The Bolund Experiment, Part I: Flow Over a Steep, Three-Dimensional Hill

We present an analysis of data from a measurement campaign performed at the Bolund peninsula in Denmark in the winter of 2007–2008. Bolund is a small isolated hill exhibiting a significantly steep escarpment in the main wind direction. The physical shape of Bolund represents, in a scaled-down form, a typical wind turbine site in complex terrain. Because of its small size the effect of atmospheric stratification can be neglected, which makes the Bolund experiment ideal for the validation of neutral flow models and hence model scenarios most relevant to wind energy. We have carefully investigated the upstream conditions. With a 7-km fetch over water, the incoming flow is characterized as flow over flat terrain with a local roughness height based on the surface momentum flux. The nearly perfect upstream conditions are important in forming a meaningful quantitative description of the flow over the Bolund hill. Depending on the wind direction, we find a maximum speed-up of 30% at the hill top accompanied by a maximum 300% enhancement of turbulence intensity. A closer inspection reveals transient behaviour with recirculation zones. From the wind energy context, this implies that the best site for erecting a turbine based on resource constraints unfortunately also imposes a penalty of high dynamic loads. On the lee side of Bolund, recirculation occurs with the turbulence intensity remaining significantly enhanced even at one hill length downstream. Its transient behaviour and many recirculation zones place Bolund in a category in which the linear flow theory is not applicable

Results of the GABLS3 diurnal-cycle benchmark for wind energy applications

© Published under licence by IOP Publishing Ltd. We present results of the GABLS3 model intercomparison benchmark revisited for wind energy applications. The case consists of a diurnal cycle, measured at the 200-m tall Cabauw tower in the Netherlands, including a nocturnal low-level jet. The benchmark includes a sensitivity analysis of WRF simulations using two input meteorological databases and five planetary boundary-layer schemes. A reference set of mesoscale tendencies is used to drive microscale simulations using RANS k- and LES turbulence models. The validation is based on rotor-based quantities of interest. Cycle-integrated mean absolute errors are used to quantify model performance. The results of the benchmark are used to discuss input uncertainties from mesoscale modelling, different meso-micro coupling strategies (online vs offline) and consistency between RANS and LES codes when dealing with boundary-layer mean flow quantities. Overall, all the microscale simulations produce a consistent coupling with mesoscale forcings.status: publishe

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region