Large-Eddy Simulation of plume dispersion within various actual urban areas Hiromasa Nakayama*, Klara Jurcakova** and Haruyasu Nagai* *Japan Atomic Energy Agency, Japan **Academy of Sciences of the Czech Republic, Prague

Local-scale atmospheric dispersion models for emergency response Atmospheric dispersion problems within urban areas Accidental release of chemical materials Intentional release of hazardous materials by terrorist attack Prediction of spatial extent of contaminated regions using building-resolving CFD models M2UE (Danish Meteorological Institute) FAST3D-CT (U.S. Naval Research Laboratory) FEM3MP (U.S. National Atmospheric Release Advisory Center) MSS (French Atomic Energy Commission) LOHDIM-LES (Japan Atomic Energy Agency) In local-scale atmospheric dispersion problems, we have important issues such as accidental release and intentional release of hazardous materials within urban area. For investigating spatial extent of contaminated areas, it is well known that numerical simulation technique using CFD is very useful. Many researchers have developed emergency response tool. Our group team also has developed LES-based Local-scale High-resolution Dispersion model. These urban dispersion models resolve individual urban buildings and can provide detailed information on turbulent flow patterns and spatial distribution patterns of plume concentrations. These urban dispersion CFD models can provide detailed information on turbulent flow patterns and spatial distributions of plume concentration within urban areas.

What is the spatial extent of distribution patterns of concentration influenced by urban surface geometry? Schematic of wind flow in urban area (Ratti et al. 2002 and Nakayama et al. 2012) Urban areas consisting of buildings with variable height and obstacle density Strong three-dimensionality of the turbulent flows within urban canopy Complex spatial distribution patterns of plume concentration In urban areas, the lower part of boundary layer is called as roughness sub-layer and especially in urban canopy layer the strong three-dimensionality of turbulent flows are formed by the influence of each urban obstacle. As a result, distribution patterns of plume concentrations become highly complex. Therefore, in numerically simulating plume dispersion, an important issue remains in setting up enough computational domain size in order to capture distribution patterns of plume concentration influenced by urban surface geometries. This table shows roughness parameters of European, North American, and Central Tokyo. Building height variability is defined as the ratio of the standard deviation of building height to the average building height. Roughness density is defined as the ratio of the total frontal area of roughness elements to the total surface area. Building height variability values of North America and Central Tokyo surface geometries of North America and Central Tokyo are found to be highly variable and show much larger than the ones of Europe. Roughness density values of Salt Lake City and Berlin are found to be smaller than the other areas. From these data, urban surface geometries show a wide variety due to a wide range of building height variability and roughness density. In this study, our objective is to carry out a series of LESs of plume dispersion in various urban areas and clarify the spatial extent of the distribution patterns of influenced by urban surface geometry by comparative analysis. The extent to which a specific air dispersion model is suitable for the evaluation of air toxic source impacts depends upon several factors Objective Carry out a series of LESs of plume dispersion in urban areas with a wide range of building height variability and obstacle density Clarify the distribution patterns of plume concentration influenced by urban surface geometry by comparative analysis

Model structure of LOHDIM-LES developed at JAEA Recycling only fluctuating components of wind velocity Turbulent Flow Turbulent Inflow Fully-developed urban boundary layer flow Spatially-developing rough-wall boundary layer flow Tripping Fence Roughness Blocks Urban area Driver region Main analysis region Schematic diagram Procedure of calculating Generate basic boundary layer flow by recycling method(Kataoka&Mizuno,2002) Produce active turbulent fluctuations by various roughness obstacles Carry out LESs of turbulent flow and plume dispersion in urban areas Basic equations Spatially-filtered continuity equation, Navier-Stokes equation, and Scalar conservation equation SGS turbulent and scalar effects For flow field, the standard Smagorinsky model(1963) with the constant of 0.1 For dispersion filed, the standard Smagorinsky model with the turbulent Schmidt number of 0.5 Building effects Immersed boundary method by Goldstein et al. (1998) This figure shows a model structure of LOHDIM-LES developed at JAES. We set up a driver region for generating spatially-developing boundary layer flow and main analysis region. First, by recycling method, basic turbulent boundary layer flow is generated and active turbulent fluctuations are produced by various roughness obstacles. Then, by imposing this turbulent inflow data at the inlet boundary of main region, LES of plume dispersion in urban area is carried out. 4

