A three-dimensional numerical study on dynamics behavior of a rising vapor bubble in uniformly superheated liquid by lattice Boltzmann method

2016 ◽  
Vol 62 ◽  
pp. 362-374 ◽  
Author(s):  
Tao Sun ◽  
Jiangang Sun ◽  
Xueye Ang ◽  
Shanshan Li ◽  
Xin Su
Keyword(s):  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Vapor Bubble ◽  
Numerical Study ◽  
Three Dimensional ◽  
Superheated Liquid ◽  
Boltzmann Method

A numerical study of droplet dynamic behaviors on a micro-structured surface using a three dimensional color-gradient lattice Boltzmann model

Soft Matter ◽  
2018 ◽  
Vol 14 (5) ◽  
pp. 837-847 ◽  
Author(s):  
Zihao Cheng ◽  
Yan Ba ◽  
Jinju Sun ◽  
Chao Wang ◽  
Shengchuan Cai ◽  
...  
Keyword(s):  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Numerical Study ◽  
Three Dimensional ◽  
Lattice Boltzmann Model ◽  
Dynamic Behaviors ◽  
Structured Surfaces ◽  
Color Gradient ◽  
Boltzmann Method ◽  
Boltzmann Model

Non-circular droplet contact areas on micro-structured surfaces are simulated using the lattice Boltzmann method.

scholarly journals A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary–lattice Boltzmann method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi Zhu ◽  
Fang-Bao Tian ◽  
John Young ◽  
James C. Liao ◽  
Joseph C. S. Lai
Keyword(s):  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Prey Capture ◽  
Numerical Study ◽  
Benchmark Problems ◽  
Immersed Boundary ◽  
Complex Environments ◽  
Fish Model ◽  
Point To Point ◽  
Boltzmann Method

AbstractFish adaption behaviors in complex environments are of great importance in improving the performance of underwater vehicles. This work presents a numerical study of the adaption behaviors of self-propelled fish in complex environments by developing a numerical framework of deep learning and immersed boundary–lattice Boltzmann method (IB–LBM). In this framework, the fish swimming in a viscous incompressible flow is simulated with an IB–LBM which is validated by conducting two benchmark problems including a uniform flow over a stationary cylinder and a self-propelled anguilliform swimming in a quiescent flow. Furthermore, a deep recurrent Q-network (DRQN) is incorporated with the IB–LBM to train the fish model to adapt its motion to optimally achieve a specific task, such as prey capture, rheotaxis and Kármán gaiting. Compared to existing learning models for fish, this work incorporates the fish position, velocity and acceleration into the state space in the DRQN; and it considers the amplitude and frequency action spaces as well as the historical effects. This framework makes use of the high computational efficiency of the IB–LBM which is of crucial importance for the effective coupling with learning algorithms. Applications of the proposed numerical framework in point-to-point swimming in quiescent flow and position holding both in a uniform stream and a Kármán vortex street demonstrate the strategies used to adapt to different situations.

Numerical study of capillary-driven flow in square micro-channel by lattice Boltzmann method

2019 ◽  
Vol 19 (1) ◽  
pp. 12
Author(s):  
Mohamed El Amine Ben Amara ◽  
Patrick Perré ◽  
Abdolreza Kharaghani ◽  
Sassi Ben Nasrallah
Keyword(s):  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Numerical Study ◽  
Micro Channel ◽  
Boltzmann Method
Sign in / Sign up

Export Citation Format

Share Document