Deep Learning for Computational Fluid Dynamics (CFD) Acceleration

Project Overview

This advanced project explores cutting-edge applications of deep learning in computational fluid dynamics (CFD). We aim to develop novel neural network architectures to accelerate CFD simulations, improve mesh generation, and enhance our understanding of complex fluid systems.

Table of Contents

• Project Overview
• Getting Started
• Project Structure
• Milestones
• To-Do List
• Tech Stack
• Contributing
• License
• Acknowledgments

Getting Started

Prerequisites

• Python 3.8+
• CUDA-capable GPU (for deep learning training)
• Basic knowledge of fluid dynamics and deep learning

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/deep-learning-cfd.git
   cd deep-learning-cfd
   

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install required packages:

   pip install -r requirements.txt
   

Project Structure

deep-learning-cfd/
│
├── data/
│   ├── cfd_simulations/
│   ├── meshes/
│   └── processed/
│
├── notebooks/
│   ├── 1_pinn_navier_stokes.ipynb
│   ├── 2_cnn_flow_prediction.ipynb
│   ├── 3_gnn_mesh_analysis.ipynb
│   └── 4_gan_mesh_generation.ipynb
│
├── src/
│   ├── models/
│   │   ├── pinn/
│   │   ├── cnn/
│   │   ├── gnn/
│   │   └── gan/
│   ├── data_processing/
│   ├── visualization/
│   └── utils/
│
├── tests/
│
├── README.md
├── requirements.txt
└── .gitignore

Milestones

1. Physics-Informed Neural Networks (PINNs) (Week 1-4)

   • Implement PINN for solving Navier-Stokes equations
   • Validate against traditional numerical solutions

2. Convolutional Neural Networks (CNNs) for Flow Prediction (Week 5-8)

   • Develop CNN architecture for flow field prediction
   • Train and evaluate on various geometries

3. Graph Neural Networks (GNNs) for Mesh-Based Simulations (Week 9-12)

   • Implement GNN for unstructured mesh analysis
   • Develop mesh refinement suggestions based on GNN predictions

4. Generative Adversarial Networks (GANs) for Mesh Generation (Week 13-16)

   • Design and train GAN for automated mesh generation
   • Evaluate mesh quality and CFD simulation results

5. Integration and Comparative Analysis (Week 17-20)

   • Combine developed models into a unified framework
   • Perform comparative analysis with traditional CFD methods

To-Do List

• Set up development environment with GPU support
• Collect and preprocess CFD simulation datasets
• Implement PINN for Navier-Stokes equations
• Develop CNN architecture for flow field prediction
• Create graph representations of CFD meshes
• Implement GNN for mesh-based simulations
• Design and train GAN for mesh generation
• Develop evaluation metrics for generated meshes
• Integrate models into a unified framework
• Perform benchmarking against traditional CFD methods
• Write comprehensive documentation and tutorials
• Prepare scientific paper drafts for each component

Tech Stack

• Python: Primary programming language
• TensorFlow/PyTorch: Deep learning frameworks
• NumPy & SciPy: Numerical computations
• Matplotlib & Plotly: Data visualization
• NetworkX: Graph manipulation for GNNs
• OpenFOAM: Open-source CFD toolbox for validation
• Docker: Containerization for reproducible environments
• Git LFS: Version control for large simulation datasets

Contributing

We welcome contributions from the CFD and machine learning communities. Please read our CONTRIBUTING.md for details on how to submit pull requests.

License

This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details.

Acknowledgments

• OpenFOAM Foundation
• TensorFlow Research Cloud for providing computational resources

Relevant Papers

1. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics, 378, 686-707.

2. Guo, X., Li, W., & Iorio, F. (2016). "Convolutional neural networks for steady flow approximation." In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 481-490).

3. Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., & Battaglia, P. W. (2021). "Learning Mesh-Based Simulation with Graph Networks." In International Conference on Machine Learning (pp. 8612-8622). PMLR.

4. Chen, C. C., & Jaiman, R. K. (2022). "MeshingNet: A New Mesh Generation Method based on Deep Learning." Computer Methods in Applied Mechanics and Engineering, 384, 113951.