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CFDBench

Yining Luo, Yingfa Chen, and Zhen Zhang
Tsinghua University
chenyingfa1999@qq.com

flow-examples

This is the code for the paper: CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics.

CFDBench is the first large-scale benchmark for evaluating machine learning methods in fluid dynamics with varied boundary conditions (BCs), physical properties, and domain geometries. It consists of four classic problems in computational fluid dynamics (CFD), with many varying operating parameters, making it perfect for testing the inference-time generalization ability of surrogate models. Such generalizability is essential for avoiding expensive re-training when applying surrogate models to new problems.

Quick Links

Data

Main download links:

Interpolated data (~13.4GB):

Raw data (~460GB):

You likely do not need the raw data if you are just trying to evaluate and/or train neural networks on our data.

Data Generation

The directory generation-code contains the code for creating the mesh (ICEM code) and the schema code for batch generation in ANSYS Fluent.

This part takes a lot of time, and you are better off using our generated data instead.

Data Interpolation

After generating data with numerical algorithms, it is then interpolated to a grid of 64x64. The raw data before interpolation is very large; the link below is the interpolated data.

Main download link: [click here]

Contains 4 problems:

  • cavity: Lid-driven cavity flow
  • tube: Flow through a circular tube
  • dam: Flow over a dam
  • cylinder: Flow around a cylinder

The cylinder flow is separated into three files because the file size exceeds the upload limit.

Each dataset includes 3 subsets, corresponding to changing BCs, domain geometries, and physical properties.

The directory tree for the datasets:

▼ cavity/
    ▼ bc/
        ▼ case0000/
            ▼ u.npy
            ▼ v.npy
        ► case0001/
    ► geo/
    ► prop/
► tube/
► dam/
► cylinder/

The actual data for each velocity field is stored in u.npy and v.npy.

Models

models

The basic types of models are autoregressive and non-autoregressive:

  • Autoregressive:

    • Auto-FFN
    • Auto-DeepONet
    • Auto-EDeepONet
    • Auto-DeepONetCNN
    • ResNet
    • U-Net
    • FNO

  • Non-autoregressive

    • FFN
    • DeepONet

The implementation of the models is located in src/models

Main Results

Multi-Step Inference

multi-step-results

Autoregressive Models

autoregressive-results

Nonautoregressive Models

nonautoregressive-results

How to Run?

Tested on:

  • PyTorch 1.13.3+cu117
  • Python 3.9.0

Installation

Make sure you have access to CUDA GPU, then setup the environment using

pip install -r requirements

Prepare Data

Move the downloaded data into a data directory next to src directory, such that the directory looks like:

▼ data/
    ▼ cavity/
        ▼ bc/
        ▼ geo/
        ▼ prop/
    ► tube/
    ► dam/
    ► cylinder/
► generation-code/
► src/
.gitignore
README.md

Training

In the src directory, run train.py or train_auto.py to train non-autoregressive or autoregressive models respectively. Specify the model with --model, it must be one of the following:

Model Value for --model Script
Non-autoregrssive FFN ffn train.py
Non-autoregressive DeepONet deeponet train.py
Autoregressive Auto-FFN auto_ffn train_auto.py
Autoregressive Auto-DeepONet auto_deeponet train_auto.py
Autoregressive Auto-EDeepONet auto_edeeponet train_auto.py
Autoregressive Auto-DeepONetCNN auto_deeponet_cnn train_auto.py
Autoregressive ResNet resnet train_auto.py
Autoregressive U-Net unet train_auto.py
Autoregressive FNO fno train_auto.py

Training Examples

For example, run FNO on the cavity flow subset with all cases:

python train_auto.py --model fno --data cavity_prop_bc_geo

or, run DeepONet on the dam flow PROP + GEO subset:

python train.py --model deeponet --data dam_prop_geo

Results will be saved to result/ directory by default, but can be customized with the --output_dir argument.

For more options, such as model hyperparameters, run python train.py -h or python train_auto.py -h.

Inference

Set --mode test when executing train.py or train_auto.py.

Hardware Requirements

See the Results section in the paper. Reduce the batch size if you run out of VRAM.

How to Add New Models/Datasets?

Our code is highly extensible and modular, and it is very easy to add new datasets or models.

Models

To add a new model, simply create a class that inherits one of the following base models:

  • CfdModel: If your model is nonautoregressive
  • AutoCfdModel: If your model is autoregressive

Then depending on which base model, you have to implement just 2 or 3 methods in addition to the model architecture itself.

  • Nonautoregressive: forward, generate_one.
  • Autoregressive: forward, generate_one, and generate_many.

Then, if your model requires new hyperparameters, add them to the argument parsed in args.py.

Finally, you should create add a new elif in for the instantiation of your model. For autoregressive models, change init_model in utils_auto.py as follows.

def init_model(args: Args) -> AutoCfdModel:
    # ...
    elif args.model == "your_model_name":
        model = YourModeClass(
            in_chan=args.in_chan,
            out_chan=args.out_chan,
            n_case_params=n_case_params,
            loss_fn=loss_fn,
            some_arg=args.some_arg,
            # ... more arguments for your model.  (or you can just pass `args`)
        ).cuda()
        return model

For nonautoregressive models, change init_model in train.py in a similar manner.

Dataset

You just have to implement a new subclass for CfdDataset or CfdAutoDataset in dataset/base.py. Like any PyTorch Dataset, it needs to implement __getitem__ and __len__. But for multi-step inference, it also has to load features into a member attribute named all_features which should be a list of Tensor or NumPy arrays.

Then, add a new elif in get_dataset in dataset/__init__.py for the instantiation of your data.

Citation

If you find this code useful, please cite our paper:

@article{CFDBench,
  title={CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics},
  author={Yining, Luo and Yingfa, Chen and Zhen, Zhang},
  url={https://arxiv.org/abs/2310.05963},
  year={2023}
}