A significant amount of radioactive material was accidentally discharged into the atmosphere from the Fukushima Dai-ichi Nuclear Power Plant from 12 March 2011, which produced high contaminated areas over a wide region in Japan. In conducting regional-scale atmospheric dispersion simulations, the computer-based nuclear emergency response system WSPEEDI-II developed by Japan Atomic Energy Agency was used. Because this system is driven by a meso-scale meteorological (MM) model, it is difficult to reproduce small-scale wind fluctuations due to the effects of local terrain variability and buildings within a nuclear facility that are not explicitly represented in MM models. In this study, we propose a computational approach to couple an LES-based CFD model with a MM model for detailed simulations of turbulent winds with buoyancy effects under real meteorological conditions using turbulent inflow technique. Compared to the simple measurement data, especially, the 10 min averaged wind directions of the LES differ by more than 30 degrees during some period of time. However, distribution patterns of wind speeds, directions, and potential temperature are similar to the MM data. This implies that our coupling technique has potential performance to provide detailed data on contaminated area in the nuclear accidents.
A significant amount of radioactive material was accidentally discharged into the atmosphere from the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) from 12 March 2011. In this nuclear accident, a computer-based nuclear emergency response system, Worldwide version of System for Prediction of Environmental Emergency Dose Information (WSPEEDI-II) developed by Japan Atomic Energy Agency was used to conduct atmospheric dispersion simulations from regional to hemispheric scales (Katata et al., 2012). This system consists of a meso-scale meteorological (MM) model and Lagrangian particle dispersion model, which can provide near real-time predictions of mean air concentrations and mean surface deposition of radionuclides. However, it is difficult to estimate contaminated areas in a local-scale where turbulent motions are dominant by the influence of local terrain variability and roughness elements such as individual buildings and trees within a nuclear facility that are not explicitly resolved in MM simulation models.
For simulating wind flows and plume dispersion in a local-scale, a computational fluid dynamics (CFD) technique is commonly used. In CFD models, buildings and structures can be explicitly represented at high resolutions. In particular, the CFD simulations using large-eddy simulation (LES) are effective to capture complex behaviors of impinging, separating, and circulating flows around a bluff body. Therefore, an approach to couple an LES-based CFD model with a MM model is expected to have a potential of becoming an effective tool to provide detailed information on turbulent flows and plume dispersion in a local-scale under real meteorological conditions. For example, in the approach by Wyszogrodzki et al. (2012), the MM outputs such as pressure, wind velocity, and potential temperature calculated based on RANS simulations were directly imposed at the inflow boundaries of the LES-based CFD model. Although they provided reasonable results in comparison to field experimental data, the inflow data did not include high-frequency turbulent fluctuations appropriate to drive LES-based CFD models. Michioka et al. (2013) coupled a micro-scale LES-based CFD model with a meso-scale LES-based model and investigated spatial distributions of plume concentrations in a residential area. However, they assumed a neutral atmospheric stability condition in the micro-scale model. The application of their approach is limited to a case building-induced turbulence is dominant.
In Japan, most nuclear facilities are located at coastal complex terrains. In this case, the assumption of neutral stability is less valid and thermal stability effects should be considered. Therefore, it is important to generate thermally-stratified boundary layers with small-scale wind fluctuations in order to more faithfully reproduce meteorological conditions in the LES model from the MM outputs. In this study, we propose a calculation approach to simulation of thermally-stratified boundary layer flows by coupling an LES-based CFD model with a MM model and examine the effectiveness of the approach in comparison to measurement and MM simulation data.
The model used for local-scale detailed simulations is the LOHDIM-LES
developed by Nakayama et al. (2014). The basic equations are the filtered
continuity, Navier-Stokes, and temperature transport equations under the
Boussinesq approximation. The subgrid scale (SGS) turbulent effect is
represented by the standard Smagorinsky model (1963) with a constant value of
0.1. The SGS scalar flux is also parameterized by an eddy viscosity model and
the turbulent Prandtl number is set to a constant value 0.71. The turbulent
effects of local terrain and plant canopy are represented by the external
force term and are incorporated into the Navier–Stokes equation. The terrain
effects are represented by immersed boundary method proposed by Goldstein et
al. (1993) as follows;
Nudging terms for wind velocity and temperature fields are incorporated into
the Navier–Stokes equation in order to maintain the mean structure of the
MM model in the LES computational domain and can be expressed as the
follows, respectively;
The coupling algorithm of the velocity and pressure fields is based on the marker-and-cell method with the second-order Adams-Bashforth scheme for time integration. The Poisson equation is solved by the successive over-relaxation method. For the spatial discretization in the basic equations, a second-order accurate central difference scheme is used.
