Capturing Day-to-day Diurnal Variations in Stability in the Convective Atmospheric Boundary Layer Using Large Eddy Simulation

Abstract

Introduction: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.Methods: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.Results / Conclusion: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.

Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines

“…The mean, −1 SD, and +1 SD cases represent the range of variations from day to day (in atmospheric stability) that were measured in the field. The large differences in E P n e t w a k e shed light in the uncertainty of E P n e t w a k e based on variations in atmospheric stability that occurs naturally (Nielson and Bhaganagar, 2018). An incorrect assumption about the surface heat flux, from a wide range of measured values, could lead to significant differences in the estimation of energy production for a wind farm.…”

“…A correlation between inversion height and surface heat flux was developed to set the initial inversion layer height. The simulations were performed with mean surface flux, +1 SD, and −1 SD from the measured surface flux values of the July 12 p.m. data (Nielson and Bhaganagar, 2018). Table 1 shows the precursor simulations performed for the study.…”

A Dynamic Large-Scale Driving-Force to Control the Targeted Wind Speed in Large Eddy Simulations above Ocean Waves

“…Over land, however, a few applications considered transient large-scale forcing terms in LES (c.f. [61][62][63][64][65][66][67]). Based on LiDAR measurements in the ABL, the overall accuracy was improved through the prescription of a linear-time-varying, and spatially homogeneous, pressure gradient [61].…”

“…Based on LiDAR measurements in the ABL, the overall accuracy was improved through the prescription of a linear-time-varying, and spatially homogeneous, pressure gradient [61]. The measurement of time-varying geostrophic winds drove the ABL during one or multiple diurnal cycles [62,63]. Employing time-varying and heightdependent forcing terms is in fact a common practice of data assimilation in meso-to micro-scale coupling procedures [64,65].…”