Charles Deebank

Wind and dispersion in a complex urban environment have been numerically simulated with large-eddy simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) and compared to wind-tunnel measurements. This paper presents a systematic comparison of LES and RANS across mesh resolutions and turbulent Schmidt numbers, linking dispersion predictions to momentum and scalar flux modelling. Model performance was quantitatively assessed using several statistical metrics, including ‘factor-of-two’ (FAC2). The velocity magnitude showed excellent agreement (FAC2 0.94) between numerical and experimental data for both RANS and LES, regardless of the adopted mesh. For the turbulence kinetic energy (TKE), excellent agreement was found for the LES (FAC2 0.99), whereas RANS systematically underpredicted TKE (FAC2 0.90). Scalar concentration predictions were satisfactory for the LES (FAC2 0.85) but significantly poorer for RANS (FAC2 0.54). Notably, RANS required substantially higher mesh refinements to obtain mesh convergence, likely due to large scalar gradients near the point-like release source. Moreover, the RANS agreement with experimental data deteriorated with increasing resolution. Turbulent momentum and scalar (FAC2 0.53) fluxes computed from LES agreed well with the measurements. In contrast, the Boussinesq eddy-viscosity model and the gradient-diffusion hypothesis used in the RANS framework estimated less accurate momentum fluxes and very poor scalar fluxes (FAC2 0.30), respectively. In many cases, RANS predicted turbulent scalar flux in the opposing direction to the experimental and LES results. The results indicate that inaccuracies in RANS concentration predictions are dominated by limitations in scalar-flux closure assumptions, which highlights a RANS modelling limitation. •Mesh-convergence can require significantly higher resolutions for RANS than LES.•LES dispersion predictions are better than RANS for all (from 0.2 to 1.3).•Poor RANS representation of turbulent scalar fluxes is the likely cause.• is recommended for complex urban environments.

The dataset herein is used in the paper titled "Modelling turbulence in axisymmetric wakes: anapplication to wind turbine wakes", whose abstract is provided below. Further details of the paper are provided in the read_me.m file within this data set.A novel fast-running model is developed to predict the three-dimensional (3D) distribution of turbulent kinetic energy (TKE) in axisymmetric wake flows. This is achieved by mathematically solving the partial differential equation of the TKE transport using the Green's function method. The developed solution reduces to a double integral that can be computed numerically for a wake prescribed by any arbitrary velocity profile. It is shown that the solution can be further simplified to a single integral for wakes with Gaussian-like velocity-deficit profiles. Wind tunnel experiments were performed to compare model results against detailed 3D laser Doppler anemometry data measured within the wake flow of a porous disk subject to a uniform freestream flow. Furthermore, the new model is used to estimate the TKE distribution at the hub-height level of the rotating non-axisymmetric wake of a model wind turbine immersed in a rough-wall boundary layer. Our results show the substantial impact of incoming turbulence on TKE generation in wake flows, an effect not fully captured by existing empirical models. The wind-tunnel data also provide insights into the evolution of important turbulent flow quantities such as turbulent viscosity, mixing length, and the TKE dissipation rate in wake flows. Both mixing length and turbulent viscosity are found to increase with the inflow turbulence and the streamwise distance. The turbulent viscosity however reaches a plateau in the far-wake region. Consistent with the non-equilibrium theory, it is also observed that the normalised energy dissipation rate is not constant and it increases with the streamwise distance.