diff --git a/_tutorials/incompressible_flow/Inc_Urban_City/Inc_Urban_City.md b/_tutorials/incompressible_flow/Inc_Urban_City/Inc_Urban_City.md new file mode 100644 index 00000000..c6bbb2b0 --- /dev/null +++ b/_tutorials/incompressible_flow/Inc_Urban_City/Inc_Urban_City.md @@ -0,0 +1,153 @@ +--- +title: unsteady pollutant dispersion in a city +permalink: /tutorials/Inc_Urban_City/ +written_by: Nijso Beishuizen +for_version: 8.4.0 +revised_by: +revision_date: +revised_version: +solver: INC_NAVIER_STOKES +requires: SU2_CFD +complexity: intermediate +--- + +![urban_city](../../../tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity.png) + +Figure (1): Flow through the streets of the city center of amsterdam. + +## Goals + +In this tutorial we will look at the modeling of wind speed and pollutant dispersion in a realistic depiction of a city - in this case the city center of Amsterdam. We will show the following aspects: +- outline of creating a mesh of a city using GIS data +- obtaining weather data of a specific date +- simulate wind velocity to map pedestrian comfort. +- simulate dispersion of a smoke cloud from a point source (burning car for instance) through streets to assess evacuation zone + + + +## Resources + +The resources for this tutorial can be found in the [incompressible_flow/Inc_Urban_City](https://github.com/su2code/Tutorials/tree/master/incompressible_flow/Inc_Urban_City) directory in the [tutorial repository](https://github.com/su2code/Tutorials). You will need the configuration file ([unsteady_incomp_cylinder.cfg](https://github.com/su2code/Tutorials/tree/master/incompressible_flow/Inc_Von_Karman_Cylinder/unsteady_incomp_cylinder.cfg)) and the mesh file ([cylinder_wake.su2](https://github.com/su2code/Tutorials/tree/master/incompressible_flow/Inc_Von_Karman_cylinder/cylinder_wake.su2)). Additionally, the Gmsh geometry is also provided so you can recreate the mesh yourself: [cylinder_wake.geo](https://github.com/su2code/Tutorials/tree/master/incompressible_flow/Inc_Von_Karman_Cylinder/cylinder_wake.geo). + + +### Background + +CFD simulations around buildings in a city have many applications. One application is to investigate pedestrian comfort due to strong winds that can occur around large buildings. Another application is the risk assessment in case of an accident where harmful polutants are released, for instance the smoke of a car fire. Both of these cases can be simulated with SU2 very easily. The challenge is in obtaining a mesh of the region of interest. + +Information about buildings, specifically their surface shape and often even their height and 3D shape, is available in many countries like the Netherlands. This data is stored in GIS-databases (Geographic Information System). Google maps and google earth are examples of well-known GIS databases. We will use 2D building information and create a 2D mesh from it. +With a simple python script it is possible to retrieve information of buildings given a city name, or longitude-latitude coordinates. +These building contours then need to be converted into a set of closed contours that can be given to a mesher like gmsh to create a 2D mesh for the SU2 CFD solver. + +### Pedestrian comfort: flow through the streets of Amsterdam + +What was the least comfortable street to walk on in the city center of Amsterdam, on new years day 2026 at noon? + +First, we need the average wind velocity at that time. A simple python script can get that information from open-meteo, given the latitude and longitude, using the python requests library: + +```python +import requests +city center of amsterdam +[lat,lon]=[52.373080,4.892453] +# Open-Meteo API endpoint (free, no API key needed) +url = "https://api.open-meteo.com/v1/forecast" +date="2026-01-01" +#time=12:00 +params = { + 'latitude': lat, + 'longitude': lon, + 'start_date': date, + 'end_date': date, + 'daily': 'temperature_2m_mean,pressure_msl_mean,wind_speed_10m_max,wind_speed_10m_mean,wind_direction_10m_dominant,relative_humidity_2m_mean', + 'timezone': 'auto' + } +response = requests.get(url, params=params) +data = response.json() +print(data) +``` + +We can also get the building information as polygons. For this information, we can use the overpass API (). We can grab all buildings within a certain radius from our given latitude and longitude. The core of the program is: +```python +overpass_url = "https://overpass-api.de/api/interpreter" + overpass_query = f""" + [out:json][timeout:60]; + ( + way["building"](around:{radius_meters},{latitude},{longitude}); + relation["building"](around:{radius_meters},{latitude},{longitude}); + way["building:part"](around:{radius_meters},{latitude},{longitude}); + relation["building:part"](around:{radius_meters},{latitude},{longitude}); + ); + out geom; + """ +``` +This information can then be saved as polygon data to a file. The information that we retrieved is visualized in Figure 2: + +![Building information](../../../tutorials_files/incompressible_flow/Inc_Urban_City/images/amsterdam_buildings.png) + +Figure 2: Visualization of the building information that was retrieved. The polygon data was saved as a pickle file for further