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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'How does the wind blow behind wind turbines and in wind farms?' taking place on Tuesday, 11 March 2014 at 16:30-18:00. The meet-the-authors will take place in the poster area.

Cian Desmond Loughborough University, United Kingdom
Co-authors:
Cian Desmond (1) F P Simon Watson (1) Mark Pearson (2)
(1) Loughborough University, Leicestershire, United Kingdom (2) Mainstream Renewable Power Ltd., London, United Kingdom

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Abstract

A METHODOLOGY FOR THE MEASUREMENT AND MODELLING OF STABILITY EFFECTS.

Introduction

It has commonly been assumed that, at the wind speeds at which wind turbines operate, inertial forces will prevail over those induced by atmospheric stability and so it is unnecessary to include the effects of buoyancy in resource assessments. However, as we move to more complex and lower wind speed sites it is found that this neutral assumption is often invalid.

In this paper we present a methodology both for the measurement of non-neutral events in the field and the inclusion of the resulting stability effects in resource assessment.



Approach

Stability effects have a severe impact on the wind resource as they alter the wind shear, turbulence levels and the distance to which wake effects persist. These factors impact the potential energy capture of wind turbines and also increase the uncertainty associated with flow simulations for the purpose resource assessment.

Unfortunately, the evolution of stability effects within the atmospheric boundary layer (ABL) is an intricate process which depends on many factors such as wind shear, ambient turbulence levels, wind speeds, the Coriolis Effect, potential temperature gradients and vertical heat fluxes. Due to this complexity, there is a lack of consensus as to what exactly should be measured on site in order to assess to prevalence and severity of non-neutral events.

In a previous paper (Desmond & Watson, 2012) we assessed the ability of a number of stability metrics (the Richardson number, Obukhov length, stability parameter, standard deviation of direction etc.) to identify stability events of consequence to the wind energy industry . During the course of this analysis we identified a methodology whereby standard site measurements (wind shear, turbulence intensity) can be used in conjunction with concurrent solar irradiance data to accurately identify stable, neutral and unstable events.

In this paper we will build on the work presented in (Desmond & Watson, 2012) to describe a comprehensive methodology for the identification and quantification of stability effects within the ABL using standard site instrumentation. Furthermore, we will show how these effects can be included in simulations conducted using commercially available computational fluid dynamic (CFD) software in order to reduce the uncertainty associated with flow simulations.




Main body of abstract

The site data used for this study has been provided by Mainstream Renewable Power Ltd. These data relate to a site in Northern American in which the terrain is flat and homogenous however the characteristics of the wind resource are complicated by significant stability effects due to a strong diurnal cycle. Data are available in 10 minute averages from a 100m meteorological mast with pairs of cup anemometers located at 30, 50, 80 and 100m. Using these data, values for 10 min averaged turbulence intensity and wind shear were calculated over a 12 month period.

For low wind speeds the thermal stratification of the atmosphere is often the most important factor determining the characteristics of the flow as buoyancy effects dominate over inertial in determining levels of vertical transportation and thus turbulence. Near the earth’s surface this stratification depends heavily on solar irradiance levels. In general it is expected that underlying air will be warmer during periods of high irradiance, thus resulting in unstable stratification, and cooler during periods of low irradiance, thus resulting in stable stratification. In order to asses this effect in the present study, values for turbulence intensity and wind shear were combined with solar irradiance data from the Meteonorm database. This database provides 10 min averaged solar irradiance values for any location on the globe based on annual averages over 10 years.

The wind shear, turbulence intensity and solar irradiance data were then combined in order to identify non-neutral events using the Pasquill-Turner stability classes. Once these non-neutral events were identified it was then possible to produce a probability distribution function describing the likelihood that each stability class would occur for a given wind speed.

In order to appreciate the effect of these non-neutral events on the local wind resource it is necessary to conduct flow modelling in which the effects of buoyancy are included. For this study we have used the commercially available CFD software ANSYS CFX 14.1. CFX contains a coupled solver for mass and momentum which allows the Reynolds Averages Navier Stokes equations to be solved for a user defined node-centred grid using an algebraic multi-grid algorithm for convergence acceleration. This software, which has been in development for 20 years, is a generalised fluid dynamics solver and requires modifications in order to simulate flows within the ABL.