Mean wind directions are not always constant and often vary due to a change
of weather conditions. Therefore, first, we proposed the treatment of the
inflow boundary conditions in order to automatically input wind velocity data
obtained by the MM model into the LES model under neutral stability
conditions, depending on mean wind directions in the meteorological field
(Nakayama et al., 2012, 2015). Figure 1 shows a schematic diagram of the treatment
of inflow boundary conditions in the LES model. First, mean wind directions
in the meteorological field are estimated and vertical planes of inflow
boundaries are determined automatically depending on them. For example, when
mean wind directions
Schematic diagram of the treatment of inflow boundary conditions in the CFD model depending on mean wind directions in the meteorological field. The solid and dotted lines are computational regions to drive turbulent winds and simulate thermally-stratified boundary layers, respectively. The open and filled arrows indicate input of wind velocities and potential temperature obtained by a MM model, respectively.
In order to drive an LES model, a recycling method proposed by Kataoka and
Mizuno (2002) is adopted. In this turbulent inflow technique, only
fluctuating components are extracted at a recycling station and recycled
back to the inlet boundary. The formulation is as follows:
In order to produce thermally-stratified boundary layers, vertical profiles of potential temperature data obtained by a MM model are imposed at a distance of 1 km inward from each horizontal boundary. As well as the case for a wind velocity field, vertical planes of inlet and outlet boundaries are automatically determined depending on mean wind directions in the meteorological field.
The meso-scale meteorological simulation model used here is the Weather Research and Forecasting (WRF) model, the Advanced Research WRF Version 3.3.1 (Skamarock et al., 2008) to provide the input data for the LES model. We use a nesting capability to resolve the FDNPP region at a fine grid spacing by setting two-way nested, four computational domains (with the top being at the 50 hPa level). The four domains cover areas of 2025 km by 2025 km at 4.5 km grid, 720 km by 720 km at 1.5 km grid, 150 km by 180 km at 500 m grid, and 50 km by 50 km at 100 m grid, respectively (Fig. 2a–d). The number of vertical levels is 53, with 15 levels in the lowest 1 km depth.
Computational areas of the WRF and LES models. The WRF is
configured with for nested domains covering areas of
The terrain data used are the global 30 s data (GTOPO30) from the US Geological Survey for the outer 2 domains and the 50 m mesh digital elevation model (DEM) dataset by the Geographical Survey Institute (GSI) of Japan for the inner 2 domains. The land-use/land-cover information is obtained from the 100 mesh dataset from the Ministry of Land, Infrastructure, Transport and Tourism of Japan.
To determine the initial and boundary conditions, we use 6-hourly Mesoscale Analysis (MANAL) data of Japan Meteorological Agency (JMA), 6-hourly Final Analysis data of the US National Centers for Environmental Prediction (NCEP FNL), and daily Merged Sea Surface Temperature (MGDSST) analyses of JMA. The times of the 6-hourly MANAL and NCEP FNL are 00:00, 06:00, 12:00, and 18:00 UTC. The horizontal resolutions of MANAL and MGDSST are 10 km and 0.25 degree, respectively. Full physics processes are included in the present simulation in order to reproduce real meteorological phenomena. A physics parameterization closely relevant to the simulation of wind fields is a PBL mixing parameterization. We choose a Mellor-Yamada Level 2.5 scheme of Janjic (2002) in which mixing is done vertically between the adjacent vertical levels. A single-moment, 6-category water- and ice-phase microphysics of Hong and Lim (2006) is employed for cloud and precipitation processes in all the domains.
The case studied is the FDNPP accident on 12 March 2011. In order to simulate wind fields for this event, the time period of the WRF simulation is from 00:00 UTC 11 March 2011 to 00:00 UTC 13 March 2011. The WRF outputs during 05:00 UTC 12 March and 06:00 UTC 12 March are used for the LES model. The simulated outputs of the innermost domain at 1 min interval are used as the inputs of the LES model.
