A 3D sparse LBM solver implemented using Taichi

Overview

taichi_LBM3D

Background

Taichi_LBM3D is a 3D lattice Boltzmann solver with Multi-Relaxation-Time collision scheme and sparse storage structure implemented using Taichi programming language, which is designed for porous medium flow simulation. Taking advantage of Taichi's computing structure, Taichi_LBM3D can be employed on shared-memory multi-core CPUs or massively parallel GPUs (OpenGL and CUDA). The code is around 400 lines, extensible and intuitive to understand.

Installation

This solver is developed using Taichi programming language (a python embedded programming language), install Taichi is required, by python3 -m pip install taichi.

Pyevtk is required for export simualtion result for visualization in Paraview, install Pyevtk by pip install pyevtk

Usage

There are several place for users to modify to fit their problems:

set computing backend

First the computing backend should be specified by ti.init(arch=ti.cpu) using parallel CPU backend, or by ti.init(arch=ti.gpu) to use OpenGL or CUDA(is available) as computing backend

set input geometry

LBM uses uniform mesh, the geometry is import as a ASCII file with 0 and 1, where 0 represent fluid point and 1 represent solid point. They are stored in format:

for k in range(nz)
  for j in range(ny)
    for i in range(nx)
      geometry[i,j,k]

You can specify the input file at: solid_np = init_geo('./img_ftb131.txt')

For two phase solver, a two phase distribution input file is also requred. This file is composed of -1 and 1 representing phase 1 and 2 respectively

set geometry size

Set geometry input file size here: nx,ny,nz = 131,131,131

set external force

Set expernal force applied on the fluid here: fx,fy,fz = 0.0e-6,0.0,0.0

set boundary conditions

There are three boundary conditions used in this code: Periodic boundary condition, fix pressure boundary condition, and fix velocity boundary condition We use the left side of X direction as an example: bc_x_left, rho_bcxl, vx_bcxl, vy_bcxl, vz_bcxl = 1, 1.0, 0.0e-5, 0.0, 0.0 set boundary condition type in bc_x_left; 0=periodic boundary condition, 1 = fix pressure boundary condition, 2 = fix velocity boundary condition if bc_x_left == 1 is select, then the desired pressure on the left side of X direction need to be given in rho_bcxl if bc_x_left == 2 is select, then the desired velocity on the left side of X direction need to be given in vx_bcxl, vy_bcxl, vz_bcxl

The same rules applied to the other five sides

set viscosity

Viscosity is set in niu = 0.1 for single phase solver

niu_l = 0.05
niu_g = 0.2

for two phase solver, niu_l for liquid phase, niu_g for phase 2

Additional parameters for two phase solver
  • Contact angle of the solid surface can be specified in psi_solid = 0.7 this value is the cosine of the desired contact angle, so the value is between -1 and 1
  • Interfical tension of two phases is set in CapA = 0.005
  • Boundary condition for the phase setting: bc_psi_x_left, psi_x_left = 1, -1.0 bc_psi_x_left = 0 for periodic boundary for the phase field, 1 = constant phase field value boundary. If bc_psi_x_left is set as 1, then the next parameter is desired constant phase for this boundary: psi_x_left should be set as -1.0 or 1.0 for phase 1 or phase 2 respectively.

All the quantities are in lattice units

Examples (Direct Numerical Simulation)

Flow over a vehicle: inertia dominated

image image

Single phase flow in a sandstone (Sandstone geometry is build from Micro-CT images at 7.5 microns): viscous dominated

image

Urban air flow: inertia dominated

image

Two Phase flow: oil (non-wetting phase) into a ketton carbonate rock saturated with water (wetting phase): capillary dominated

Alt text

Authors

Jianhui Yang @yjhp1016 Liang Yang @ly16302

License

MIT

Owner
Jianhui Yang
Researcher in CFD, porous medium flow and data science
Jianhui Yang
catch-22: CAnonical Time-series CHaracteristics

catch22 - CAnonical Time-series CHaracteristics About catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Ma

Carl H Lubba 229 Oct 21, 2022
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation (AAAI 2021) Official pytorch implementation of our paper: Discriminative

Beom 74 Dec 27, 2022
A parallel framework for population-based multi-agent reinforcement learning.

