ACCELERATING LATTICE BOLTZMANN METHOD SIMULATION TIME ON PYTHON
The Lattice Boltzmann Method (LBM) is a powerful approach for simulating fluid flow. LBM has gained increasing popularity over time due to its simplicity, efficiency, and its capability to handle complex geometries. This undergraduate thesis focuses on developing a computation prototype using LBM...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Subjects: | |
| Online Access: | https://digilib.itb.ac.id/gdl/view/84762 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The Lattice Boltzmann Method (LBM) is a powerful approach for simulating
fluid flow. LBM has gained increasing popularity over time due to its simplicity,
efficiency, and its capability to handle complex geometries. This undergraduate
thesis focuses on developing a computation prototype using LBM, specifically for
simulating 1D transient heat diffusion, 2D Poiseuille flow, and 2D flow over a
circular cylinder. The primary goal of this project is to develop and validate the
LBM solver, with a special emphasis on using Python for its implementation.
Python was chosen as programming for developing the LBM solver because
of its ease of development, its ability to create prototypes quickly, and its userfriendly
syntax. Although Python has a slower execution speed compared to other
programming languages, it supports various acceleration methods, such as array
vectorization and Just-In-Time (JIT) compilation, making it an excellent choice
for rapid prototyping of an LBM solver.
This study shows that, although using loops in Python may not be efficient,
JIT compilation can significantly improve the performance of the LBM program,
speeding up its execution time by hundreds to thousands of times. This thesis
highlights Python’s potential for creating efficient LBM prototypes and emphasizes
the importance of selecting the right approach to accelerate the solver based
on computation demands. |
|---|