Wind resource assessment is a critical parameter in a diverse range of considerations within the built environment. Engineers and scientists, engaging in building design, energy conservation/application and air-quality/air-pollution control measures, need to be cognisant of how the associated wind resource imposes increased complexities in their design and modelling processes. In this regard, the morphological heterogeneities within these environments, present significant challenges to quantifying the resource and its turbulent characteristics. This paper presents three aspects of turbulence assessment within the built environment. Firstly, an analysis of how turbulence is currently quantified is considered. The industry standard, Turbulent Intensity (TI) is compared with a proposed alternative metric described as Turbulent Fourier Dimension modelling (T Df ). Secondly, the application of the turbulence assessment is considered with respect to how TI affects the productivity of small/micro wind turbines in complex environments though Gaussian distribution analysis. Finally, an extended discussion on current developments such as the concept of a turbulence rose and the ongoing development of statistical modelling is presented.
Abstract-The urban terrain and the associated topographical complexities therein, present significant challenges to the deployment of small wind turbines. In particular, a considerable amount of uncertainty is attributable to the lack of understanding concerning how turbulence within urban environments affects turbine productivity. This paper considers how the industry standard metric, turbulence intensity (TI), in conjunction with the power characteristic of a 2.5kW wind turbine, can be employed to estimate turbine power performance.The research presented here considers the potential productivity of a wind turbine installation at two sites in (urban and suburban) Dublin, Ireland where the prevalent turbulence at both locations is considered. The industry metric of TI and the statistical properties of the high resolution wind observations at both locations are utilised to drive two models. The high resolution nature of the wind speed observations facilitates accurate application of Gaussian and Weibull statistics in this regard. The analysis demonstrates that the proposed methodologies could provide a means for installers to accurately predict power performance for a wind turbine based on (wind speed) standard deviation and TI observations.
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