Resolved actual urban area Computational conditions Computational model Driver region Main analysis region Resolved actual urban area Mesh number 460×250×90 475×250×90 375×250×90 Domain size 5.5km×1km×1km 2.0km×1km×1km 1.5km×1km×1km Grid resolution 4.0m-20.0m×4.0m× 1.3m-53m 4.0m×4.0m× Boundary Flow field Dispersion field Inlet Driver region Recycling technique (Kataoka & Mizuno, 2002) Main region Turbulent inflow generated in the driver region Exit Conventional convective type Top Ground Side Periodic Building effects External force (Goldstein et al.,1993)

Cases of comparative analysis for wind tunnel experiments（Bezpalcova,2008）and LESs Surface geometry type Average building height[m] Building height variability[-] Obstacle density[-] Idealized urban canopy (Bezpalcova,2008) Cubic buildings array 42.0 0.0 0.16 0.25 0.33 Actual urban site in Central Tokyo (present LESs) Low-rise buildings area 9.6 0.60 0.39 Street-canyon area 22.7 0.71 0.56 Complex of high-rise and low-rise buildings area 26.5 0.85 0.52 High-rise buildings area 34.1 0.94 In this study, we set up two types of urban surface geomtries.

Building heights distributions of Central Tokyo used in LESs 100m 0m NNW (a)Low-rise buildings area (b)Street-canyon area 100m 0m NNW (c)Complex of high-rise and low-rise buildings area (d)High-rise buildings area

Characteristics of approach flow generated in driver region 1km 1km Tripping fence Roughness blocks 0.5km 5.0km These figure compares the LES results of turbulence characteristics of approach flow with the wind tunnel experimental data of Bezpalcova and Ohba (2008) and the recommended data of Engineering Science Data Unit (ESDU). ESDU provides comprehensive turbulence characteristics of neutrally stratified atmospheric boundary layer ranging from the ground surface to 300m. It is found that mean wind velocity of LES is consistent with experimental profile of 0.25 power law. Turbulence intensities for each component of the experiment lie within the ESDU recommended data of moderate rough and rough surfaces. Although w-component of the LES is consistent with the experimental data, u-component is underestimated and v-component is overestimated. But, each component of the LES also lie within the ESDU recommended data. Mean velocity Turbulence intensity for u-component Turbulence intensity for v-component Turbulence intensity for w-component

Spatial distributions of mean concentration at ground level Source location Source location 10-1 10-6 Cmean/Cinit NNW (a)Low-rise buildings area (b)Street-canyon area Source location Source location 10-1 10-6 Cmean/Cinit NNW (c)Complex of high-rise and low-rise buildings area (d)High-rise buildings area

Spatial distributions of r.m.s. concentration at ground level Source location Source location 10-1 10-6 Cr.m.s./Cinit NNW (a)Low-rise buildings area (b)Street-canyon area Source location Source location 10-1 10-6 Cr.m.s./Cinit NNW (c)Complex of high-rise and low-rise buildings area (d)High-rise buildings area

Mean concentration distributions along wind direction from the point source

R.m.s. concentration distributions along wind direction from the point source

Conclusion We investigated spatial extent of the distribution patterns of plume concentrations within urban canopy. Mean concentration distributions are nearly the same among various urban areas at a downwind distance of the point source greater than 1.0km. The difference of r.m.s. concentration distributions among various urban areas also become small at a downwind distance of the point source greater than 1.0km. In order to capture distribution patterns of plume concentrations within urban canopy, computational domain size should be at least 1.0km along wind direction from the point source.