Update README.md

README.md

@@ -59,7 +59,7 @@ Next, we will explain how we tackled the data and the simulation orchestration i
 ## Generating a large quantity of Automobile-like 3D Meshes
 Due to the lack of publicly available 3D meshes of automobile objects, we decided to use recent advances in image-to-mesh models to generate meshes from images of automobiles that are freely available. 
 
-More specifically, we used the InstantMesh model, which is open-source (Apache-2.0) and is currently state-of-the-art in image-to-mesh generation. The automobile-like meshes were generated by running the InstatMesh image-to-mesh model on 1k images from the publicly available (Apache-2.0) Stanford Cars Dataset consisting of 16,185 images of automobiles.
+More specifically, we used the [InstantMesh](https://github.com/TencentARC/InstantMesh) model, which is open-source (Apache-2.0) and is currently state-of-the-art in image-to-mesh generation. The automobile-like meshes were generated by running the InstatMesh image-to-mesh model on 1k images from the publicly available (Apache-2.0) [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset) consisting of 16,185 images of automobiles.
 
 Naturally, running the image-to-mesh model leads to meshes that may have certain defects, such as irregular surfaces, asymmetry issues, holes and disconnected components. Therefore, after running the image-to-mesh model, we run a custom post-processing step where we try to improve the meshes quality. We used PCA to align the mesh with the main axis and we removed disconnected components.
 
@@ -69,9 +69,9 @@ The resulting set of meshes still have little defects, such as presence of "spik
 ## Orchestrating 20k simulations on the cloud (just using Python)
 For solving the challenge of orchestrating 20k OpenFOAM simulations, we resorted to the Inductiva API. The Inductiva platform exposes a simple Python API for running simulation workflows on the cloud. Inductiva makes available several popular open-source simulation packages, including OpenFOAM. Here is an example of how to run an OpenFOAM simulation using Inductiva (point to the doc that Paulo is preparing). 
 
-Using the Inductiva API, it becomes easy to parametrise specific simulation scenarios and run variations of a base case by programatically changing the input parameters and starting conditions of the simulation. Additionally, users can build custom Python classes that wrap parameterized simulation scenarios, allowing them to have a simple Python interface to running simulations without the need to directly interface with the low level simulation packages. We leveraged Inductiva API to create a Python class for the Wind Tunnel scenario (point to GitHub), which we then used to run 20k simulations over a range of input parameters.
+Using the Inductiva API, it becomes easy to parametrise specific simulation scenarios and run variations of a base case by programatically changing the input parameters and starting conditions of the simulation. Additionally, users can build custom Python classes that wrap parameterized simulation scenarios, allowing them to have a simple Python interface to running simulations without the need to directly interface with the low level simulation packages. We leveraged [Inductiva API to create a Python class for the Wind Tunnel scenario](https://github.com/inductiva/wind-tunnel), which we then used to run 20k simulations over a range of input parameters.
 
-For more information on how to transform complex simulation workflows in simple Python classes check this blog post (point to the blog post).
+For more information on how to transform complex simulation workflows in simple Python classes check this [blog post](https://inductiva.ai/blog/article/transform-complex-simulations).
 
 
 # How did we generate the dataset?
@@ -112,28 +112,60 @@ Here’s a breakdown of the files included in each simulation:
 
 
 ### input_mesh.obj
-Input mesh generated with InstantMesh model from images of the Stanford Cars Dataset.
-This mesh was used as the input of the OpenFoam simulation.
+The input mesh was generated using the Instant Mesh model from images in the Stanford Cars Dataset. This mesh serves as the input for the subsequent OpenFOAM simulation.
 
