Predictive large eddy simulations for urban flows: Challenges and opportunities. (July 2018)

• Record Type: Journal Article
• Authors: García-Sánchez, C.; van Beeck, J.; Gorlé, C.
• Is Part Of: Building and environment. Volume 139(2018)
• Page Start: 146
• Page End: 156
• Publication Date: 2018-07
• Subjects: Large eddy simulations -- Uncertainty quantification -- Atmospheric boundary layer -- Urban wind flows; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2018.05.007
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Computational fluid dynamics predictions of urban flow are subject to several sources of uncertainty, such as the definition of the inflow boundary conditions or the turbulence model. Compared to Reynolds-averaged Navier-Stokes (RANS) simulations, large eddy simulations (LES) can reduce turbulence model uncertainty by resolving the turbulence down to scales in the inertial subrange, but the presence of other uncertainties will not be reduced. The objective of this study is to present an initial investigation of the relative importance of these different types of uncertainties by comparing urban flow predictions obtained using RANS and LES to field measurements. The simulations are designed to reproduce measurements performed during the Joint Urban 2003 field experiments. The time-averaged velocity measured at an upstream wind sensor is used to define the inflow boundary condition, and the results are compared to time-averaged measurements at 34 locations in the downtown area. For the turbulence kinetic energy, the LES is found to be more accurate than the RANS in 80% of the available high-frequency measurement locations. For the mean velocity field, this number reduces to 50% of all stations. Comparison of the LES results with a previous inflow uncertainty quantification study for RANS shows that locations where the LES is less accurate than the RANS correspond to locations where the RANS solution is highly sensitive to the inflow boundary conditions. This suggests that inflow uncertainties can be a dominant factor, and that their effect on LES results should be quantified to guarantee predictive capabilities. Highlights: RANS and LES results for the flow in Oklahoma City are compared to field measurements. LES mean velocity predictions are more accurate in 50% of the measurement stations. LES turbulence kinetic energy predictions are more accurate in 80% of the stations. LES mean velocity is less accurate in stations sensitive to inflow variability in RANS. Accounting for the different uncertainties in urban flow simulations is essential.