Boundary-Layer Meteorology

, Volume 171, Issue 2, pp 213–235 | Cite as

On the Feasibility of Using Large-Eddy Simulations for Real-Time Turbulent-Flow Forecasting in the Atmospheric Boundary Layer

  • Pieter Bauweraerts
  • Johan MeyersEmail author
Research Article


We investigate the feasibility of using large-eddy simulation (LES) for real-time forecasting of instantaneous turbulent velocity fluctuations in the atmospheric boundary layer. Although LES is generally considered computationally too expensive for real-time use, wall-clock time can be significantly reduced by using very coarse meshes. Here, we focus on forecasting errors arising on such coarse grids, and investigate the trade-off between computational speed and accuracy. We omit any aspects related to state estimation or model bias, but rather look at the size and evolution of restriction errors, subgrid-scale errors, and chaotic divergence, to obtain a first idea of the feasibility of LES as a forecasting tool. To this end, we set-up an idealized test scenario in which the forecasting error in a neutral atmospheric boundary layer is investigated based on a fine reference simulation, and a series of coarser LES grids. We find that errors only slowly increase with grid coarsening, related to restriction errors that increase. Unexpectedly, modelling errors slightly decrease with grid coarsening, as both chaotic divergence and subgrid-scale error sources decrease. A practical example, inspired by wind-energy applications, reveals that there is a range of forecasting horizons for which the variance of the forecasting error is significantly reduced compared to the turbulent background variance, while at the same time, associated LES wall times are up to 300 times smaller than simulated time.


Large-eddy simulation Turbulent boundary layer Wind energy 



The authors acknowledge support from the Agency for Innovation and Entrepreneurship through research Grant No. 141689. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government department EWI.

