09:00 - 10:30 Challenges of forest modelling
Resource assessment
Room: Hall G1
In this session we will get deep into the forest! We will look at how computational fluid dynamics (CFD) can be used in modelling the flow in forested areas, how to model profiles and also the turbulence. Different models will be applied and there will be lots of data from real forested sites around the world. The effects of using different input data will be investigated, and the use of lidar LIDAR scans to estimate roughness and other forest related parameters will be discussed. We will also hear about a series of experiments aiming amongst others to improve our understanding of flow in forested areas.
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Learning objectives
- Understand how to use CFD in forested areas;
- Understand how to model turbulence and profiles;
- Evaluate the different inputs for forest modelling;
- Understand what needs to be measured in forest-related experiments.
Presenter
Co-authors:
Gibson Kersting (1) F Catherine Meissner (2) Jan Borras (2)
(1) E.ON Climate & Renewables, Austin, United States (2) WindSim, Tonsberg, Norway
Presenter's biography
Biographies are supplied directly by presenters at WindEurope Summit 2016 and are published here uneditedGibson Kersting has bachelor’s degree in Physics from Univesidade Federal do Rio Grande do Sul in Brazil with emphasis in Mathematical Physics and Computer modeling. Gibson Kersting has been a Senior Analyst at E.ON in Austin, Texas since 2012. Previously Gibson Kersting worked both in Rome, Italy and Austin, USA as a Wind Analyst. The main focus of Mr. Kersting’s work at E.ON are CFD simulations, uncertainty calculations, and working with the Risk Assessment and Financial department at E.ON.
Abstract
Validation of CFD based forest modelling for large forested areas with many measurement masts
Introduction
Forested areas show larger uncertainties in wind flow modeling than non-forested areas. The reason is the non-linear influence of the forest onto the wind flow and the difficulties to describe this influence by physical models. For computational fluid dynamics (CFD) modeling many forest models have been developed and many validation studies have been run. These studies often lack the availability of good measurement data and a detailed map of the forest heights. With only one or two measurement masts onsite which do not reach higher up into the boundary layer and no atmospheric stability information a good validation of the forest models is almost impossible.
In recent years the awareness has grown that more measurements are necessary in forested sites to get a reliable wind resource assessment and that remote sensing devices can reduce uncertainty. Also the mapping of tree heights has become much more accurate. Therefore, more sites are now available which have many measurement masts, atmospheric stability measurements and some of them have also remote sensing data. Thereby, a more in depth validation of CFD forest models is possible setting up the models based on detailed forest height maps.
Approach
This study has validated CFD forest models on sites with up to 8 measurement masts with heights between 80 and 120 meter. One of them has also remote sensing measurements. We will discuss the advantages and disadvantages of the different forest models.
Comparisons will be shown between simulations with detailed forest height maps and only rough estimated tree height maps.
As the sites are mostly in Northern Europe and therefore often face stable atmospheric conditions the stability classification from measured data and from high resolution mesoscale models will be used to set the CFD model parameters correctly and the feasibility of stability classification from mesoscale models will be discussed.
Main body of abstract
Validation of CFD forest models is often limited to smaller forest areas but especially in Northern Europe often the forest extension is quite wide and the boundary layer over the forest is quite well established. If a CFD model is able to keep the correct vertical wind profile over several kilometers over forested areas and if the development of the profile is simulated correctly is often difficult to judge due to only one or two measurement masts available. With the projects available for this study such questions can be answered and as the met towers reach up to hub height and some remote sensing measurements are available a really meaningful validation is possible which reduces the uncertainty about how CFD models behave in hub height over forested areas.
As the consideration of atmospheric stability in CFD simulations is important in Northern Europe information about atmospheric stability from mesoscale models is used. Those models often provide the Richardson number but using the theoretical classification after the book does often lead to false classifications and attention has to be paid to unreasonable values sometimes produced by the mesoscale model. In this study we present an approach how to classify atmospheric stability for CFD models from the Richardson number produced by a mesoscale model.
Conclusion
Modeling the vertical wind profile over large forested areas require a careful set-up of the CFD forest model and especially in Northern Europe the knowledge of the atmospheric stability. It is possible to take the atmospheric stability classification from mesoscale models if that classification is used with some predefined rules.
Learning objectives
The delegates will learn more about the performance of CFD forest models on large forested sites and about the possibility to classify atmospheric stability from mesoscale models for the use in CFD models.
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