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An incoming canonical spatially developing turbulent boundary layer (SDTBL) over a 2-D curved hill is numerically investigated via the Reynolds-averaged Navier–Stokes (RANS) equations plus two eddy-viscosity models: the K−ω SST (henceforth SST) and the Spalart–Allmaras (henceforth SA) turbulence models. A spatially evolving thermal boundary layer has also been included, assuming temperature as a passive scalar (Pr = 0.71) and a turbulent Prandtl number, Prt, of 0.90 for wall-normal turbulent heat flux modeling. The complex flow with a combined strong adverse/favorable streamline curvature-driven pressure gradient caused by concave/convex surface curvatures has been replicated from wind-tunnel experiments from the literature, and the measured velocity and pressure fields have been used for validation purposes (the thermal field was not experimentally measured). Furthermore, direct numerical simulation (DNS) databases from the literature were also employed for the incoming turbulent flow assessment. Concerning first-order statistics, the SA model demonstrated a better agreement with experiments where the turbulent boundary layer remained attached, for instance, in Cp, Cf, and Us predictions. Conversely, the SST model has shown a slightly better match with experiments over the flow separation zone (in terms of Cp and Cf) and in Us profiles just upstream of the bubble. The Reynolds analogy, based on the St/(Cf/2) ratio, holds in zero-pressure gradient (ZPG) zones; however, it is significantly deteriorated by the presence of streamline curvature-driven pressure gradient, particularly due to concave wall curvature or adverse-pressure gradient (APG). In terms of second-order statistics, the SST model has better captured the positively correlated characteristics of u′ and v′ or positive Reynolds shear stresses ( > 0) inside the recirculating zone. Very strong APG induced outer secondary peaks in and turbulence production as well as an evident negative slope on the constant shear layer.
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Predictability of passive scalar dispersion in atmospheric surface layers with urban‐like roughness: A large‐eddy simulations study
Abstract The predictability of passive scalar dispersion is of both theoretical interest and practical importance, for example for high‐resolution numerical weather prediction and air quality modeling. However, the implications for the numerical modeling of urban areas remain relatively unexplored. Using obstacle‐resolving large‐eddy simulations (LES), we conducted twin experiments, with and without a velocity perturbation, to investigate how the presence of urban roughness affects error growth in streamwise velocity ( u ) and passive scalar ( θ ) fields, as well as the differences between error evolutions in u and θ fields. The predictability limit is characterized using the signal‐to‐noise ratio (SNR) as a continuous metric to indicate when error reaches saturation. The presence of urban roughness decreases of the passive scalar by around 20% compared to cases without them. The error statistics of θ indicate that urban roughness‐induced flow structures and different scalar source locations affect the scalar dispersion and relative fluctuations, which subsequently dictate the evolution of the SNR. Analysis of the passive scalar error energy ( ϵ θ 2 ) budget indicates that the contributions from advective transport by the velocity and velocity error dominate. The error energy spectra of both u and θ exhibit a −5/3 slope in flat‐wall cases, but not in the presence of urban roughness, thereby highlighting the deviation from the assumption of locally isotropic turbulence. This study reveals that urban roughness can decrease the predictability of the passive scalar and destroy the similarity between the error statistics of the velocity and the passive scalar.
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- Award ID(s):
- 2028644
- PAR ID:
- 10453043
- Date Published:
- Journal Name:
- Quarterly Journal of the Royal Meteorological Society
- Volume:
- 149
- Issue:
- 752
- ISSN:
- 0035-9009
- Page Range / eLocation ID:
- 994 to 1017
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/qj.4445