Supplementary material


  1. Abe H, Kawamura H, Choi H (2004) Very large-scale structures and their effects on the wall shear–stress fluctuations in a turbulent channel flow up to \(\text{ Re }_\tau = 640\). J Fluids Eng 126(5):835–843CrossRefGoogle Scholar
  2. Ainslie JF (1988) Calculating the flowfield in the wake of wind turbines. J Wind Eng Ind Aerodyn 27(1–3):213–224CrossRefGoogle Scholar
  3. Aurell E, Boffetta G, Crisanti A, Paladin G, Vulpiani A (1997) Predictability in the large: an extension of the concept of Lyapunov exponent. J Phys A Math Gen 30(1):1–26CrossRefGoogle Scholar
  4. Basu S, Foufoula-Georgiou E, Porté-Agel F (2002) Predictability of atmospheric boundary-layer flows as a function of scale. Geophys Res Lett 29(21):2038CrossRefGoogle Scholar
  5. Beare RJ, Macvean MK, Holtslag AA, Cuxart J, Esau I, Golaz JC, Jimenez MA, Khairoutdinov M, Kosovic B, Lewellen D et al (2006) An intercomparison of large-eddy simulations of the stable boundary layer. Boundary-Layer Meteorol 118(2):247–272CrossRefGoogle Scholar
  6. Belcher S, Coceal O, Goulart E, Rudd A, Robins A (2015) Processes controlling atmospheric dispersion through city centres. J Fluid Mech 763:51–81CrossRefGoogle Scholar
  7. Bou-Zeid E, Meneveau C, Parlange M (2005) A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows. Phys Fluids 17(2):025105CrossRefGoogle Scholar
  8. Brijs T (2017) Electricity storage participation and modeling in short-term electricity markets. PhD thesis, KU LeuvenGoogle Scholar
  9. Calaf M, Meneveau C, Meyers J (2010) Large eddy simulation study of fully developed wind-turbine array boundary layers. Phys Fluids 22(1):015110CrossRefGoogle Scholar
  10. Canuto C, Quarteroni A, Hussaini MY, Zang TA (1988) Spectral methods in fluid dynamics. Springer, BerlinCrossRefGoogle Scholar
  11. de Roode SR, Jonker HJ, van de Wiel BJ, Vertregt V, Perrin V (2017) A diagnosis of excessive mixing in smagorinsky subfilter-scale turbulent kinetic energy models. J Atmos Sci 74(5):1495–1511CrossRefGoogle Scholar
  12. Fang J, Porté-Agel F (2015) Large-eddy simulation of very-large-scale motions in the neutrally stratified atmospheric boundary layer. Boundary-Layer Meteorol 155(3):397–416CrossRefGoogle Scholar
  13. Frigo M, Johnson SG (2005) The design and implementation of FFTW3. Proc IEEE 93(2):216–231CrossRefGoogle Scholar
  14. Fuhrer O, Chadha T, Hoefler T, Kwasniewski G, Lapillonne X, Leutwyler D, Lüthi D, Osuna C, Schär C, Schulthess TC et al (2018) Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0. Geosci Model Dev 11(4):1665–1681CrossRefGoogle Scholar
  15. Gebraad P, Teeuwisse F, Wingerden J, Fleming PA, Ruben S, Marden J, Pao L (2016) Wind plant power optimization through yaw control using a parametric model for wake effectstest—a CFD simulation study. Wind Energy 19(1):95–114CrossRefGoogle Scholar
  16. Germano M (1992) Turbulence: the filtering approach. J Fluid Mech 238:325–336CrossRefGoogle Scholar
  17. Goit JP, Meyers J (2015) Optimal control of energy extraction in wind-farm boundary layers. J Fluid Mech 768:5–50CrossRefGoogle Scholar
  18. Goit JP, Munters W, Meyers J (2016) Optimal coordinated control of power extraction in les of a wind farm with entrance effects. Energies 9(1):29CrossRefGoogle Scholar
  19. Hirth BD, Schroeder JL, Irons Z, Walter K (2016) Dual-Doppler measurements of a wind ramp event at an Oklahoma wind plant. Wind Energy 19(5):953–962CrossRefGoogle Scholar
  20. Holmes NS, Morawska L (2006) A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmos Environ 40(30):5902–5928CrossRefGoogle Scholar
  21. Hutchins N, Marusic I (2007) Evidence of very long meandering features in the logarithmic region of turbulent boundary layers. J Fluid Mech 579:1–28CrossRefGoogle Scholar
  22. Jiménez J (1998) The largest scales of turbulent wall flows. CTR Annu Res Briefs 137:54Google Scholar
  23. Jung J, Broadwater RP (2014) Current status and future advances for wind speed and power forecasting. Renew Sust Energy Rev 31:762–777CrossRefGoogle Scholar
  24. Kalman RE et al (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45CrossRefGoogle Scholar
  25. Katata G, Chino M, Kobayashi T, Terada H, Ota M, Nagai H, Kajino M, Draxler R, Hort M, Malo A et al (2015) Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model. Atmos Chem Phys 15(2):1029–1070CrossRefGoogle Scholar
  26. Katic I, Højstrup J, Jensen NO (1986) A simple model for cluster efficiency. In: European wind energy association conference and exhibition, pp 407–410Google Scholar
  27. Kim K, Adrian R (1999) Very large-scale motion in the outer layer. Phys Fluids 11(2):417–422CrossRefGoogle Scholar
  28. Knudsen T, Bak T, Svenstrup M (2015) Survey of wind farm control—power and fatigue optimization. Wind Energy 18(8):1333–1351CrossRefGoogle Scholar
  29. Lapillonne X, Osterried K, Fuhrer O (2017) Using OpenACC to port large legacy climate and weather modeling code to GPUs. In: Farber R (ed) Parallel programming with OpenACC. Elsevier, Amsterdam, pp 267–290Google Scholar
  30. Larsen GC, Madsen HA, Thomsen K, Larsen TJ (2008) Wake meandering: a pragmatic approach. Wind Energy 11(4):377–395CrossRefGoogle Scholar
  31. Le Dimet FX, Talagrand O (1986) Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus A Dyn Meteorol Oceanogr 38(2):97–110CrossRefGoogle Scholar
  32. Leelőssy Á, Molnár F, Izsák F, Havasi Á, Lagzi I, Mészáros R (2014) Dispersion modeling of air pollutants in the atmosphere: a review. Open Geosci 6(3):257–278Google Scholar
