SHIFT-Submarine-sample: High-Fidelity Computational Fluid Dynamics Dataset for Submarine Hydrodynamics

We're excited to introduce the SHIFT-Submarine dataset — a high-fidelity hydrodynamic 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 Myring type submarines.

Website: www.luminarycloud.com/models

Contact: shift@luminarycloud.com

Note: this is a sample dataset of the first ~100 samples in the full SHIFT-Submarine. It provides the same information as provided in the full dataset. This should be sufficient to assess pipelines and test whether you want access to the full dataset. The full dataset is available at https://huggingface.co/datasets/luminary-shift/Submarine.

Summary

National security concerns necessitate rapid scoping and design of submarines to shrink the time needed for production and deployment. Early configuration decisions are often susceptible to uncertainties and bias due to the traditionally lengthy process of exploring large design spaces. SHIFT-Submarine addresses this gap by providing a systematically generated dataset for rapid screening and sensitivity analysis across thousands of geometric variants, ultimately aiding early-stage design exploration and trade studies.

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

Applications

• Rapid hydrodynamic prototyping and shape optimization for submarines
• Research in hydro-inference, point cloud learning, or physics-aware generative models
• Training and fine-tuning Physics AI models for defense applications

Attribution

Luminary Cloud for both geometry generation and the SHIFT-Submarine 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_submarine_2026,
  author = "{Luminary Cloud}",
  title = {SHIFT-Submarine: High-Fidelity Computational Fluid Dynamics Dataset for Submarine Hydrodynamics},
  year = {2026},
  url = {https://huggingface.co/datasets/luminary-shift/Submarine}
}

Contents

This repository contains the SHIFT-Submarine 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 Myring-type submarine using Onshape. The dataset varies six geometric parameters, but the parametrized geometry includes over 20 unique parameters.

These geometry parameters include variation in the cross section, forebody, and aft body shapes, sail and back fin positions, and submarine lengh-to-diameter ratio

CFD Solver

All cases were run using the Luminary Cloud platform at representative flow 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 Submarine 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/Submarine

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/Submarine-sample .

# 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

License

Copyright © 2026 Luminary Cloud. All rights reserved.

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