Impeller Fluid Flow Simulator and Optimizer

A comprehensive tool for simulating, optimizing, and visualizing fluid flow through parameterized impellers. This project combines advanced fluid dynamics simulation with visualization tools to help engineers and researchers optimize impeller designs.

Features

• Parameterized Impeller Design

  • Configurable blade count, angles, and dimensions
  • Real-time geometry updates
  • Automated parameter optimization

• Advanced Simulation

  • Fluid flow simulation using simplified Navier-Stokes equations
  • Performance metrics tracking and analysis
  • Convergence monitoring

• Rich Visualization Suite

  • Performance maps with efficiency contours
  • Blade loading analysis
  • Flow field visualization
  • Optimization progress tracking

Installation

1. Clone the repository:

git clone https://github.com/johnbenac/impeller.git
cd impeller

2. Create and activate a virtual environment (recommended):

# Windows
python -m venv venv
.\venv\Scripts\activate

# Linux/Mac
python -m venv venv
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

Usage

Running Simulations and Generating Visualizations

1. Configure simulation parameters in config.yaml
2. Run the automated optimization and visualization:

python auto_runner.py

This will:

• Run all configured test cases
• Optimize impeller parameters
• Automatically generate comprehensive performance visualizations
• Save all results and visualizations in the results directory

Note: There's no need to run the visualization separately as it's fully integrated into the auto_runner.

Visualization Suite

1. Performance Map

• Dimensionless performance characteristics (ψ-φ diagram)
• Efficiency contours showing optimal operating regions
• Operating points with efficiency values
• Best Efficiency Point (BEP) marker

2. Blade Loading Analysis

• Pressure distribution along blade surfaces
• Velocity profiles at key meridional positions
• Comparison between pressure and suction sides
• Flow separation and loading indicators

3. Optimization Progress

• Evolution of design parameters during optimization
• Convergence of objective function
• Efficiency improvements over iterations
• Parameter sensitivity analysis

4. Interactive Analysis Dashboard

The interactive dashboard (results/interactive_analysis.html) provides:

• Dynamic performance map exploration
• Real-time parameter correlation analysis
• Blade loading animations
• Velocity triangle visualizations

Performance Metrics

Key Indicators

• Maximum Efficiency: 84.4%
• Operating Range: 0.05-0.15 m³/s
• Optimal Flow Coefficient: 0.1-0.12

Operating Recommendations

1. Best Efficiency Point:

   • Flow Coefficient (φ): 0.11
   • Head Coefficient (ψ): 0.32
   • Efficiency: 84.4%

2. Operating Ranges:

   • Normal Operation: φ = 0.08-0.14
   • Extended Operation: φ = 0.05-0.15
   • Avoid: φ > 0.15 (high flow instability)

3. Design Parameters at BEP:

   • Number of Blades: 4
   • Inlet Angle: 10.0°
   • Outlet Angle: 38.4°
   • Rotational Speed: 3000 RPM

Project Structure

├── auto_runner.py         # Automated simulation runner
├── config.yaml           # Configuration parameters
├── impeller_sim.py       # Core simulation logic
├── plot_results.py       # Visualization generator
├── requirements.txt      # Project dependencies
├── test_impeller.py      # Test suite
└── results/              # Generated visualizations and data

Contributing

Contributions are welcome! Please read our contributing guidelines and code of conduct before submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this tool in your research, please cite:

@software{impeller_simulator,
  author = {Benac, John},
  title = {Impeller Fluid Flow Simulator and Optimizer},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/johnbenac/impeller}
}

Contact

For questions, issues, or contributions, please:

• Open an issue on GitHub
• Submit a pull request
• Contact the maintainer at GitHub