Update README.md

README.md

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 license: cc-by-4.0
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-Data is in the process of being uploaded - please do not try to use the data until uploads are fully complete. Gated access for early testing. Paper will be published March 2026 with full details. Write to nashton@nvidia.com for any questions.
+# HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
+
+**Contact:**
+Neil Ashton (nashton@nvidia.com)
+
+## Summary
+This dataset provides the first-ever open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. The dataset is composed of 1,800 samples, arising from 180 geometry variants of the NASA Common Research Model (CRM-HL) across 10 angles of attack (from 4° to 22° in 2° increments). 
+
+One of the key novelties of this dataset is the use of a GPU-accelerated high-fidelity explicit, wall-modeled LES (WMLES) approach on solution-adapted grids containing between 300M and 500M cells. By making this data publicly available, the aim is to accelerate the research and development of AI surrogate modeling within the aerospace industry by addressing the lack of high-fidelity, scale-resolving CFD training data for complex 3D airframes in the high-lift regime.
+
+## CFD Solver
+The simulations were performed using the **"Fidelity Charles"** flow-solver, an explicit, unstructured, finite-volume solver for the compressible Navier-Stokes equations. 
+* **Accuracy:** 2nd-order accurate in space and 3rd-order accurate in time. 
+* **Meshing:** The domain was discretized using "Fidelity Stitch", an unstructured mesh generator based on Voronoi diagrams. 
+* **Turbulence Modeling:** To capture complex, unsteady flow phenomena such as massive flow separation, the Dynamic Smagorinsky subgrid-scale model and an equilibrium wall model were employed.
+
+## How to Cite This Dataset
+In order to cite the use of this dataset, please cite the corresponding preprint paper:
+
+```bibtex
+@article{ashton2026hiliftaeroml,
+  title={{HiLiftAeroML - High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics}},
+  year={2026},
+  author={Ashton, Neil and Clark, Adam and Heidt, Liam and Ivey, Christopher and Bose, Sanjeeb and Agrawal, Rahul and Goc, Konrad and Ranade, Rishi and Adams, Corey and Sharpe, Peter and Nidhan, Sheel}
+}
+```
+
+## Dataset Structure and Files
+Each folder (e.g., `geo_LHCi_AoA_j` where `i` is the geometry ID from 001 to 180 and `j` is the angle of attack) corresponds to a different geometry and angle of attack. Inside each run folder, you will find:
+
+* `boundary_geo_LHCi_AoA_j.vtu.tgz`: Time-averaged flow quantities on the surface boundary (approx. 13 GB compressed, 21 GB unzipped).
+* `volume_geo_LHCi_AoA_j.vtu.tgz`: Time-averaged flow quantities within the domain volume (approx. 23 GB compressed, 86 GB unzipped).
+* `geo_LHCi_AoA_j.stl`: Surface mesh (tris) of the geometry (approx. 197 MB).
+* `geo_LHCi_AoA_j.stp`: Surface geometry definition (approx. 48 MB).
+* `force_mom_geo_LHCi_AoA_j.csv`: Time-averaged drag, lift, moment, and pressure/viscous coefficients.
+* `geo_values_geo_LHCi_AoA_j.csv`: Reference geometry values used to define the geometry via the DoE method.
+* `ref_values_geo_LHCi_AoA_j.csv`: Reference values such as area, Q, and AoA.
+* `img_wss_LHCi_AoA_j.png`: Visual rendering of the wall shear stress/skin-friction on the aircraft surface.
+* `plot_CD_geo_LHCi_AoA_j.png`: Convergence plot of the Drag Coefficient over time.
+* `plot_CL_geo_LHCi_AoA_j.png`: Convergence plot of the Lift Coefficient over time.
+* `plot_CM_geo_LHCi_AoA_j.png`: Convergence plot of the Pitching Moment Coefficient over time.
+
+In addition to the files per run folder, there are also general summary files in the root directory:
+* `geo_parameters_all.csv`: Reference geometry values used to define the geometry via the DoE method for all runs.
+* `force_mom_all.csv`: Time-averaged drag, lift, moment, and pressure/viscous coefficients for all runs.
+
+## How to Download
+The dataset is openly accessible on Hugging Face without any additional costs. 
+
+**Example 1: Download all files (≈ 63 TB compressed, 190 TB unzipped)**
+Please note you will need to have `git lfs` installed first. Then, run the following command:
+
+```bash
+git clone git@hf.co:datasets/nvidia/hiliftaeroml
+```
+
+**Example 2: Download select files via Bash Script**
+Create the following bash script to systematically download specific components (e.g., volume, boundary, force and moments) without pulling the entire repository at once:
+
+```bash
+#!/bin/bash
+# Set the Hugging Face base URL
+HF_BASE="[https://huggingface.co/datasets/nvidia/HiLiftAeroML/resolve/main](https://huggingface.co/datasets/nvidia/HiLiftAeroML/resolve/main)"
+
+# Set the local directory to download the files
+LOCAL_DIR="./hiliftaeroml_data"
+
+# Create the local directory if it doesn't exist
+mkdir -p "$LOCAL_DIR"
+
+# Loop through the Geometry IDs from 1 to 180
+for i in {1..180}; do
+  # Format the Geometry ID with 3-digit zero-padding (e.g., 1 -> 001)
+  XX=$(printf "%03d" $i)
+  
+  # Loop through AoA from 4 to 22 in steps of 2
+  for YY in $(seq 4 2 22); do
+    # Construct the unique identifier string
+    ID="geo_LHC${XX}_AoA_${YY}"
+    
+    # Define the local folder for this specific run
+    RUN_LOCAL_DIR="$LOCAL_DIR/$ID"
+    echo "Processing: $ID"
+    mkdir -p "$RUN_LOCAL_DIR"
+    
+    # List of files to download for this specific run
+    FILES=(
+      "boundary_${ID}.vtu.tgz"
+      "${ID}.stl"
+      "${ID}.stp"
+      "force_mom_${ID}.csv"
+      "geo_values_${ID}.csv"
+      "ref_values_${ID}.csv"
+      "volume_${ID}.vtu.tgz"
+    )
+    
+    # Loop through the files and download them
+    for FILE in "${FILES[@]}"; do
+      # Construct the full URL
+      FILE_URL="${HF_BASE}/${ID}/${FILE}"
+      
+      # Download using wget (-nc skips existing files)
+      wget -q --show-progress -nc "$FILE_URL" -O "$RUN_LOCAL_DIR/$FILE"
+    done
+  done
+done
+```
+
+## Credits
+The dataset was created through a collaboration between **NVIDIA**, **Cadence Design Systems**, and **The Boeing Company**. 
+
+Computing resources were generously provided by:
+* Cadence
+* NVIDIA
+* Texas Advanced Computing Center (TACC) at The University of Texas at Austin
+* Swiss National Supercomputing Centre (CSCS)
+* Oak Ridge Leadership Computing Facility (ORNL)
+
+## License
+This dataset is provided under the permissive open-source **CC-BY-4.0** license. Users are free to share and adapt the material for any purpose, even commercially, provided appropriate credit is given to the original authors.