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In this paper, we use an analysis function for gas diffusion known as the Research Institute for Applied Mechanics, Kyushu University, Computational Prediction of Airflow over Complex Terrain (RIAM-COMPACT), which was developed for complex terrain, in Airflow Analyst software, and apply it to the spread and dissipation of a fluid layer (assuming the fluid layer contains COVID-19 particles). First, to verify the prediction accuracy of the gas diffusion using RIAM-COMPACT, comparisons with past wind tunnel test results conducted on simple and complex terrains are presented under neutral atmospheric stability. The results of the numerical simulations carried out in this study show good agreement with the wind tunnel experiments for both simple and complex terrains. Next, a model of the Japan National Stadium (Tokyo Olympic Stadium) was constructed using 3D detailed topographic Advanced World 3D Map (AW3D) data generated by combining high-resolution satellite images. We tried to reproduce the hypothetical spread and dissipation of the fluid layer (assuming the fluid layer contains COVID-19 particles) inside and outside of the Japan National Stadium using Airflow Analyst implemented with the RIAM-COMPACT analysis function for gas diffusion. We paid special attention to the effect of wind ventilation driven by natural wind. The numerical results under various scenarios show that ventilation driven by natural wind is very effective for the Japan National Stadium.

Currently, computational fluid dynamics (CFD) techniques have entered the stage of practical application in various fields [

In parallel with the above, we are also currently developing new software based on CFD techniques focusing on wind environment assessment in urban areas [

A significant reduction in the required time related to 3D city data construction by utilizing paid and free geospatial data resources that are generally distributed

A significant reduction in the required time for 3D city data construction due to the rapid progress in data compatibility with computer-aided design (CAD), building information modeling (BIM), construction information modeling (CIM), and GIS

It is possible to easily import 3D city data constructed based on satellite photos, aerial photos, and unmanned aerial vehicle (UAV) photos, as well as more sophisticated 3D city data created from laser-measured point cloud data

It is possible to set the wind direction to be calculated, as well as the computational domain and the conditions for grid generation, intuitively and in a short period of time when confirming the actual scale on the map

The simulation results are visualized three-dimensionally on a general map because this map holds spatial reference information, such as coordinate information. Furthermore, spatial analysis that overlaps with other spatial information is also possible

It is simple to distribute or share a series of calculation results by overlaying them on a map service on the Web

In this paper, we apply the RIAM-COMPACT analysis function for gas diffusion, which was developed for complex terrain, to Airflow Analyst and use it to study the spread and dissipation of the fluid layer (assuming the fluid layer contains COVID-19 particles). First, to verify the prediction accuracy of the gas diffusion using RIAM-COMPACT, comparisons with past wind tunnel test results conducted on simple and complex terrain were conducted. Next, a model of the Japan National Stadium (Tokyo Olympic Stadium) was constructed using 3D detailed topographic Advanced World 3D Map (AW3D) data [

For the numerical simulations, a collocated grid in a general curvilinear coordinate system and a staggered grid in a Cartesian orthogonal coordinate system were adopted. For the numerical technique, the finite-difference method (FDM) was adopted, and a large-eddy simulation (LES) model was used for the turbulence model. In the LES model, a spatial filter was applied to the flow field to separate eddies of various scales into grid-scale (GS) components that were larger than the computational grid cells and subgrid-scale (SGS) components, which were smaller than the computational grid cells. Large-scale eddies, i.e., the GS components of turbulence eddies, were directly numerically simulated without the use of a physically simplified model. In contrast, the dissipation of energy, which is the main effect of small-scale eddies (i.e., the SGS components), was modeled according to a physics-based analysis of SGS stress.

For the governing equations of flow in tensor form (

For the computational algorithm, a method similar to the fractional step (FS) method [

In this study, the prediction accuracy was verified by comparing our results with the measurements of the concentration distributions downwind of a point source in a wind tunnel experiment in a neutral case (see Figure

Computational grid in the

Parameters in the study simulations.

Case 1 | Case 2 | Case 3 | |
---|---|---|---|

Domain size | |||

Grid points | |||

Grid width in the | |||

Grid width in the | |||

Nondimensional time increment | |||

Nondimensional time averaging | |||

Reynolds number defined by the height |

To perform validation testing of the gas diffusion modeling, the tracer gas release started from the nondimensional time

Spatial distribution of the instantaneous nondimensional

Spatial distribution of the instantaneous nondimensional

Spatial distribution of the instantaneous nondimensional scalar concentration in the

In this research, various sensitive analyses were performed for the horizontal spatial grid resolution and the length-of-time averages, as shown in Table

Time-averaged numerical results in the

Time-averaged numerical results in the

Time-averaged numerical results in the

Vertical distribution of the scalar concentration at position

Finally, a comparison of the numerical results obtained in this study with the measurements of the concentration distributions in the wind tunnel experiment for a neutral case by Snyder and Hunt [

Measurements of the time-averaged concentration distributions downwind of a point source in the wind tunnel experiment for a neutral case [

Measurements of the time-averaged concentration distributions downwind of a point source in the numerical simulation for a neutral case (Case 1).

For the case of complex terrain, the prediction accuracy of the numerical simulation was verified by comparing it with the result of the wind tunnel experiment for turbulent diffusion in complex terrain reported by Hayashi et al. in 2001 [

Mt. Tsukuba targeted in this study.

