Analysing momentum balance over a large wind farm using a numerical weather prediction model

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dc.contributor.authorLun, Ma
dc.contributor.authorDunstan, Thomas D.
dc.contributor.authorNishino, Takafumi
dc.date.accessioned2021-05-21T13:53:57Z
dc.date.available2021-05-21T13:53:57Z
dc.date.issued2020-09-21
dc.description.abstractThis study attempts to better understand the mechanisms of wind farm blockage effect by analysing momentum balance in realistic atmospheric flow over an idealised large offshore wind farm. The analysis is performed following the two-scale momentum theory, which predicts the importance of three different terms in the farm-scale momentum balance, namely the streamwise pressure gradient, Coriolis force and acceleration/deceleration terms. A numerical weather prediction (NWP) model is used as a realistic farm-scale flow model in this study to investigate how these three terms tend to change in time. Initial results suggest that the streamwise pressure gradient may be enhanced substantially by the resistance caused by the wind farm, whereas its influence on the other two terms appears to be relatively minor. These results suggest the importance of modelling the farm-induced pressure gradient accurately for various weather conditions in future studies of wind farm blockageen_UK
dc.identifier.citationMa L, Dunstan TD, Nishino T. (2020) Analysing momentum balance over a large wind farm using a numerical weather prediction model. Journal of Physics: Conference Series, Volume 1618, Issue 6, September 2020, Article number 062010en_UK
dc.identifier.issn1742-6588
dc.identifier.urihttps://doi.org/10.1088/1742-6596/1618/6/062010
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16711
dc.language.isoenen_UK
dc.publisherIOP Publishingen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectnumerical weather prediction (NWP)en_UK
dc.subjectstreamwise pressure gradienten_UK
dc.titleAnalysing momentum balance over a large wind farm using a numerical weather prediction modelen_UK
dc.typeArticleen_UK

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