Elastic Doublet Fusion Using SPH

This is a Python project for simulating an elastic doublet fusion using the Smoothed Particle Hydrodynamics (SPH) method. The code is part of the implementation of the "Physically-based simulation of elastic-plastic fusion of 3D bioprinted spheroids" article by Bahrami et al. (2023) published in the "Biofabrication" journal. The full article can be found at the link below:

https://iopscience.iop.org/article/10.1088/1758-5090/acf2cb/meta

Table of Contents

• Code Description
  • Particles.py
  • Physics.py
  • Initialization.py
  • glWidget.py
• How to Use
• Dependencies
• Installation
• Example Usage
• Simulation Preview
• License

Code Description

The project has four main Python files:

• Particles.py
• Physics.py
• Initialization.py
• glWidget.py

Particles.py

This file includes a class for particles used in the SPH method. Each particle has multiple attributes, such as mass, density, etc.

Physics.py

This file contains the main computation of the SPH methods. The main class is called "Environment", which refers to the physical environment of the particles and includes various functions for computing the physical quantities of the particles, such as density, pressure, momentum matrix, elastic force, etc.

Initialization.py

This file initializes two spheres (doublet), samples particles inside them, and computes the initial values of some particle attributes such as SPH kernel radius, particle mass, initial densities, volumes, etc.

glWidget.py

This file contains the class and related functions for visualizing the elastic fusion of the doublet. For visualization, we used OpenGL contents in the "pyQTGraph" package. This allows us to use QT GUI in our project. The GUI includes parameters such as Young's modulus, Poisson Ratio, volume constant, density constant, elastic limit, and plastic limit. Changing these parameters will change the behavior of the doublet in real time.

How to Use

Run glWidget.py and then click Run!

Dependencies

All mathematical computations are done using "numpy". We also used KDTree from the "scipy" package to find the nearest neighbor of each particle. Visualization depends on "OpenGL" and "pyQTGraph". The required packages for this project are:

• numpy
• scipy
• pyQTGraph
• pyOpenGL

Installation

To install the required dependencies, run:

pip install numpy scipy pyqtgraph pyopengl

Example Usage

1- Clone the repository

git clone https://github.com/yourusername/ElasticDoubletFusion.git

2- Navigate to the project directory:

cd ElasticDoubletFusion

3- Run the simulation

python glWidget.py

Simulation preview

Here is a preview of the simulation:

License

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