669/1/012018

Mostafa Bakhoday Paskyabi, Joachim Reuder + Show 3 more

Open Access

https://doi.org/10.1088/1742-6596/1669/1/012018

Copy DOI

Abstract

In this paper, the primary objective is to investigate flow structures in the wake of wind turbines based on applying a truncated Proper Orthogonal Decomposition (POD) approach. This scheme decomposes the three-dimensional velocity fields produced by the high-fidelity PArallelized LES Model (PALM) into a number of orthogonal spatial modes and time-dependent weighting coefficients. PALM has been combined with an actuator disk model with rotation to incorporate the effects of a turbine array. The time-dependent deterministic weights from applying the POD scheme are replaced by stochastic weights, estimated from two independent stochastic techniques that aim to account for unresolved small-scale features for a number of POD modes. We then reconstruct the flow field by a small number of stochastic modes to investigate how well the applied stochastic methodologies can reproduce the flow field compared to the original LES results.

Highlights

Variable wakes and wake-wake interaction within the area of a wind farm play a key role in governing the performance of the wind farm and controlling the fatigue loads and intermittency events acting on downwind turbines
The shown results in this figure are associated with the simulations under stable boundary condition in order to show the effects of atmospheric boundary layer height variability on developments of wake
In this study, we used LES snapshots under neutral atmospheric boundary condition, that incorporate the effects of wind turbines, to develop low-order models based on a truncated Proper Orthogonal Decomposition (POD) approach

Summary

Introduction

Variable wakes and wake-wake interaction within the area of a wind farm play a key role in governing the performance of the wind farm (e.g. its total power output) and controlling the fatigue loads and intermittency events acting on downwind turbines. How to predict key wake features, such as meandering of the wake, far-field (approximately ≥4 rotor diameter) recovery of the velocity deficit, effects of rotor geometry, wake interactions with shear and turbulence in the atmospheric boundary layer, nearfield rotor-induced turbulence, and wake-induced turbine loads have led to emergence of different modelling approaches over the last decade, see for example [1] We may classify these models into three main groups: empirical models that estimate analytically the wake velocity based on some over-simplified assumptions and ideal hypothesis [4, 8]; models that treat turbines as roughness elements applicable for wake loss prediction over a large wind farm [10, 3]; and Computational

Objectives

Methods

Results

Conclusion