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case_id
int64
0
1.43k
mach
float64
0.4
1.2
reynolds
float64
1.07M
100M
temperature
float64
220
310
cl_target
float64
0.5
1.5
area_ratio_min
float64
0.75
1
area_initial
float64
0.03
0.22
cd
float64
0.01
0.64
cl
float64
0.35
1.5
cl_con_violation
float64
-0.45
0
area_ratio
float64
0.75
1.14
initial_design
dict
optimal_design
dict
6
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277.230633
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{ "angle_of_attack": 5.994855173257575, "coords": [ [ 0.979999, 0.000875835 ], [ 0.98, 0.0013834 ], [ 0.98, 0.00155248 ], [ 0.979831, 0.00156557 ], [ 0.979322, 0.00160489 ], [ 0.978474, 0.00167047 ...
{ "angle_of_attack": 3.032473744109874, "coords": [ [ 0.979999, 0.001059372 ], [ 0.98, 0.001135914 ], [ 0.98, 0.001161507 ], [ 0.979831, 0.001211529 ], [ 0.979322, 0.001362075 ], [ 0.978474, 0.0016125...
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{ "angle_of_attack": 1.7215484664137763, "coords": [ [ 0.98, 0.001696702 ], [ 0.98, 0.001774648 ], [ 0.98, 0.00180057 ], [ 0.979831, 0.001833123 ], [ 0.979323, 0.001930875 ], [ 0.978476, 0.002093576 ...
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{ "angle_of_attack": 8.746662754310881, "coords": [ [ 0.98, 0.00162219 ], [ 0.98, 0.001744878 ], [ 0.98, 0.001785672 ], [ 0.979831, 0.001848118 ], [ 0.979322, 0.002035888 ], [ 0.978474, 0.002347814 ...
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{ "angle_of_attack": 2.5972932461503433, "coords": [ [ 0.98, 0.00175976 ], [ 0.98, 0.00192892 ], [ 0.979831, 0.00194444 ], [ 0.979322, 0.00199104 ], [ 0.978474, 0.00206885 ], [ 0.977285, 0.00217809 ...
{ "angle_of_attack": 3.6358714405650643, "coords": [ [ 0.98, 0.001940752 ], [ 0.98, 0.001977975 ], [ 0.979831, 0.002004941 ], [ 0.979322, 0.002085979 ], [ 0.978474, 0.002220532 ], [ 0.977285, 0.002408...
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{ "angle_of_attack": 3.0627058493432164, "coords": [ [ 0.98, 0.001114382 ], [ 0.98, 0.001234138 ], [ 0.98, 0.001274025 ], [ 0.979831, 0.001300869 ], [ 0.979323, 0.001381388 ], [ 0.978475, 0.001515251 ...
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{ "angle_of_attack": 3.632617481621992, "coords": [ [ 0.98, 0.001976878 ], [ 0.98, 0.002057612 ], [ 0.98, 0.002084445 ], [ 0.97983, 0.002129888 ], [ 0.979321, 0.002265846 ], [ 0.978471, 0.002492459 ...
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{ "angle_of_attack": 1.8051064267636725, "coords": [ [ 0.979999, 0.003180226 ], [ 0.98, 0.003479243 ], [ 0.98, 0.003659117 ], [ 0.98, 0.003718799 ], [ 0.979833, 0.003748356 ], [ 0.97933, 0.003837005 ...
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{ "angle_of_attack": 2.177823718378372, "coords": [ [ 0.979999, 0.001247318 ], [ 0.98, 0.001374871 ], [ 0.98, 0.001451638 ], [ 0.98, 0.001477123 ], [ 0.97983, 0.001519097 ], [ 0.979321, 0.001644692 ...
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{ "angle_of_attack": 6.48743549429033, "coords": [ [ 0.979999, 0.00138802 ], [ 0.98, 0.001541463 ], [ 0.98, 0.001633846 ], [ 0.98, 0.001664512 ], [ 0.979831, 0.001718901 ], [ 0.979322, 0.001882422 ...
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{ "angle_of_attack": 4.600147923623633, "coords": [ [ 0.98, 0.002184978 ], [ 0.98, 0.003681018 ], [ 0.98, 0.004179345 ], [ 0.979831, 0.00429467 ], [ 0.979323, 0.004639867 ], [ 0.978475, 0.00521188 ...
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{ "angle_of_attack": 6.806678578304032, "coords": [ [ 0.98, 0.001828445 ], [ 0.98, 0.00197684 ], [ 0.98, 0.002026275 ], [ 0.979831, 0.002074156 ], [ 0.979322, 0.002218071 ], [ 0.978474, 0.002457088 ...
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{ "angle_of_attack": 1.0493184345684716, "coords": [ [ 0.98, 0.000307183 ], [ 0.98, 0.00181318 ], [ 0.98, 0.002313887 ], [ 0.97983, 0.002358399 ], [ 0.97932, 0.002491815 ], [ 0.978468, 0.002713914 ...
14
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{ "angle_of_attack": 5.291083352276971, "coords": [ [ 0.98, 0.00184703 ], [ 0.98, 0.00201618 ], [ 0.979831, 0.00203267 ], [ 0.979322, 0.00208221 ], [ 0.978474, 0.00216491 ], [ 0.977285, 0.00228099 ...
{ "angle_of_attack": 1.7137362172831472, "coords": [ [ 0.98, 0.001500903 ], [ 0.98, 0.001525849 ], [ 0.979831, 0.001545687 ], [ 0.979322, 0.001605377 ], [ 0.978474, 0.001704718 ], [ 0.977285, 0.001843...
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{ "angle_of_attack": 3.855454339817867, "coords": [ [ 0.979999, 0.00109081 ], [ 0.98, 0.00159893 ], [ 0.98, 0.00176818 ], [ 0.97983, 0.00177791 ], [ 0.979319, 0.00180739 ], [ 0.978466, 0.00185739 ...
{ "angle_of_attack": 1.9692037050965612, "coords": [ [ 0.979999, 0.000762769 ], [ 0.98, 0.002265412 ], [ 0.98, 0.002765732 ], [ 0.97983, 0.002692851 ], [ 0.979319, 0.002475044 ], [ 0.978466, 0.0021157...
25
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{ "angle_of_attack": 1.8765332980436344, "coords": [ [ 0.98, 0.001142908 ], [ 0.98, 0.001214909 ], [ 0.98, 0.001238879 ], [ 0.97983, 0.001261035 ], [ 0.97932, 0.001327523 ], [ 0.978469, 0.001438517 ...
24
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{ "angle_of_attack": 3.706466236172554, "coords": [ [ 0.98, 0.001539511 ], [ 0.98, 0.002498501 ], [ 0.98, 0.002817029 ], [ 0.97983, 0.002853514 ], [ 0.97932, 0.002963033 ], [ 0.978468, 0.003145844 ...
28
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{ "angle_of_attack": 2.067574841607665, "coords": [ [ 0.98, 0.000931409 ], [ 0.98, 0.001007502 ], [ 0.98, 0.00103285 ], [ 0.97983, 0.001054995 ], [ 0.97932, 0.001121412 ], [ 0.978469, 0.001232159 ...
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OptiWing Airfoil 2D