These modifications have been included in the CFX add-on software package WindModeller which provides a user friendly graphical interface and automates many of the processes required to conduct flow simulations within the ABL. In a recent update of the WindModeller software functionality has been introduced to allow the modelling of stability effects. In this paper we will test the ability of this new feature to modelling the stability events identified in the site data.

The Shear Stress Transport (SST) turbulence model has been used for closure of the RANS equations in all simulations as it has been found that this closure model performs well when considering flows in the ABL. SST switches between the k-ε and k-ω equations depending on the proximity to roughness elements and is known to perform well when dealing with adverse pressure gradients and separating flow.

This work has been carried out with funding from the EU FP7-PEOPLE program under WAUDIT Marie-Curie Initial Training Network.

Conclusion

In this paper a comprehensive methodology will be described for the inclusion of stability effects in commercial resource assessments. Firstly, we describe a methodology for identifying non-neutral events from site data collected using standard meteorological instrumentation such as cup anemometers. We then show how these events can be modelled in commercially available CFD software in order to reduce the uncertainty associated with resource assessments.

We do not have a more comprehensive list of conclusions at this stage. Below are some additional details on the classical metrics available for the identification and quantification of stability effects.

---> Richardson Number
Denoted as, Ri, this is a non-dimensional parameter which relates the importance of buoyancy and shear forces in creating turbulence. It requires measurements of both temperature and wind speed at two heights and is given by the equation below [Kaimel & Finnigan, 1994].

Ri= g/θ [(dθ /dz)/(dU /dz)^2 ]

Where, g = 9.81 m/s ,
θ = Average potential temperature (K)
dθ /dz = rate of change of potential temperature with heights (K)


The Richardson number is thus positive for stable atmospheric stratification, negative for unstable and zero for neutral. Near-neutral conditions can be assumed [Mannan & Lee, 2005] for values of:

-0.13≤Ri ≤0.03

-->Obukhov Length
This is another important metric for the assessment of stability in the atmospheric boundary layer and is calculated from the equation [Stull, 1988]:

1/L= - (κ.(g/θ ).((ω'θ') ))/U*

Where, κ = Von Kármán constant = 0.41
((ω'θ') ) = Average vertical heat flux (K m/s)
U* = Friction velocity (m/s)

And,

U*=[((u'ω'))^2+ ((v'ω'))^2 ]^0.25

Where, u',v',ω' = Wind velocity turbulent fluctuations in x , y , z (m/s)
((u'ω')) = Covariance of both parameters (m^2/s^2)


--> Standard Deviation of Direction
This measure was by proposed by the US Nuclear Regulation Commission [Office of Nuclear Regulatory Research, 2007 ] to assess atmospheric stability for the purposes of guiding emergency responses in the event of an incident. A high value of the standard deviation of wind direction turbulent fluctuations, σ_φ, indicates unstable conditions; low values indicate stable conditions and near-neutral conditions are characterised by some intermediate value. Specific values will be highly site and measurement height dependant.





Learning objectives
It is hoped that this paper will provide an understanding of the development and importance of stability effects within the ABL. We also believe that this presentation will equip those in attendance with the tools and knowledge required to immediately begin including these effects in their resource assessments.


References
Desmond C & Watson S, "A study of stability effects in forested terrain", Proceedings of The Science of Making Torque from Wind, Oldenburg, October 2012

Kaimal J C and Finnigan J J 1994 Atmospheric boundary layer flows (Oxford: University Press)

Mannan S and Lee F 2005 Lee's Loss Prevention in the Process Industries (London: Elsevier)

Stull R 1988 An introduction to boundary layer meteorology (Dordrecht: Kluwer Academic Publishers)

Office of Nuclear Regulatory Research 2007 Regulatory guide 1.23: Meteorological monitoring programs for nuclear power plants (Washington: US Nuclear regulatory Authority)