{"title":"利用大涡模拟捕捉对流大气边界层稳定性的逐日变化","authors":"Jordan Nielson, Kiran Bhaganagar","doi":"10.2174/1874282301812010107","DOIUrl":null,"url":null,"abstract":"Large Eddy Simulation (LES) modelers must begin to answer the question of how to better incorporate large datasets into simulations. This question is important because, at a given location, the diurnal, seasonal, and day-to-day variations of atmospheric stability have significant consequences for the power generated by wind turbines. The following study provides a methodology to obtain discrete values of surface flux, inversion height and geostrophic wind for LES using field data over multiple diurnal cycles (averaged over a month) at 12 Local Time (LT) (during the convective ABL). The methodology will allow the discrete LES to quantify the day-to-day variations over multiple diurnal cycles.The study tests the hypothesis that LES can capture the mean velocity and TKE profiles from the averaged variations in surface heat flux at 12 LT measured in the field (mean, +1 standard deviation, and -1 standard deviation). The discrete LES from the mean, +1 standard deviation, and -1 standard deviation surface heat flux represent the variations in the ABL due to the day-to-day variations in surface heat flux. The method calculates the surface heat flux for the NREL NWTC M5 dataset. The field data were used to generate Probability Density Functions (PDFs) of surface heat flux for the January and July 12 LT. The PDFs are used to select the surface heat fluxes as inputs into the discrete LES.A correlation function between the surface heat flux and the boundary layer height was determined to select the initial inversion height, and the geostrophic departure function was used to determine the geostrophic wind for each surface heat flux. The LES profiles matched the averaged velocity profiles from the field data to 4% and the averaged TKE profiles to 6% and, therefore, validated the methodology. The method allows for further quantification of day-to-day stability variations using LES.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Capturing Day-to-day Diurnal Variations in Stability in the Convective Atmospheric Boundary Layer Using Large Eddy Simulation\",\"authors\":\"Jordan Nielson, Kiran Bhaganagar\",\"doi\":\"10.2174/1874282301812010107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Large Eddy Simulation (LES) modelers must begin to answer the question of how to better incorporate large datasets into simulations. This question is important because, at a given location, the diurnal, seasonal, and day-to-day variations of atmospheric stability have significant consequences for the power generated by wind turbines. The following study provides a methodology to obtain discrete values of surface flux, inversion height and geostrophic wind for LES using field data over multiple diurnal cycles (averaged over a month) at 12 Local Time (LT) (during the convective ABL). The methodology will allow the discrete LES to quantify the day-to-day variations over multiple diurnal cycles.The study tests the hypothesis that LES can capture the mean velocity and TKE profiles from the averaged variations in surface heat flux at 12 LT measured in the field (mean, +1 standard deviation, and -1 standard deviation). The discrete LES from the mean, +1 standard deviation, and -1 standard deviation surface heat flux represent the variations in the ABL due to the day-to-day variations in surface heat flux. The method calculates the surface heat flux for the NREL NWTC M5 dataset. The field data were used to generate Probability Density Functions (PDFs) of surface heat flux for the January and July 12 LT. The PDFs are used to select the surface heat fluxes as inputs into the discrete LES.A correlation function between the surface heat flux and the boundary layer height was determined to select the initial inversion height, and the geostrophic departure function was used to determine the geostrophic wind for each surface heat flux. The LES profiles matched the averaged velocity profiles from the field data to 4% and the averaged TKE profiles to 6% and, therefore, validated the methodology. 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引用次数: 3
摘要
大涡模拟(LES)建模者必须开始回答如何更好地将大数据集纳入模拟的问题。这个问题很重要,因为在一个给定的地点,大气稳定性的日、季节和日变化对风力涡轮机产生的功率有重大影响。下面的研究提供了一种方法,利用多个日周期(平均超过一个月)在12 Local Time (LT)(对流ABL期间)的现场数据获得地表通量、反演高度和地转风的离散值。该方法将允许离散LES在多个日循环中量化日常变化。该研究验证了LES可以从现场测量的12 LT表面热通量的平均变化(平均值,+1标准差和-1标准差)中捕获平均速度和TKE剖面的假设。地表热通量均值、+1标准差和-1标准差的离散LES代表地表热通量逐日变化引起的ABL变化。该方法计算了NREL NWTC M5数据集的地表热通量。利用野外数据生成1月和7月12日ltf的地表热通量概率密度函数(PDFs),并利用PDFs选择地表热通量作为离散LES的输入。通过确定地表热通量与边界层高度的相关函数选择初始反演高度,利用地转偏离函数确定各地表热通量的地转风。LES曲线与现场数据的平均速度曲线匹配度为4%,平均TKE曲线匹配度为6%,因此验证了该方法。该方法允许使用LES进一步量化日常稳定性变化。
Capturing Day-to-day Diurnal Variations in Stability in the Convective Atmospheric Boundary Layer Using Large Eddy Simulation
Large Eddy Simulation (LES) modelers must begin to answer the question of how to better incorporate large datasets into simulations. This question is important because, at a given location, the diurnal, seasonal, and day-to-day variations of atmospheric stability have significant consequences for the power generated by wind turbines. The following study provides a methodology to obtain discrete values of surface flux, inversion height and geostrophic wind for LES using field data over multiple diurnal cycles (averaged over a month) at 12 Local Time (LT) (during the convective ABL). The methodology will allow the discrete LES to quantify the day-to-day variations over multiple diurnal cycles.The study tests the hypothesis that LES can capture the mean velocity and TKE profiles from the averaged variations in surface heat flux at 12 LT measured in the field (mean, +1 standard deviation, and -1 standard deviation). The discrete LES from the mean, +1 standard deviation, and -1 standard deviation surface heat flux represent the variations in the ABL due to the day-to-day variations in surface heat flux. The method calculates the surface heat flux for the NREL NWTC M5 dataset. The field data were used to generate Probability Density Functions (PDFs) of surface heat flux for the January and July 12 LT. The PDFs are used to select the surface heat fluxes as inputs into the discrete LES.A correlation function between the surface heat flux and the boundary layer height was determined to select the initial inversion height, and the geostrophic departure function was used to determine the geostrophic wind for each surface heat flux. The LES profiles matched the averaged velocity profiles from the field data to 4% and the averaged TKE profiles to 6% and, therefore, validated the methodology. The method allows for further quantification of day-to-day stability variations using LES.