processing. + +We found 1615 buildings in this region. +With this polygon information, we can build a 2D CFD mesh, but the first step towards that goal is to merge all the connecting and overlapping buildings. For buildings that are not touching but are separated by a very small distance, we use the dilate-erode method. First, let buildings that are very close grow in size, then compute the new contours of the merged shapes, then erode the shapes again to go back to their original size. We also remove any contours with small surface areas. We are then left with only 125 buildings, a huge reduction! + +We can further smooth the shapes by using dilation-erosion, and also by using the Douglas-Peucker method to simplify polygons and reduce node and edge count. After this procedure we went from 10730 vertices to 2374 . We also remove all buildings with an are smaller than 25 square meters. We are now left with 115 buildings and 2209 edges. + + +### Mesh Description + + +The final step in creating this setup is to give all polygons to gmsh and create the mesh. We use a triangulated mesh with a quadrilateral inflation layer around the buildings. The final mesh is 770k cells. + +
+ + + + + + +
+Figure 3: Gmsh mesh and zoom of the mesh around the royal palace showing the inflation layers. Note that the mesh is very coarse close to the wall and needs to be refined for more accurate results. + + + +### Configuration File Options + +The setup itself is a standard setup. The main input is the velocity: +``` +% wind speed on 01-01-2026 at 12:00 in Amsterdam (262 degrees 6.81 m/s) +INC_VELOCITY_INIT = (6.74, 0.95, 0.0 ) +``` + +And we need to define the boundaries. We only have a far-field and the walls of the building: +``` +MARKER_FAR= farfield +MARKER_HEATFLUX= wall_buildings +``` + + +### Running SU2 + +If possible, always use a parallel setup to reduce computational time (wall clock time). Run the SU2_CFD executable in parallel using MPI and 4 nodes by entering: + + $ mpirun -n 4 SU2_CFD amsterdam.cfg + + + +### Results + + +![urban_city](../../../tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity_zoom.png) + +Figure (4): Zoom of the flow through the streets of the city center of amsterdam. + +The zoom of the wind velocity in Figure 4 shows the wind around the royal palace on the 'dam' with the national monument for war victims on the right. It is clear that walking through the 'paleisstraat' into the main square would have been very uncomfortable. + + +### Fire! + +What if there was a fire on the main square? How would the smoke be transported by the wind through the streets of Amsterdam? This can be simulated very easily by adding a species source term using the python wrapper. We will use a circular source of smoke. + +### Final notes + +The paraview statefile to create the movie can be found here: [statefile_with_particles.pvsm](https://github.com/su2code/Tutorials/blob/master/incompressible_flow/Inc_Von_Karman/statefile_with_particles.pvsm) +and here: +[statefile_movablearrow_timeseries.pvsm](https://github.com/su2code/Tutorials/blob/master/incompressible_flow/Inc_Von_Karman/statefile_movablearrow_timeseries.pvsm) +Note that you have to select your own, local files when you load the statefile. diff --git a/tutorials_files/incompressible_flow/Inc_Urban_City/images/amsterdam_buildings.png b/tutorials_files/incompressible_flow/Inc_Urban_City/images/amsterdam_buildings.png new file mode 100644 index 00000000..db358a84 Binary files /dev/null and b/tutorials_files/incompressible_flow/Inc_Urban_City/images/amsterdam_buildings.png differ diff --git a/tutorials_files/incompressible_flow/Inc_Urban_City/images/amsterdam_complete_map.png b/tutorials_files/incompressible_flow/Inc_Urban_City/images/amsterdam_complete_map.png new file mode 100644 index 00000000..e6a05dc5 Binary files /dev/null and b/tutorials_files/incompressible_flow/Inc_Urban_City/images/amsterdam_complete_map.png differ diff --git a/tutorials_files/incompressible_flow/Inc_Urban_City/images/gmsh_mesh.png b/tutorials_files/incompressible_flow/Inc_Urban_City/images/gmsh_mesh.png new file mode 100644 index 00000000..e140f016 Binary files /dev/null and b/tutorials_files/incompressible_flow/Inc_Urban_City/images/gmsh_mesh.png differ diff --git a/tutorials_files/incompressible_flow/Inc_Urban_City/images/gmsh_mesh_zoom.png b/tutorials_files/incompressible_flow/Inc_Urban_City/images/gmsh_mesh_zoom.png new file mode 100644 index 00000000..8c99827b Binary files /dev/null and b/tutorials_files/incompressible_flow/Inc_Urban_City/images/gmsh_mesh_zoom.png differ diff --git a/tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity.png b/tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity.png new file mode 100644 index 00000000..c5170858 Binary files /dev/null and b/tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity.png differ diff --git a/tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity_zoom.png b/tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity_zoom.png new file mode 100644 index 00000000..b8b99c1c Binary files /dev/null and b/tutorials_files/incompressible_flow/Inc_Urban_City/images/wind_velocity_zoom.png differ