In particular, we paid attention to how the central axis of the plume released from an upwind point of Mt. Tsukuba is directly affected by the topographic effects. The wind tunnel experiment was conducted using the diffusion wind tunnel equipment owned by Mitsubishi Heavy Industries (MHI). The test section of the wind tunnel used was 25 m long, 3 m wide, and 2 m high. A 1/5000 scale model was installed in the wind tunnel. Therefore, in the wind tunnel, Mt. Tsukuba was reproduced at a maximum height of 0.175 m. The blockage ratio defined by the wind tunnel height and the model maximum height was 8.75%. Figure

Scale model of Mt. Tsukuba installed in the wind tunnel [

Next, the numerical simulation carried out in this research is outlined. The numerical simulation was performed under almost the same conditions as the wind tunnel experiment. The computational domain was 15 km in the streamwise direction (

We considered the flow pattern near the terrain surface (

Flow pattern near the terrain surface (

Finally, a comparison of the numerical simulation results obtained in this study and the wind tunnel experiment conducted by Hayashi et al. in 2001 [

Measurements of the average surface concentration contours on Mt. Tsukuba in the wind tunnel experiment for a neutral case [

Measurements of the average surface concentration contours on Mt. Tsukuba in the numerical simulation for a neutral case.

Next, using the latest detailed urban data, the Japan National Stadium (Tokyo Olympic Stadium) was reproduced in detail, and a CFD simulation with a large number of grid points/cells was performed. This section introduces the results obtained from these large-scale computations.

Generally, when performing CFD simulations for urban areas, the four processes shown in Figure

Procedure employed for the computational fluid dynamics (CFD) simulation in the present study.

AW3D [

In parallel, considering the building shape of the Japan National Stadium, 3D data were created independently using the 3D modeling software SketchUP based on the publicly available plan. The created 3D model data were imported into GIS according to the position and scale of the UTM coordinate system. Airflow Analyst can recognize 3D model data as a DTM image (in the TIFF format), an ESRI shapefile, or CAD and BIM files. In this way, the user (planner) can easily generate the grid for CFD calculations. As a result, the planner is freed from CFD preprocessing and can concentrate on considering the simulation results and revising the plan based on them. Figure

Japan National Stadium 3D model created based on Advanced World 3D Map (AW3D) and surrounding 3D models.

Computational domain and grid information.

Enlarged view of the grid around the Japan National Stadium.

First, we consider the structure of the flow pattern inside the Japan National Stadium. As shown in Figure

Flow visualization and structure inside and around the Japan National Stadium (Tokyo Olympic Stadium) for the instantaneous flow field: (a) nondimensional

We created a nondimensional

Three-dimensional flow visualization and structure inside the Japan National Stadium (Tokyo Olympic Stadium) used to study the instantaneous flow field using streamlines.

Image of the stadium cross section [

Next, we tried to reproduce the hypothetical spread and dissipation of the fluid layer (assuming the fluid layer contains COVID-19 particles) inside and outside the Japan National Stadium. We paid special attention to the effect of ventilation driven by natural wind. First, we will explore the case inside the stadium. Figure

Volume source inside the stadium.

Time variation of the scalar concentration field in the

Scalar concentration distribution near the spectator seats and the ground in the stadium corresponding to Figure

Time variation of the integrated value of the scalar concentration in the fluid layer shown in red in Figure

Finally, we tried to reproduce the hypothetical spread and dissipation of the fluid layer (assuming the fluid layer contains COVID-19 particles) outside the Japan National Stadium. Similar to the simulation shown above, we paid attention to the effect of ventilation driven by natural wind. As shown in Figure

Two volume sources outside the Japan National Stadium (Tokyo Olympic Stadium).

Spatial distribution of the instantaneous nondimensional

Figure

Time variation of the 2D scalar concentration field emitted from volume source A shown in Figure

Time variation of the 3D scalar concentration field emitted from volume source A shown in Figure

Figure

Time variation of the 2D scalar concentration field emitted from volume source B shown in Figure

Time variation of the 3D scalar concentration field emitted from volume source B shown in Figure

In this paper, we applied the RIAM-COMPACT analysis function for gas diffusion, which was developed for complex terrain, using Airflow Analyst software and then used it to study the spread and dissipation of the fluid layer (assuming the fluid layer contains COVID-19 particles). First, to verify the prediction accuracy of the gas diffusion of RIAM-COMPACT, comparisons with past wind tunnel test results conducted on simple and complex terrains were presented under neutral atmospheric stability. The results of the numerical simulation carried out in this study showed good agreement with the wind tunnel experiment for both simple and complex terrain. Next, a model of the Japan National Stadium (Tokyo Olympic Stadium) was constructed using detailed 3D topographic AW3D data generated by combining high-resolution satellite images. We tried to reproduce the hypothetical spread and dissipation of the fluid layer (assuming the fluid layer contains COVID-19 particles) inside and outside the Japan National Stadium using Airflow Analyst implemented with the RIAM-COMPACT analysis function for gas diffusion. We paid special attention to the effect of ventilation driven by natural wind. For inside the stadium, as the initial condition, we set a situation where the volume source (assuming the volume source contains COVID-19 particles) exists on the entire ground. It should be noted that the accumulated value rapidly decreased about 300 seconds after the release of the scalar concentration due to the separated flow from the roof of the stadium. For outside the stadium, we assumed two volume sources (assuming the volume sources contain COVID-19 particles). One involved setting the volume source inside the near-wake region. The second volume source was set in the region of the flow around the side of the stadium. In both cases, it was clearly shown that once released, the passive scalar was rapidly diffused to the downstream side of the stadium. The numerical results, assuming various scenarios, ultimately demonstrated that ventilation driven by natural wind is very effective for the Japan National Stadium.

The data used to support the findings of this study are available from the corresponding author upon request.

All authors declare no conflicts of interest.

For conducting this research, the authors were provided with various types of data by NTT DATA Corporation and Remote Sensing Technology Center of Japan (RESTEC). The authors would like to express their gratitude to all the organizations.