Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198243111> ?p ?o ?g. }
- W3198243111 abstract "Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction." @default.
- W3198243111 created "2021-09-13" @default.
- W3198243111 creator A5037070388 @default.
- W3198243111 creator A5048243433 @default.
- W3198243111 creator A5055225422 @default.
- W3198243111 creator A5079765625 @default.
- W3198243111 date "2022-03-01" @default.
- W3198243111 modified "2023-09-24" @default.
- W3198243111 title "Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements" @default.
- W3198243111 cites W1633869374 @default.
- W3198243111 cites W1897867007 @default.
- W3198243111 cites W1911805562 @default.
- W3198243111 cites W1931511542 @default.
- W3198243111 cites W1970536223 @default.
- W3198243111 cites W1970871395 @default.
- W3198243111 cites W1972960566 @default.
- W3198243111 cites W1980495275 @default.
- W3198243111 cites W1987971958 @default.
- W3198243111 cites W1992419399 @default.
- W3198243111 cites W1993570113 @default.
- W3198243111 cites W1994019711 @default.
- W3198243111 cites W2006653321 @default.
- W3198243111 cites W2006709212 @default.
- W3198243111 cites W2007686844 @default.
- W3198243111 cites W2010531978 @default.
- W3198243111 cites W2019859456 @default.
- W3198243111 cites W2052170279 @default.
- W3198243111 cites W2064787083 @default.
- W3198243111 cites W2066579159 @default.
- W3198243111 cites W2067084426 @default.
- W3198243111 cites W2070601851 @default.
- W3198243111 cites W2071562292 @default.
- W3198243111 cites W2083432583 @default.
- W3198243111 cites W2087957467 @default.
- W3198243111 cites W2095176311 @default.
- W3198243111 cites W2106564841 @default.
- W3198243111 cites W2107757677 @default.
- W3198243111 cites W2112823474 @default.
- W3198243111 cites W2121502079 @default.
- W3198243111 cites W2122388304 @default.
- W3198243111 cites W2130116100 @default.
- W3198243111 cites W2130523818 @default.
- W3198243111 cites W2134118153 @default.
- W3198243111 cites W2136146048 @default.
- W3198243111 cites W2140405352 @default.
- W3198243111 cites W2151984087 @default.
- W3198243111 cites W2155549533 @default.
- W3198243111 cites W2155772953 @default.
- W3198243111 cites W2164594722 @default.
- W3198243111 cites W2292710182 @default.
- W3198243111 cites W2293352272 @default.
- W3198243111 cites W2405704642 @default.
- W3198243111 cites W2469409513 @default.
- W3198243111 cites W2508643429 @default.
- W3198243111 cites W2514050064 @default.
- W3198243111 cites W2530065124 @default.
- W3198243111 cites W2537835256 @default.
- W3198243111 cites W2556071208 @default.
- W3198243111 cites W2593300601 @default.
- W3198243111 cites W2594040502 @default.
- W3198243111 cites W2620158713 @default.
- W3198243111 cites W2622215529 @default.
- W3198243111 cites W2725695395 @default.
- W3198243111 cites W2803690711 @default.
- W3198243111 cites W2809224582 @default.
- W3198243111 cites W2912131502 @default.
- W3198243111 cites W2974473399 @default.
- W3198243111 cites W2979841977 @default.
- W3198243111 cites W2995697713 @default.
- W3198243111 cites W3000555404 @default.
- W3198243111 cites W3009709629 @default.
- W3198243111 cites W3043358016 @default.
- W3198243111 cites W3082012038 @default.
- W3198243111 cites W3092323236 @default.
- W3198243111 cites W3092494698 @default.
- W3198243111 cites W3100157365 @default.
- W3198243111 cites W3100490547 @default.
- W3198243111 cites W3107240888 @default.
- W3198243111 cites W3117990810 @default.
- W3198243111 cites W3118778491 @default.
- W3198243111 cites W3120372161 @default.
- W3198243111 cites W3136844132 @default.
- W3198243111 cites W3179229703 @default.
- W3198243111 cites W3205517708 @default.
- W3198243111 doi "https://doi.org/10.1063/5.0070094" @default.
- W3198243111 hasPublicationYear "2022" @default.
- W3198243111 type Work @default.
- W3198243111 sameAs 3198243111 @default.
- W3198243111 citedByCount "0" @default.
- W3198243111 crossrefType "journal-article" @default.
- W3198243111 hasAuthorship W3198243111A5037070388 @default.
- W3198243111 hasAuthorship W3198243111A5048243433 @default.
- W3198243111 hasAuthorship W3198243111A5055225422 @default.
- W3198243111 hasAuthorship W3198243111A5079765625 @default.
- W3198243111 hasBestOaLocation W31982431111 @default.
- W3198243111 hasConcept C115051666 @default.
- W3198243111 hasConcept C119599485 @default.
- W3198243111 hasConcept C121332964 @default.
- W3198243111 hasConcept C127313418 @default.
- W3198243111 hasConcept C127413603 @default.