-The mesh generation process is described [here](#why).
+Details on the mesh generation process can be found [here](#Generating-a-large-quantity-of-Automobile-like-3D-Meshes).
 
 | **Input Mesh**               | **Points Histogram**           |
 |-------------------------------|------------------------------|
 | ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/input_mesh.png) | ![Histogram](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/histogram_of_points_input.png) |
 
+    
+```python
+import pyvista as pv
+
+# Load the mesh
+mesh_path = "input_mesh.obj"
+mesh = pv.read(mesh_path)
+
+# Get the vertices (points)
+vertices = mesh.points
+
+# Get the faces (connections)
+# The faces array contains the number of vertices per face followed by the vertex indices.
+# For example: [3, v1, v2, v3, 3, v4, v5, v6, ...] where 3 means a triangle.
+faces = mesh.faces
+``` 
+
+
+
 ### openfoam_mesh.obj
-Output mesh obtained from the OpenFoam simulation. The number of points is smaller than `input_mesh` due to internal OpenFoam processing.
+This mesh was generated as a result of the OpenFOAM simulation. The number of points is reduced compared to the `input_mesh.obj`, due to mesh refinement and processing steps applied by OpenFOAM during the simulation.
 
 | **Open Foam Mesh**               | **Points Histogram**           |
 |-------------------------------|------------------------------|
 | ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/openfoam_mesh.png) | ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/histogram_of_points_foam.png) |
 
-### pressure_field_mesh.obj
-We extracted pressure values from the `openfoam_mesh.obj`.
-Then we interpolated the pressure values with closest_point strategy on the `input_mesh.obj` so that we have a higher resolution mesh.
-As can be seen on the histogram, the distribution of points is the same as the input_mesh.obj.
+```python
+import pyvista as pv
+
+# Load the mesh
+mesh_path = "openfoam_mesh.obj"
+mesh = pv.read(mesh_path)
+
+# Get the vertices (points)
+vertices = mesh.points
+
+# Get the faces (connections)
+# The faces array contains the number of vertices per face followed by the vertex indices.
+# For example: [3, v1, v2, v3, 3, v4, v5, v6, ...] where 3 means a triangle.
+faces = mesh.faces
+``` 
+
+### pressure_field_mesh.vtk
+Pressure values were extracted from the `openfoam_mesh.obj` and then interpolated onto the `input_mesh.obj` using the closest point strategy. This approach allowed us to project the pressure values onto a higher-resolution mesh. As shown in the histogram, the distribution of points matches that of the `input_mesh.obj`.
 
-More information [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018531ff05d0d8ef9d63d8c108800f78f/windtunnel/windtunnel_outputs.py#L111).
+More details can be found here [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018531ff05d0d8ef9d63d8c108800f78f/windtunnel/windtunnel_outputs.py#L111).
 
 | **Pressure Field Mesh**               | **Points Histogram**           |
 |-------------------------------|------------------------------|
@@ -141,9 +173,9 @@ More information [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018
 
 
 ### streamlines_mesh.ply
-We generated streamlines from the `openfoam_mesh.obj`.
+Streamlines were generated from the openfoam_mesh.obj, providing a visual representation of the flow characteristics within the simulation.
 
-More information [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018531ff05d0d8ef9d63d8c108800f78f/windtunnel/windtunnel_outputs.py#L70).
+More information can be found [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018531ff05d0d8ef9d63d8c108800f78f/windtunnel/windtunnel_outputs.py#L70).
 
 
 
@@ -152,10 +184,7 @@ More information [here](https://github.com/inductiva/wind-tunnel/blob/deab68a018
 | ![Input Mesh](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/streamlines_mesh.png)  |
 
 ### metadata.obj
-This file contains metadata information about the simulation. 
-It consists of input parameters like `wind_speed`, `rotate_angle`, `num_iterations` and `resolution`.
-It also has output parameters like `drag_coefficient`, `moment_coefficient`, `lift_coefficient`, `front_lift_coefficient`, `rear_lift_coefficient`
- and the location of the output meshes: 
+This file contains metadata related to the simulation, including input parameters such as `wind_speed`, `rotate_angle`, `num_iterations`, and `resolution`. Additionally, it includes output parameters like `drag_coefficient`, `moment_coefficient`, `lift_coefficient`, `front_lift_coefficient`, and `rear_lift_coefficient`. The file also specifies the locations of the generated output meshes.
  
   ```json
     {