MALib: A parallel framework for population-based multi-agent reinforcement learning MALib is a parallel framework of population-based learning nested

MARL @ SJTU 348 Jan 08, 2023
Official implementation of DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations in TensorFlow 2

DreamerPro Official implementation of DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations in TensorFl

22 Nov 01, 2022
Pacman-AI - AI project designed by UC Berkeley. Designed reflex and minimax agents for the game Pacman.

Pacman AI Jussi Doherty CAP 4601 - Introduction to Artificial Intelligence - Fall 2020 Python version 3.0+ Source of this project This repo contains a

Jussi Doherty 1 Jan 03, 2022
Official implementation of "MetaSDF: Meta-learning Signed Distance Functions"

MetaSDF: Meta-learning Signed Distance Functions Project Page | Paper | Data Vincent Sitzmann*, Eric Ryan Chan*, Richard Tucker, Noah Snavely Gordon W

Vincent Sitzmann 100 Jan 01, 2023
Keqing Chatbot With Python

KeqingChatbot A public running instance can be found on telegram as @keqingchat_bot. Requirements Python 3.8 or higher. A bot token. Local Deploy git

Rikka-Chan 2 Jan 16, 2022
Point-NeRF: Point-based Neural Radiance Fields

Point-NeRF: Point-based Neural Radiance Fields Project Sites | Paper | Primary c

Qiangeng Xu 662 Jan 01, 2023
Trajectory Extraction of road users via Traffic Camera

Traffic Monitoring Citation The associated paper for this project will be published here as soon as possible. When using this software, please cite th

Julian Strosahl 14 Dec 17, 2022
This is the repository for Learning to Generate Piano Music With Sustain Pedals

SusPedal-Gen This is the official repository of Learning to Generate Piano Music With Sustain Pedals Demo Page Dataset The dataset used in this projec

Joann Ching 12 Sep 02, 2022
Object classification with basic computer vision techniques

naive-image-classification Object classification with basic computer vision techniques. Final assignment for the computer vision course I took at univ

2 Jul 01, 2022
Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources.

Illumination_Decomposition Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources. This code implements the

QAY 7 Nov 15, 2020
Text to Image Generation with Semantic-Spatial Aware GAN

text2image This repository includes the implementation for Text to Image Generation with Semantic-Spatial Aware GAN This repo is not completely. Netwo

CVDDL 124 Dec 30, 2022
This is a simple face recognition mini project that was completed by a team of 3 members in 1 week's time

PeekingDuckling 1. Description This is an implementation of facial identification algorithm to detect and identify the faces of the 3 team members Cla

Eric Kwok 2 Jan 25, 2022
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

Advances in Financial Machine Learning Exercises Experimental solutions to selected exercises from the book Advances in Financial Machine Learning by

Brian 1.4k Jan 04, 2023
Users can free try their models on SIDD dataset based on this code

SIDD benchmark 1 Train python train.py If you want to train your network, just modify the yaml in the options folder. 2 Validation python validation.p

Yuzhi ZHAO 2 May 20, 2022
Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference

Ankou Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering t

SoftSec Lab 54 Dec 24, 2022
The project was to detect traffic signs, based on the Megengine framework.

trafficsign 赛题 旷视AI智慧交通开源赛道,初赛1/177,复赛1/12。 本赛题为复杂场景的交通标志检测,对五种交通标志进行识别。 框架 megengine 算法方案 网络框架 atss + resnext101_32x8d 训练阶段 图片尺寸 最终提交版本输入图片尺寸为(1500,2

20 Dec 02, 2022
《Train in Germany, Test in The USA: Making 3D Object Detectors Generalize》(CVPR 2020)

Train in Germany, Test in The USA: Making 3D Object Detectors Generalize This paper has been accpeted by Conference on Computer Vision and Pattern Rec

Xiangyu Chen 101 Jan 02, 2023