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AlanZee/pyOpenFOAM-reference-data

pyOpenFOAM Reference Data & Validation Results OpenFOAM-13 reference simulation data and pyOpenFOAM validation results for pyOpenFOAM — a pure Python/PyTorch reimplementation of OpenFOAM with GPU acceleration and automatic differentiation. Dataset Summary / 数据摘要 Property Value Total reference cases 257 Validated cases 233 (90.7%) Categories 21 Source OpenFOAM v11/v13 Reference data size 2.42 GB pyOpenFOAM results 3.3 MB Field files analyzed… See the full description on the dataset page: https://huggingface.co/datasets/AlanZee/pyOpenFOAM-reference-data.

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pyOpenFOAM Reference Data & Validation Results

OpenFOAM-13 reference simulation data and pyOpenFOAM validation results for pyOpenFOAM — a pure Python/PyTorch reimplementation of OpenFOAM with GPU acceleration and automatic differentiation.


Dataset Summary / 数据摘要

PropertyValue
Total reference cases257
Validated cases233 (90.7%)
Categories21
SourceOpenFOAM v11/v13
Reference data size2.42 GB
pyOpenFOAM results3.3 MB
Field files analyzed2,032
Unique field types376
Validation figures11

Contents / 内容

FileSizeDescription
openfoam-reference-data.tar.gz2.42 GB257 OpenFOAM reference cases (96% of v13 tutorials)
pyopenfoam-simulation-results.tar.gz47 KBpyOpenFOAM validation results (34 JSON files)
validation_report.md19 KBComprehensive validation report with academic citations
figures/3.3 MB11 validation figures (300 DPI)
per_case_data/1.1 MBPer-case analysis for all 257 reference cases
Docker image622 MBOpenFOAM-13 compiled environment (122 libs, 9 binaries)

Validation Results / 验证结果

MetricResult
Unit tests (CPU)17,130 / 17,130 pass
Unit tests (GPU)17,082 / 17,085 pass
Differentiable CFD42 / 42 pass
Solver E2E verification69 / 69 pass
Couette flow error0.001%
Poiseuille flow error0.02%
Cavity Re=100 (32×32)1.0% vs Ghia et al. (1982)

Quick Start / 快速开始

python
from huggingface_hub import hf_hub_download
import tarfile

# Download reference data
path = hf_hub_download(
    repo_id="AlanZee/pyOpenFOAM-reference-data",
    filename="openfoam-reference-data.tar.gz",
    repo_type="dataset"
)
with tarfile.open(path, "r:gz") as tar:
    tar.extractall("validation/reference/openfoam/")

Citation / 引用

bibtex
@software{pyopenfoam2026,
  author = {AlanZee},
  title = {pyOpenFOAM: Pure Python/PyTorch CFD with GPU Acceleration},
  year = {2026},
  url = {https://github.com/AlanZee/pyOpenFOAM}
}

References / 参考文献

  1. 1.Ghia, K.N., Ghia, U., Shin, C.T. (1982). "High-Re solutions for incompressible flow." J. Comput. Phys., 48, 387-411.
  2. 2.Weller, H.G. et al. (1998). "A tensorial approach to computational continuum mechanics." Computers in Physics, 12(6), 620-631.
  3. 3.OpenFOAM Foundation (2025). OpenFOAM-13. https://openfoam.org/

数据集说明 (中文)

本数据集包含 pyOpenFOAM 项目的所有验证数据:

  • 参照数据: 257 个 OpenFOAM-13 官方教程的仿真结果,覆盖 21 个求解器类别
  • 验证结果: pyOpenFOAM 在所有参照算例上的运行结果和精度对比
  • 分析报告: 符合学术规范的全量验证报告,含 11 张数据可视化图表
  • Docker 镜像: 预编译的 OpenFOAM-13 环境 (Ubuntu 22.04, GCC 10)

验证覆盖率

类别算例数已验证覆盖率
不可压缩稳态554785.5%
不可压缩 VoF393384.6%
多相 Euler2626100%
通用流体312993.5%
多组分反应191894.7%
多区域 CHT201890.0%
可压缩 VoF8787.5%
可压缩激波88100%
其他 (8 类)312993.5%
总计25723390.7%