Pipeline Sizing: Engineering Procedure

This repository contains a Python implementation of a pipeline sizing algorithm based on engineering principles. The script leverages fundamental fluid mechanics equations to determine the optimal pipe diameter and ensures compliance with velocity and pressure drop constraints.

Features

• Accurate Calculations: Implements Continuity Equation, Reynolds Number, and Darcy-Weisbach equations.
• Iterative Design: Adjusts pipe diameter iteratively to meet design criteria.
• Flexibility: Suitable for various industries requiring pipeline design.

Inputs

The user provides the following inputs:

• Mass Flow Rate: Fluid mass flow rate (kg/s).
• Density: Fluid density (kg/m^3).
• Viscosity: Fluid dynamic viscosity (Pa.s).
• Pipe Length: Length of the pipeline (m).
• Maximum Allowable Pressure Drop: Maximum pressure drop across the pipeline (Pa).
• Maximum Allowable Velocity: Maximum velocity of the fluid (m/s).

Outputs

The script returns:

• Optimal pipe diameter (m).
• Fluid velocity (m/s).
• Reynolds number.
• Darcy-Weisbach friction factor.
• Pressure drop (Pa).

Example Usage

from pipeline_sizing import pipeline_sizing

# Example input parameters
result = pipeline_sizing(
    mass_flow_rate=2.0,  # kg/s
    density=1000.0,      # kg/m^3
    viscosity=0.001,     # Pa.s
    pipe_length=50.0,    # m
    max_pressure_drop=50000.0,  # Pa
    max_velocity=3.0     # m/s
)

# Display results
for key, value in result.items():
    print(f"{key}: {value}")

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/pipeline-sizing.git

2. Navigate to the directory:

   cd pipeline-sizing

3. Run the Python script:

   python pipeline_sizing.py

Contributions

Contributions are welcome! Feel free to fork the repository and submit pull requests.

License

This project is licensed under the MIT License. See the LICENSE file for more details.