scholarly journals Further Improvement of Surface Flux Estimation in the Unstable Surface Layer Based on Large‐Eddy Simulation Data

2019 ◽  
Vol 124 (17-18) ◽  
pp. 9839-9854
Author(s):  
S. Liu ◽  
X. Zeng ◽  
Y. Dai ◽  
Y. Shao
Keyword(s):  
Surface Layer ◽  
Large Eddy Simulation ◽  
Surface Flux ◽  
Simulation Data ◽  
Eddy Simulation ◽  
Unstable Surface ◽  
Large Eddy ◽  
Flux Estimation

scholarly journals Vertically nested LES for high-resolution simulation of the surface layer in PALM (version 5.0)

2019 ◽  
Vol 12 (6) ◽  
pp. 2523-2538 ◽  
Author(s):  
Sadiq Huq ◽  
Frederik De Roo ◽  
Siegfried Raasch ◽  
Matthias Mauder
Keyword(s):  
Boundary Layer ◽  
Surface Layer ◽  
High Resolution ◽  
Large Eddy Simulation ◽  
Grid Resolution ◽  
Computational Power ◽  
Fine Grid ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Nesting Algorithm

Abstract. Large-eddy simulation (LES) has become a well-established tool in the atmospheric boundary layer research community to study turbulence. It allows three-dimensional realizations of the turbulent fields, which large-scale models and most experimental studies cannot yield. To resolve the largest eddies in the mixed layer, a moderate grid resolution in the range of 10 to 100 m is often sufficient, and these simulations can be run on a computing cluster with a few hundred processors or even on a workstation for simple configurations. The desired resolution is usually limited by the computational resources. However, to compare with tower measurements of turbulence and exchange fluxes in the surface layer, a much higher resolution is required. In spite of the growth in computational power, a high-resolution LES of the surface layer is often not feasible: to fully resolve the energy-containing eddies near the surface, a grid spacing of O(1 m) is required. One way to tackle this problem is to employ a vertical grid nesting technique, in which the surface is simulated at the necessary fine grid resolution, and it is coupled with a standard, coarse, LES that resolves the turbulence in the whole boundary layer. We modified the LES model PALM (Parallelized Large-eddy simulation Model) and implemented a two-way nesting technique, with coupling in both directions between the coarse and the fine grid. The coupling algorithm has to ensure correct boundary conditions for the fine grid. Our nesting algorithm is realized by modifying the standard third-order Runge–Kutta time stepping to allow communication of data between the two grids. The two grids are concurrently advanced in time while ensuring that the sum of resolved and sub-grid-scale kinetic energy is conserved. We design a validation test and show that the temporally averaged profiles from the fine grid agree well compared to the reference simulation with high resolution in the entire domain. The overall performance and scalability of the nesting algorithm is found to be satisfactory. Our nesting results in more than 80 % savings in computational power for 5 times higher resolution in each direction in the surface layer.

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

2019 ◽  
Vol 43 (6) ◽  
pp. 625-638 ◽  
Author(s):  
Jordan Nielson ◽  
Kiran Bhaganagar
Keyword(s):  
Large Eddy Simulation ◽  
Wind Turbines ◽  
Surface Heat Flux ◽  
Energy Production ◽  
Surface Flux ◽  
Surface Heat ◽  
Net Energy ◽  
Eddy Simulation ◽  
Large Eddy

A novel and a robust high-fidelity numerical methodology has been developed to realistically estimate the net energy production of full-scale horizontal axis wind turbines in a convective atmospheric boundary layer, for both isolated and multiple wind turbine arrays by accounting for the wake effects between them. Large eddy simulation has been used to understand the role of atmospheric stability in net energy production (annual energy production) of full-scale horizontal axis wind turbines placed in the convective atmospheric boundary layer. The simulations are performed during the convective conditions corresponding to the National Renewable Energy Laboratory field campaign of July 2015. A mathematical framework was developed to incorporate the field-based measurements as boundary conditions for the large eddy simulation by averaging the surface flux over multiple diurnal cycles. The objective of the study is to quantify the role of surface flux in the calculation of energy production for an isolated, two and three wind turbine configuration. The study compares the mean value, +1 standard deviation, and −1 standard deviation from the measured surface flux to demonstrate the role of surface heat flux. The uniqueness of the study is that power deficits from large eddy simulation were used to determine wake losses and obtain a net energy production that accounts for the wake losses. The frequency of stability events, from field measurements, is input into the calculation of an ensemble energy production prediction with wake losses for different wind turbine arrays. The increased surface heat flux increases the atmospheric turbulence into the wind turbines. Higher turbulence results in faster wake recovery by a factor of two. The faster wake recovery rates result in lowering the power deficits from 46% to 28% for the two-turbine array. The difference in net energy production between the +1 and −1 standard deviation (with respect to surface heat flux) simulations was 10% for the two-turbine array and 8% for the three-turbine array. An ensemble net energy production by accounting for the wake losses indicated the overestimation of annual energy production from current practices could be corrected by accounting for variation of surface flux from the mean value.

Structure of subfilter-scale fluxes in the atmospheric surface layer with application to large-eddy simulation modelling

2003 ◽  
Vol 482 ◽  
pp. 101-139 ◽  
Author(s):  
PETER P. SULLIVAN ◽  
THOMAS W. HORST ◽  
DONALD H. LENSCHOW ◽  
CHIN-HOH MOENG ◽  
JEFFREY C. WEIL
Keyword(s):  
Surface Layer ◽  
Large Eddy Simulation ◽  
Atmospheric Surface Layer ◽  
Simulation Modelling ◽  
Eddy Simulation ◽  
Large Eddy
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