Update README.md

README.md

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-SuperWing, a comprehensive benchmark dataset of transonic swept wings comprising 3,213 wing shapes and more than 20,000 flow fields across diverse geometries and
-operating conditions. Unlike previous efforts that rely on perturbations of a baseline wing, SuperWing is generated using a simplified yet expressive parameterization scheme. By incorporating spanwise-
-varying dihedral, twist, and airfoil characteristics, the dataset captures realistic design complexity and ensures greater diversity than existing ones.
-
+SuperWing, a comprehensive benchmark dataset of transonic swept wings comprising 4239 wing shapes and near 30,000 flow fields across diverse geometries and
+operating conditions. Unlike previous efforts that rely on perturbations of a baseline wing, SuperWing is generated using a simplified yet expressive 
+parameterization scheme. By incorporating spanwise-varying dihedral, twist, and airfoil characteristics, the dataset captures realistic design complexity and 
+ensures greater diversity than existing ones. Please refer to our ![arXiv paper]{https://arxiv.org/abs/2512.14397} for more details on the dataset. 
 
 ![{DF9389F4-4D8B-4D16-A1E0-384D6E3F5847}](https://cdn-uploads.huggingface.co/production/uploads/6878e482bd4380c813fd99de/wL4x6q1tbNx1kjrexh5g5.png)
+
+# Features:
+
+1. Focusing on the **"kink" wings** (with two segments instead of one in the spanwise direction), which bring more complex flow features and are closer to the industry.
+2. **More diversity** on the wing shape by generating them from basic parameters instead of perturbing from a baseline wing shape
+3. RANS simulation with **well-validated** solver `ADflow` and structural computational mesh.
+
+# Data format:
+
+1. Geometric parameters
+   `config.dat` includes the basic shape parameters to build a wing from scratch, with the method detailed in our ![arXiv paper]{https://arxiv.org/abs/2512.14397}. For each wing
+   shape, we provide 8 operating conditions, but note that they are not exactly the operating conditions in the final dataset since some of them may not lead to convergent
+   results.
+   
+   |indexs|type|variables|comments|
+   |-|-|-|-|
+   |1-7| global planform parameter | sweep angle, dihedral angle at tip, dihedral angle at kink, aspect ratio, taper ratio, kink adjustment, root adjustment |
+   |8-17| spanwise variation of airfoils | thickness ratios (x3), camber ratios (x3), twist angles (x4) |
+   |18-38| baseline airfoil shape | CSTs (9th order) for upper (x10) and lower (x10) surface, max thickness |
+   |39-56| Mach numbers and angles of attacks |
+   
+2. Index file
+  `index.npy` provides the crucial information for all provided samples; it has a shape of $28856 \times 8$, with each channel listed below:
+
+   |index|description|
+   |-|-|
+   |0|wing shape index (corresponding to in `config.dat`)|
+   |1|operating condition index (count in each wing shape) |
+   |2|angle of attack (in degrees)|
+   |3|mach number|
+   |4|reference area (to calculate coefficient)|
+   |5|half span length|
+   |6|lift coefficient|
+   |7|drag coefficient|
+   |8|pitching moment coefficient|
+  
+3. Reference surface mesh and surface physical quantities on this reference mesh
+   The reference mesh `geom0` contains the cell-centric coordinates of the reference surface mesh with size $256 \times 128 \times 3$, and the three channels stand
+   for $x, y, z$. The surface physical quantities `data.npy` are on the same reference mesh with size $256 \times 128 \times 3$, and the three channels stand for
+   $C_p, C_{f,\tau}, C_{f,z}$.   (the latter two are the decomposed friction coefficients on the streamwise and spanwise directions). These data can serve as input and
+   output for a machine learning model that predicts the aerodynamics of wings. 
+
+   **reference mesh**: The simulation mesh on the wing surface is first interpolated to a reference mesh. In the spanwise ($j$-direction), 128 cross-sectional planes are
+   sampled with even spacing, and tips are excluded. For each cross-section ($i$-direction), a fixed set of normalized chordwise positions $\{(x/c)_i\}$ s is used for both 
+   the upper and lower surfaces, and the tail edge is represented only with one cell. The reference mesh along the wing surface is then unfolded as shown below, resulting 
+   in a final vertex surface grid of $257 \times 129$ points per wing (`origingeom.npy`). This is useful when we need to calculate coefficients from the surface flow outputs.
+   The cell-centric grid for the mesh is obtained just by averaging the coordinates at the four vertices. 
+
+   ![image](https://cdn-uploads.huggingface.co/production/uploads/6878e482bd4380c813fd99de/deWUNg0C2bxl7ZDQFfowu.png)
+
+5. Raw solver output
+   The raw output of surface flow (`surf.cgns`) and 3D volume fields (`vol.cgns`) is also available in their original formats (CGNS files with the ADF format). They need the
+   CGNS library to be read out. Since they are too large, they are available via university storage upon reasonable request to the authors.
+
+The table below summarizes the data.
+
+|Type | File & Description | Size 
+|-|-|-|
+|Geometric parameters | `Configs.dat` | shape parameters  |  5.0 MB
+|Information | `index.npy` | group information, operating conditions, and aerodynamic coefficients |  2.8 MB
+|Surface mesh | `*\wing.xyz` | surface simulation mesh |  7.8 GB 
+|         | `origingeom.npy` | reference surface mesh (grid points) |  3.3 GB 
+|         | `geom0.npy` | reference surface mesh (cell center) |  3.3 GB   
+|Surface flow | `data.npy` | $C_p, \bm {C_f}$ at reference mesh (cell center) |  22.7 GB 
+|        | `*\surf.cgns` | raw surface flow output | 161.5 GB
+|Volume flow | `*\vol.cgns` | raw flow field output |  5.5 TB 
+
+