Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
1.29_Clive: Lidar and the integration of wind data into digital workflows
Lidar is a versatile wind measurement instrument that can both emulate the capabilities of conventional met mast mounted anemometry and go beyond those capabilities to acquire data that would not otherwise be available. While these additional data may be useless for the purposes of strict implementation of wind resource assessment procedures based on met mast capabilities, they give us a pathway to full digitisation of wind data during the earliest stages wind power projects, such as resource assessment, and their integration into digital workflows that achieve a lifetime of successful project performance. The limitations of met mast methodologies are now apparent. We are confronting issues such as global blockage effects. The real implications of this are that we can no longer apply simple terrain approximations or distinguish between pre- and post- construction phases of project delivery: the circumstances that determine wind conditions on which project performance is based only arise once the wind farm is constructed. Therefore, we need new measurement and analysis methodologies that can be applied consistently both pre- and post-construction to accommodate this. Met mast measurements are used to initiate computational fluid dynamics (CFD) models coupled to aeroelastic models (AEM) coupled to wind turbine models using finite element methods (FEM) to predict, for example, fatigue loads. Similarly, met mast data are coupled to long term reference data using measure-correlate-predict (MCP) methods to allow long term correction of wind resource estimates. Where lidars replace met masts they must emulate their functionality. However, lidars operate in a fundamentally different manner. Their primary measurements (so-called intermediate variables such as radial wind speed, time-of-flight, and beam azimuth and elevation angles) must be processed using a wind field reconstruction (WFR) algorithm based on a model of the relationship between wind conditions and intermediate variables, to produce final variables that fulfil the same data requirements as met masts. There are two problems. The WFR model is typically inadequate under all but the simplest circumstances. This can result in measurement ambiguity and uncertainty. Secondly, the met masts themselves, as emulated by the lidar, do not uniquely determine CFD predictions, introducing uncertainty in any analysis that relies on them. The solution lies in going beyond the capabilities of met masts. There is no need to replicate the limitations of met masts with WFR. Minimum uncertainty is achieved by directly validating CFD with intermediate variables, eliminating two major sources of uncertainty: the WFR algorithm and CFD validation. This is compatible with the procedures that cope with our inability to neatly distinguish between pre- and post-construction scenarios. The same instruments and methods can be applied in a consistent manner during any phase of project delivery. One consequence is that the data requirements arising at every phase of project delivery becomes aligned. Wind resource assessment should be a telescope which one uses to try to see the completion of a successful project while standing at its commencement. Lidar, its digitisation of wind data, and their integration into a coherent digital workflow, makes this possible.
1.29_Clive: Lidar and the integration of wind data into digital workflows
Lidar is a versatile wind measurement instrument that can both emulate the capabilities of conventional met mast mounted anemometry and go beyond those capabilities to acquire data that would not otherwise be available. While these additional data may be useless for the purposes of strict implementation of wind resource assessment procedures based on met mast capabilities, they give us a pathway to full digitisation of wind data during the earliest stages wind power projects, such as resource assessment, and their integration into digital workflows that achieve a lifetime of successful project performance. The limitations of met mast methodologies are now apparent. We are confronting issues such as global blockage effects. The real implications of this are that we can no longer apply simple terrain approximations or distinguish between pre- and post- construction phases of project delivery: the circumstances that determine wind conditions on which project performance is based only arise once the wind farm is constructed. Therefore, we need new measurement and analysis methodologies that can be applied consistently both pre- and post-construction to accommodate this. Met mast measurements are used to initiate computational fluid dynamics (CFD) models coupled to aeroelastic models (AEM) coupled to wind turbine models using finite element methods (FEM) to predict, for example, fatigue loads. Similarly, met mast data are coupled to long term reference data using measure-correlate-predict (MCP) methods to allow long term correction of wind resource estimates. Where lidars replace met masts they must emulate their functionality. However, lidars operate in a fundamentally different manner. Their primary measurements (so-called intermediate variables such as radial wind speed, time-of-flight, and beam azimuth and elevation angles) must be processed using a wind field reconstruction (WFR) algorithm based on a model of the relationship between wind conditions and intermediate variables, to produce final variables that fulfil the same data requirements as met masts. There are two problems. The WFR model is typically inadequate under all but the simplest circumstances. This can result in measurement ambiguity and uncertainty. Secondly, the met masts themselves, as emulated by the lidar, do not uniquely determine CFD predictions, introducing uncertainty in any analysis that relies on them. The solution lies in going beyond the capabilities of met masts. There is no need to replicate the limitations of met masts with WFR. Minimum uncertainty is achieved by directly validating CFD with intermediate variables, eliminating two major sources of uncertainty: the WFR algorithm and CFD validation. This is compatible with the procedures that cope with our inability to neatly distinguish between pre- and post-construction scenarios. The same instruments and methods can be applied in a consistent manner during any phase of project delivery. One consequence is that the data requirements arising at every phase of project delivery becomes aligned. Wind resource assessment should be a telescope which one uses to try to see the completion of a successful project while standing at its commencement. Lidar, its digitisation of wind data, and their integration into a coherent digital workflow, makes this possible.
1.29_Clive: Lidar and the integration of wind data into digital workflows
Clive, Peter J M (Autor:in)
27.08.2019
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC:
690
1.29_Clive: Lidar and the integration of wind data into digital workflows
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