SHIFT-CCA: High-Fidelity Computational Fluid Dynamics Dataset for Collaborative Combat Aircraft Aerodynamics

We're excited to introduce the SHIFT-CCA dataset — a high-fidelity aerodynamic simulation dataset developed as part of the Luminary SHIFT Models initiative. This dataset enables the training and benchmarking of real-time physics AI models for Group 5 UAV-class configurations representative of Collaborative Combat Aircraft (CCA) concepts.

Website: www.luminarycloud.com/models

Contact: shift@luminarycloud.com

Summary

Collaborative Combat Aircraft programs are being asked to converge on credible designs under extreme uncertainty, compressed timelines, and unprecedented production scale. Early configuration decisions are often made with limited visibility into sensitivities, second-order effects, and interactions between design variables. SHIFT-CCA addresses this gap by providing a systematically generated dataset for early-stage design exploration and trade studies.

SHIFT-CCA is purpose-built for rapid screening and sensitivity analysis across thousands of geometric variants. Developed in collaboration with nTop, the dataset enables teams to narrow the design space with greater context before committing to resource-intensive simulation campaigns.

This dataset supports training surface-based or volume-based aerodynamic surrogate models, real-time inference systems, and exploring shape-performance correlations for defense aerospace design.

Applications

• Rapid aerodynamic prototyping and shape optimization for CCA-class vehicles
• Research in aero-inference, point cloud learning, or physics-aware generative models
• Training and fine-tuning Physics AI models for defense applications

Attribution

Please attribute nTop for geometry parameterization, and Luminary Cloud for the SHIFT-CCA dataset.

An article is being prepared so users can cite this dataset - we will update this accordingly when available. Until then you can use this citation:

@misc{shift_cca_2026,
  author = "{Luminary Cloud}",
  title = {SHIFT-CCA: High-Fidelity Computational Fluid Dynamics Dataset for Collaborative Combat Aircraft Aerodynamics},
  year = {2026},
  url = {https://huggingface.co/datasets/luminary-shift/CCA}
}

Contents

This repository contains the SHIFT-CCA dataset. We will continue to push newly computed samples to this repository periodically. The data generation and organization within the repository is described below.

Geometry Variation

The dataset is derived from a parameterized Group 5 UAV reference configuration. Geometry variation was driven through robust parametric controls in nTop, enabling the generation of thousands of simulation-ready configurations without manual intervention or geometry failures.

nTop: Implicit Geometry Representation

nTop's implicit geometry representation enables "unbreakable" parametric models that can be used to generate many simulation-ready configurations with no failures. This removes a significant failure mode for generating large datasets for Physics AI model training.

The parameterization includes planform design parameters such as root chord, panel break position, leading and trailing edge sweep angles, and wing tip geometry. These parameters allow exploration of configurations relevant to CCA-class vehicle design.

CFD Solver

All cases were run using the Luminary Cloud platform at representative cruise conditions. The cases were simulated using simulation practices honed during our participation in the NASA HLPW and leverage our automated adaptive solution technology: Luminary Mesh Adaptation (LMA). This ensures thin and sharp features are accurately captured by the solver with no user input and across the wide range of flow conditions and geometries.

Files

At the top level of the repository you will see sample folders indexed by sample_xxxxxx, where the indices are a zero-padded six digit integer. Each of these samples contains a set of files describing their geometry and simulation results.

In each directory you will find the following files:

• merged_surfaces.stl: STL file with the CCA geometry from the final adapted mesh
• merged_surfaces.vtp: surface field solution file with pressure and wall shear-stress fields at the final iteration
• merged_volumes.vtu: volume field solution file with pressure, velocity, eddy viscosity, density, and temperature at the final iteration
• forces.json: file containing drag, lift, normalized coefficients, and moments from the final iteration
• params.json: description of geometry parameters and flow parameters defining both the geometry sample and the flow condition
• metadata.json: additional metadata for the sample

Downloading

You can use HuggingFace to gain access to the entire repository, but will require the associated TBs of storage available locally. Note you will need to have git lfs installed first, then run

git clone git@hf.co:datasets/luminary-shift/CCA

If you will access only a subset of the data, or wish to interact in a staged manner, you can clone the repository where the LFS files are not checked out (simply pointers):

GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/luminary-shift/CCA .

# to ensure future `git pull` commands won't checkout full files, you'll want to ensure the skip is active in this repo
cd <path/to/repo>
git lfs install --skip-smudge --local

You can then pull down files you want to interact with in multiple ways:

# pull a specific file
git lfs pull --include="path/to/your/file"

# pull a directory
git lfs pull --include="path/to/file1,path/to/dir/*"

# pull, but exclude certain paths
git lfs pull --exclude="**/*.mp4"

and remove those files and reset them to pointers when done using them:

rm path/to/your/file
git checkout -- path/to/your/file

Credits

nTop

nTop provided fast, robust parametric geometry variation through their implicit geometry representation, enabling the generation of thousands of simulation-ready configurations.

License

Copyright © 2026 Luminary Cloud. All rights reserved.

This dataset and associated AI models are proprietary to Luminary Cloud.