  33. Leonard A (1975) Energy cascade in large-eddy simulations of turbulent fluid flows. In: Frenkiel FN, Munn RE (eds) Advances in geophysics, vol 18. Elsevier, Amsterdam, pp 237–248Google Scholar
  34. Li N, Laizet S (2010) 2DECOMP & FFT—a highly scalable 2D decomposition library and FFT interface. In: Cray user group 2010 conference, pp 1–13Google Scholar
  35. Lorenc A (1981) A global three-dimensional multivariate statistical interpolation scheme. Mon Weather Rev 109(4):701–721CrossRefGoogle Scholar
  36. Lorenz EN (1969) The predictability of a flow which possesses many scales of motion. Tellus 21(3):289–307CrossRefGoogle Scholar
  37. Mason PJ, Thomson D (1992) Stochastic backscatter in large-eddy simulations of boundary layers. J Fluid Mech 242:51–78CrossRefGoogle Scholar
  38. Meyers J (2011) Error-landscape assessment of large-eddy simulations: a review of the methodology. J Sci Comput 49(1):65–77CrossRefGoogle Scholar
  39. Meyers J, Meneveau C (2013) Flow visualization using momentum and energy transport tubes and applications to turbulent flow in wind farms. J Fluid Mech 715:335–358CrossRefGoogle Scholar
  40. Mikkelsen T (2014) Lidar-based research and innovation at DTU wind energy—a review. J Phys Conf Ser 524:012007CrossRefGoogle Scholar
  41. Moeng CH (1984) A large-eddy-simulation model for the study of planetary boundary-layer turbulence. J Atmos Sci 41(13):2052–2062CrossRefGoogle Scholar
  42. Mukherjee S, Schalkwijk J, Jonker HJ (2016) Predictability of dry convective boundary layers: an les study. J Atmos Sci 73(7):2715–2727CrossRefGoogle Scholar
  43. Munters W, Meyers J (2017a) An optimal control framework for dynamic induction control of wind farms and their interaction with the atmospheric boundary layer. Philos Trans R Soc A 375(2091):20160100CrossRefGoogle Scholar
  44. Munters W, Meyers J (2017b) Optimal coordinated control of wind-farm boundary layers in large-eddy simulations: intercomparison between dynamic yaw control and dynamic induction control. PhD thesis, Dept Mech Eng, KU LeuvenGoogle Scholar
  45. Munters W, Meyers J (2018) Dynamic strategies for yaw and induction control of wind farms based on large-eddy simulation and optimization. Energies 11:177CrossRefGoogle Scholar
  46. Munters W, Meneveau C, Meyers J (2016) Shifted periodic boundary conditions for simulations of wall-bounded turbulent flows. Phys Fluids 28(2):025112CrossRefGoogle Scholar
  47. Niayifar A, Porté-Agel F (2015) A new analytical model for wind farm power prediction. J Phys Conf Ser 625:012039CrossRefGoogle Scholar
  48. Pope SB (2000) Turbulent flows. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  49. Rebours YG, Kirschen DS, Trotignon M, Rossignol S (2007) A survey of frequency and voltage control ancillary services—part I: technical features. IEEE Trans Power Syst 22(1):350–357CrossRefGoogle Scholar
  50. Sathe A, Mann J (2013) A review of turbulence measurements using ground-based wind lidars. Atmos Meas Tech 6(11):3147CrossRefGoogle Scholar
  51. Schlipf D, Trabucchi D, Bischoff O, Hofsäß M, Mann J, Mikkelsen T, Rettenmeier A, Trujillo JJ, Kühn M (2010) Testing of frozen turbulence hypothesis for wind turbine applications with a scanning lidar system. ISARSGoogle Scholar
  52. Schlipf D, Schlipf DJ, Kühn M (2013) Nonlinear model predictive control of wind turbines using lidar. Wind Energy 16(7):1107–1129CrossRefGoogle Scholar
  53. Shah S, Bou-Zeid E (2014) Very-large-scale motions in the atmospheric boundary layer educed by snapshot proper orthogonal decomposition. Boundary-Layer Meteorol 153(3):355–387CrossRefGoogle Scholar
  54. Shapiro CR, Bauweraerts P, Meyers J, Meneveau C, Gayme DF (2017) Model-based receding horizon control of wind farms for secondary frequency regulation. Wind Energy 20(7):1261–1275CrossRefGoogle Scholar
  55. Smagorinsky J (1963) General circulation experiments with the primitive equations: I. The basic experiment. Mon Weather Rev 91(3):99–164CrossRefGoogle Scholar
  56. Sullivan PP, Patton EG (2011) The effect of mesh resolution on convective boundary layer statistics and structures generated by large-eddy simulation. J Atmos Sci 68(10):2395–2415CrossRefGoogle Scholar
  57. van Stratum BJ, Stevens B (2015) The influence of misrepresenting the nocturnal boundary layer on idealized daytime convection in large-eddy simulation. J Adv Mod Earth Syst 7(2):423–436CrossRefGoogle Scholar
  58. Váňa F, Düben P, Lang S, Palmer T, Leutbecher M, Salmond D, Carver G (2017) Single precision in weather forecasting models: an evaluation with the IFS. Mon Weather Rev 145(2):495–502CrossRefGoogle Scholar
  59. VerHulst C, Meneveau C (2014) Large eddy simulation study of the kinetic energy entrainment by energetic turbulent flow structures in large wind farms. Phys Fluids 26(2):025113CrossRefGoogle Scholar
  60. Verstappen R, Veldman A (2003) Symmetry-preserving discretization of turbulent flow. J Comput Phys 187(1):343–368CrossRefGoogle Scholar
  61. Vervecken L, Camps J, Meyers J (2015) Stable reduced-order models for pollutant dispersion in the built environment. Build Environ 92:360–367CrossRefGoogle Scholar
  62. Wang Q, Zhang C, Ding Y, Xydis G, Wang J, Østergaard J (2015) Review of real-time electricity markets for integrating distributed energy resources and demand response. Appl Energy 138:695–706CrossRefGoogle Scholar
  63. Wiernga J (1993) Representative roughness parameters for homogeneous terrain. Boundary-Layer Meteorol 63(4):323–363CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Mechanical EngineeringKU LeuvenLeuvenBelgium

Personalised recommendations