A novel framework for coupling mesoscale and steady-state CFD models for wind resource assessment
Doctoral thesis

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Date
2020Metadata
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- Doctoral theses (MINA) [106]
Abstract
The estimation of the energy production of wind farms is a key factor for the development of wind energy projects. Currently, these estimations utilize only a few onsite measurement points to estimate the wind resource at the location of the wind turbines by means of a wind flow model. One of the most advanced wind flow models utilized in the wind energy industry for this purpose are the steady-state computational fluid dynamic (CFD) models. These models have proven to be successful in modelling the wind flow in complex terrain. Nevertheless, there are some limitations in their applicability at sites with complex weather patterns.
In this PhD thesis, these limitations are addressed by coupling a CFD model with a mesoscale meteorological model (MMM). MMMs are widely used for weather forecast and can reproduce the complex weather phenomena that a CFD model lacks. In this study, the framework to couple both models consists in utilizing the mesoscale simulation results to compute the boundary conditions of the CFD model. Two variants of the meso-microscale coupling approach are here studied.
The first approach consists in utilizing the average values of the mesoscale fields by wind directional sector. It is shown that this approach improves the wind estimations in complex terrain and in areas that are located at the wake of the terrain features of a site. Nevertheless, the approach presents important limitations in sites where the wind blows from few wind directions. The second approach addresses this limitation by extracting weather patterns from the mesoscale simulations by means of a fully automated clustering methodology. This classification technique is capable of extracting the predominant weather
patterns and organizing them in a meaningful way. Overall, by downscaling the extracted patterns the modelling error is reduced compared with the mesoscale model. Such a methodology has a lot of potential for wind turbine wake studies as well as for forecasting solutions that utilize CFD models.