Articles | Volume 2, issue 2
Research article
16 Nov 2017
Research article |  | 16 Nov 2017

An engineering model for 3-D turbulent wind inflow based on a limited set of random variables

Manuel Fluck and Curran Crawford

Abstract. Emerging stochastic analysis methods are of potentially great benefit for wind turbine power output and loads analysis. Instead of requiring multiple (e.g. 10 min) deterministic simulations, a stochastic approach can enable a quick assessment of a turbine's long-term performance (e.g. 20-year fatigue and extreme loads) from a single stochastic simulation. However, even though the wind inflow is often described as a stochastic process, the common spectral formulation requires a large number of random variables to be considered. This is a major issue for stochastic methods, which suffer from the curse of dimensionality leading to a steep performance drop with an increasing number of random variables contained in the governing equations. In this paper a novel engineering wind model is developed which reduces the number of random variables by 4–5 orders of magnitude compared to typical models while retaining proper spatial correlation of wind speed sample points across a wind turbine rotor. The new model can then be used as input to direct stochastic simulations models under development. A comparison of the new method to results from the commercial code TurbSim and a custom implementation of the standard spectral model shows that for a 3-D wind field, the most important properties (cross-correlation, covariance, auto- and cross-spectrum) are conserved adequately by the proposed reduced-order method.

Short summary
We present an engineering model of 3-D turbulent wind inflow which reduces the number of random variables required from tens of thousands to ~ 20. This new model is a vital step towards stochastic modelling of wind turbines. Such models can quickly assess turbine lifetime loads and fluctuating power output and thus can be used to design better turbines. However, stochastic models are only viable when the input is expressed with very few random variables, hence the new wind model presented here.
Final-revised paper