The size of the computational domain is 11.0 km by 11.0 km in the horizontal directions with the depth of 1.6 km (Fig. 2e). The total mesh number is 550 by 550 by 94 nodes. The grid spacing is 20 m in the horizontal directions and 2.5–64 m stretched in the vertical direction based on an orthogonal grid system. In the previous study, we conducted LESs of urban boundary layer flows in the urban central district by coupling with a MM model and showed reasonable results in comparison to the field experimental data of vertical profiles of wind speeds and directions (Nakayama et al., 2015). Those calculation conditions such grid resolution and computational domain were almost the same as the present ones. Therefore, it is considered that the present model set-up is reasonable to reproduce basic characteristics of the meteorological conditions in the LES model. For a wind velocity field, the inlet and spanwise boundaries are determined by the WRF wind velocity data (with 1 min interval and 100 m resolution) linearly interpolated on the spatial resolution of the LES domain with 1 min interval. At the outlet boundary, a free-slip condition is applied for each component of wind velocity. At the upper boundary, a free-slip condition for the horizontal velocity components and zero-speed condition for the vertical velocity component is imposed. For a potential temperature field, the bottom, ground surfaces, and spanwise boundaries are determined by the WRF potential temperature data (with 1 min interval and 100 m resolution) linearly interpolated on the spatial resolution of the LES domain with 1 min interval. At the outlet, a free-slip condition is imposed.
Focusing on the ground surface of the study site shown in Fig. 3a, it is found that the FDNPP is located along the coast and many forest canopies are densely situated over the land. Ground surface geometries are represented using the 50 m mesh DEM of GSI linearly interpolated on the spatial resolution of the LES model. Buffer zones with a length of 1.0 km is set in only land area and roughness blocks are placed in order to represent roughened ground surface as shown in Fig. 3b. The forest canopy is arranged based on the mixed forest in USGS 24-category land use. The LAI and canopy height are set to 4.0 and 12.0 m, respectively.
The nudging constant is set to rapidly vary from 0.0 to 0.01 s
Instantaneous fields of
Vertical profiles of wind speed, wind direction, and potential
temperature obtained at
Figure 4 shows instantaneous fields of (a) wind speed and (b) potential temperature. It is found that small-scale fluctuations in both wind and temperature fields are reproduced by the turbulent inflow technique. Figure 5 compares the LES results with vertical profiles of wind speeds, wind directions, and potential temperature of the WRF model obtained at (a) Main gate, (b) MP4, and (c) MP8 at 06:00 UTC 12 March 2011. The LES wind speeds and directions are found to be generally distributed along the WRF data and considerably fluctuate up to 100 m height at each point due to the turbulent effects by local terrain variability and forest canopy. However, at MP4, locally rapid variations of wind speeds and directions around 750 m are not captured in the LES model. The vertical profiles of potential temperature are similar to those of the WRF model.
Time series of
Figure 6 compares the LES results with the simple measurement and WRF data of
time series of wind speeds and directions. The measurement and LES data are
obtained at the ground-level and the WRF data are obtained at a height of
50 m. According to the press release (28 May 2011) by Tokyo Electric Power
Company (2011), all monitoring posts at the FDNPP did not work due to blackout caused
by the severe earthquake. Therefore, wind speeds, wind directions, and
radiation dose were measured by monitoring cars. Although these simple
measurement data are not appropriate to evaluate the model performance, we
use the data for a comparison. The measurement data of wind speeds vary
within the range from 2.7 to 3.5 m s
Although the difference between the LES and measurement data are observed at a ground-level during some period of time, it is successful in generally maintaining the simulated meteorological fields as the basic flow with buoyancy effects in the LES domain.
We proposed a calculation approach to couple the LES-based CFD model with the MM model for detailed simulations of turbulent winds with buoyancy effects under real meteorological conditions using turbulent inflow technique and examined the effectiveness in comparison to the simple measurement and MM simulation data. Inflow boundary conditions were set to automatically input wind velocity data obtained by the MM model into the LES model, depending on mean wind directions in the meteorological field. In generating thermally-stratified boundary layers from the MM outputs, first, small-scale wind fluctuations were generated at the main inlet boundary by a recycling technique. Then, the potential temperature profiles obtained by the MM model were imposed at the recycle station.
Compared to vertical profiles of the MM simulation data, it is seen that the LES wind speeds, directions, and potential temperature generally fluctuate around the MM data although locally rapid variations of wind speeds and directions are not reproduced well. The 10 min averaged wind directions of the LES differ by more than 30 degrees from those of the measurement data during some period of time although the averaged wind speeds of the LES are in good agreements with them. Important issues still remain in accurately simulating local-scale turbulent winds at a ground-level under real meteorological conditions. However, our approach is successful in generally reproducing thermally-stratified boundary layer flows with turbulent fluctuations in the LES model. It can be concluded that our coupling technique has potential performance to provide detailed data on contaminated area in the nuclear accidents.
This study is partly supported by General Collaborative Research #25G-05 provided by Disaster Prevention Research Institute, Kyoto University, is also supported by JSPS KAKENHI Grant 26282107. Edited by: M. Piringer Reviewed by: two anonymous referees