In wind energy, the accuracy of the estimation of the wind resource has an enormous effect on the expected rate of return of a project. For a given project, the wind resource assessor is faced with a difficult choice of a wide range of simulation tools and workflows with varying accuracies (or “skill”) and costs. There is currently no guideline or process available in the industry for helping with the decision of the most “optimal” choice—and this is particularly challenging in mountainous (or “complex”) terrain. In this work, a new decision process for selecting the Wind Resource Assessment (WRA) workflow that would expect to deliver the best compromise between skill and costs for a given wind energy project is developed, with a focus on complex terrain. This involves estimating the expected skill and cost scores using a set of pre-defined weighted parameters. The new process is designed and tested by applying seven different WRA workflows to five different complex terrain sites. The quality of the decision process is then assessed for all the sites by comparing the decision made (i.e., choice of optimal workflow) using the expected skill and cost scores with the decision made using the actual skill and cost scores (obtained by comparing measurements and simulations at a validation location). The results show that the decision process works well, but the accuracy decreases as the site complexity increases. It is therefore concluded that some of the parameter weightings should be dependent on site complexity. On-going work involves collecting more data from a large range of sites, implementing measures to reduce the subjectivity of the process and developing a reliable and robust automated decision tool for the industry.

Micrometeorological observations from a tower, an eddy-covariance (EC) station and an unmanned aircraft system (UAS) at the WINSENT test-site are used to validate a computational fluid dynamics (CFD) model, driven by a mesoscale model. The observation site is characterised by a forested escarpment in a complex terrain. A two-day measurement campaign with a flow almost perpendicular to the escarpment is analysed. The first day is dominated by high wind speeds, while, on the second one, calm wind conditions are present. Despite some minor differences, the flow structure, analysed in terms of horizontal wind speeds, wind direction and inclination angles shows similarities for both days. A real-time strategy is used for the CFD validation with the UAS measurement, where the model follows spatially and temporally the aircraft. This strategy has proved to be successful. Stability indices such as the potential temperature and the bulk Richardson number are calculated to diagnose atmospheric boundary layer (ABL) characteristics up to the highest flight level. The calculated bulk Richardson values indicate a dynamically unstable region behind the escarpment and near the ground for both days. At higher altitudes, the ABL is returning to a near neutral state. The same characteristics are found in the model but only for the first day. The second day, where shear instabilities are more dominant, is not well simulated. UAS proves its great value for sensing the flow over complex terrains at high altitudes and we demonstrate the usefulness of UAS for validating and improving models.

This investigation presents a modelling strategy for wind-energy studies in complex terrains using computational fluid dynamics (CFD). A model, based on an unsteady Reynolds Averaged Navier-Stokes (URANS) approach with a modified version of the standard k-ε model, is applied. A validation study based on the Leipzig experiment shows the ability of the model to simulate atmospheric boundary layer characteristics such as the Coriolis force and shallow boundary layer. By combining the results of the model and a design of experiments (DoE) method, we could determine the degree to which the slope, the leaf area index, and the forest height of an escarpment have an effect on the horizontal velocity, the flow inclination angle, and the turbulent kinetic energy at critical positions. The DoE study shows that the primary contributor at a turbine-relevant height is the slope of the escarpment. In the second step, the method is extended to the WINSENT test site. The model is compared with measurements from an unmanned aircraft system (UAS). We show the potential of the methodology and the satisfactory results of our model in depicting some interesting flow features. The results indicate that the wakes with high turbulence levels downstream of the escarpment are likely to impact the rotor blade of future wind turbines.

A model for the simulation of wind flow in complex terrain is presented based on the Reynolds averaged Navier–Stokes (RANS) equations. For the description of turbulence, the standard k-ε, the renormalization group (RNG) k-ε, and a Reynolds stress turbulence model are applied. Additional terms are implemented in the momentum equations to describe stratification of the Earth’s atmosphere and to account for the Coriolis forces driven by the Earth’s rotation, as well as for the drag force due to forested canopy. Furthermore, turbulence production and dissipation terms are added to the turbulence equations for the two-equation, as well as for the Reynolds stress models, in order to capture different types of land use. The approaches for the turbulence models are verified by means of a homogeneous canopy test case with flat terrain and constant forest height. The validation of the models is performed by investigating the WindForS wind test site. The simulation results are compared with five-hole probe velocity measurements using multipurpose airborne sensor carrier (MASC) systems (unmanned small research aircraft)—UAV at different locations for the main wind regime. Additionally, Reynolds stresses measured with sonic anemometers at a meteorological wind mast at different heights are compared with simulation results using the Reynolds stress turbulence model.

Urban areas significantly influence atmospheric flow fields and momentum exchange processes, which are relevant for wind energy applications and meso-scale atmospheric modeling. However, meso-scale simulations typically represent urban effects using surface roughness parameterizations that neglect volumetric momentum losses within the urban canopy layer. In this study, a methodology is presented to derive a volumetric urban canopy parameterization directly from building-resolved computational fluid dynamics (CFD) simulations. A detailed micro-scale CFD simulation of a real urban region is used to evaluate the momentum balance within a control volume surrounding the urban region. Based on this analysis, two key parameters are derived: the vertical distribution of the House Area Density (HAD), representing the geometric characteristics of the urban morphology, and an effective drag coefficient describing the momentum loss induced by the built environment. These parameters are subsequently implemented as volumetric source terms in a urban canopy model formulated analogously to plant canopy parameterizations. The resulting urban canopy model is validated by comparison with the fully resolved CFD simulation. The results show good agreement in the streamwise momentum balance and pressure loss distribution, while computational cost is significantly reduced. The proposed urban canopy model provides a physically consistent framework for representing urban momentum sinks in meso-scale flow simulations.