Initial and optimized airfoil designs with the flow conditions, objectives, and constraint violations — EngiBench airfoil_v0-style.

This is the 2D OptiWing aerodynamic shape-optimization dataset, formatted to match the EngiBench airfoil convention (one row per optimization case). The three OptiWing-2D datasets share case_id, so a row in one joins to the same case in the others. All three are built from a single raw pass, so their geometry and fields are identical where they overlap.

Geometry is the native CFD surface contour at physical chord (c ≈ 0.98): coords are (x, y) at the solver's surface nodes (~200 per section, ordered TE → upper → LE → lower → TE), and every surface field is the unmodified solver value at those same nodes — no resampling, no normalization. The contour re-meshes to reproduce the simulation, and each field value corresponds exactly to its coordinate. For a one-to-one comparison with the 192-point 3D dataset, resample onto a common grid. The 2D area is the analogue of the 3D paper's volume constraint; area_ratio is the achieved area / initial area.

Splits

split examples
train 878
val 49
test 107

Splits are at the case level and shared with the OptiWing 3D companion data, so paired 2D/3D cases land in the same split.

Features

field type description
case_id int optimization case id (shared across all three datasets)
mach float freestream Mach number (sampled input condition)
reynolds float Reynolds number (sampled input condition)
temperature float freestream static temperature [K] (sampled input condition)
cl_target float target lift coefficient (input condition / constraint)
area_ratio_min float minimum allowable area ratio (input constraint)
area_initial float chord-normalized area of the initial section (shoelace)
cd float optimized drag coefficient (objective)
cl float optimized lift coefficient (≈ cl_target)
cl_con_violation float lift-constraint violation at the optimum
area_ratio float achieved area / initial area at the optimum
initial_design struct {angle_of_attack, coords: [[x, y], …] (~200×2, physical chord ≈0.98)}
optimal_design struct {angle_of_attack, coords: [[x, y], …] (~200×2, physical chord ≈0.98)}

Loading

from datasets import load_dataset
ds = load_dataset("Cashen/optiwing-airfoil-2d-v1")
print(ds["train"][0].keys())

Citation

This dataset does not yet have a companion paper or DOI. Until one exists, please cite the dataset directly by its Hugging Face URL and cite the prior-version works in "Built upon".

@misc{optiwing_airfoil_2d_2026_basic,
  title        = {OptiWing Airfoil 2D (basic): a 2D aerodynamic shape-optimization dataset},
  author       = {Cashen Diniz and Mark Fuge},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/Cashen/optiwing-airfoil-2d-v1},
  note         = {Version v0}
}

Built upon

This is a new dataset (wider parameter bounds, full surface fields, and complete optimization trajectories), but its lineage and format derive from prior work — please cite these as well:

@misc{diniz2025optiwing3d,
  title         = {OptiWing3D: A Diverse Dataset of Optimized Wing Designs},
  author        = {Diniz, Cashen and Fuge, Mark D.},
  year          = {2025},
  eprint        = {2512.12867},
  archivePrefix = {arXiv},
  doi           = {10.48550/arXiv.2512.12867},
  url           = {https://arxiv.org/abs/2512.12867}
}

@inproceedings{felten2025engibench,
  title     = {EngiBench: A Framework for Data-Driven Engineering Design Research},
  author    = {Felten, Florian and Apaza, Gabriel and Br\"{a}unlich, Gerhard and
               Diniz, Cashen and Dong, Xuliang and Drake, Arthur and Habibi, Milad and
               Hoffman, Nathaniel J. and Keeler, Matthew and Massoudi, Soheyl and
               VanGessel, Francis G. and Fuge, Mark},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS),
               Datasets and Benchmarks Track},
  year      = {2025},
  eprint    = {2508.00831},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2508.00831}
}

The EngiBench airfoil format this dataset mirrors is airfoil_v0: https://huggingface.co/datasets/IDEALLab/airfoil_v0.

License

cc-by-nc-sa-4.0

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Papers for Cashen/optiwing-airfoil-2d-v1