Signed distance field enhanced fully resolved CFD-DEM for simulation of granular flows involving multiphase fluids and irregularly shaped particles

计算流体力学 CFD-DEM公司 离散元法 机械 粒状材料 浸入边界法 多相流 计算科学 计算机科学 物理 几何学 经典力学 工程类 边界(拓扑) 数学 岩土工程 数学分析
作者
Zhengshou Lai,Jidong Zhao,Shiwei Zhao,Linchong Huang
出处
期刊:Computer Methods in Applied Mechanics and Engineering [Elsevier BV]
卷期号:414: 116195-116195
标识
DOI:10.1016/j.cma.2023.116195
摘要

It is challenging to model granular particles with arbitrary shapes and related complications to fluid–particle interactions for granular flows which are widely encountered in nature and engineering. This paper presents an improved framework of the immersed boundary method (IBM)-based fully resolved computational fluid dynamics (CFD) and discrete element method (DEM), with an emphasis on irregular-shaped particles and the implications to particle–fluid interactions. The improved CFD-DEM framework is featured by two novel enhancements with signed distance field (SDF). First, an SDF-based formulation is employed to enable handling of granular particles with arbitrary shapes in DEM robustly and efficiently. Second, the IBM is modified to be consistent with SDF to fully resolve fluid–particle interactions in the presence of non-spherical particles. Such treatments leverage SDF as a generic interface to furnish a new SDF-CFD-DEM framework for universal modeling of arbitrarily shaped particles interacting with multiphase fluids with desired resolutions. Exemplified particle shape models include super-quadrics, spherical harmonics, polyhedron and level set, and new shape models can be flexibly developed by implementing the unified SDF-based shape interface. The proposed SDF-CFD-DEM is validated and showcased with examples including particle settling, drafting–kissing–tumbling, immersed granular collapse, and mudflow. The results demonstrate the good accuracy and robustness of the SDF-CFD-DEM and its potential for efficient computational modeling of multiphase granular flows involving granular particles with arbitrary shapes.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
华仔应助善良曼寒采纳,获得10
刚刚
CipherSage应助ji采纳,获得10
1秒前
俞辰发布了新的文献求助10
1秒前
嘎嘎嘎嘎完成签到,获得积分10
2秒前
2秒前
科研通AI6.2应助肖恩采纳,获得10
2秒前
3秒前
烂泥发布了新的文献求助10
3秒前
完美世界应助念念采纳,获得10
3秒前
3秒前
苗儿发布了新的文献求助10
3秒前
西in发布了新的文献求助10
3秒前
3秒前
Fremerty完成签到,获得积分10
3秒前
3秒前
2052669099应助sandra采纳,获得10
4秒前
qwertyuiop发布了新的文献求助10
4秒前
万能图书馆应助sandra采纳,获得10
4秒前
科研通AI6.2应助LL采纳,获得10
4秒前
月月关注了科研通微信公众号
4秒前
4秒前
4秒前
六十号完成签到,获得积分10
4秒前
HHH完成签到,获得积分10
4秒前
科研通AI6.2应助Propitious采纳,获得10
4秒前
SciGPT应助合适的咖啡采纳,获得10
5秒前
5秒前
5秒前
5秒前
5秒前
坤儿哥发布了新的文献求助10
5秒前
故意的凤妖完成签到,获得积分10
5秒前
我是老大应助CubeQ采纳,获得40
5秒前
xuuuuu完成签到,获得积分10
5秒前
niii发布了新的文献求助10
5秒前
6秒前
tian发布了新的文献求助10
6秒前
6秒前
卢立欣发布了新的文献求助10
6秒前
优美元枫完成签到,获得积分10
7秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 2000
Burger's Medicinal Chemistry, Drug Discovery and Development, Volumes 1 - 8, 8 Volume Set, 8th Edition 1800
Cronologia da história de Macau 1600
文献PREDICTION EQUATIONS FOR SHIPS' TURNING CIRCLES或期刊Transactions of the North East Coast Institution of Engineers and Shipbuilders第95卷 1000
BRITTLE FRACTURE IN WELDED SHIPS 1000
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 计算机科学 化学工程 生物化学 物理 复合材料 内科学 催化作用 物理化学 光电子学 细胞生物学 基因 电极 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6147328
求助须知:如何正确求助?哪些是违规求助? 7974032
关于积分的说明 16565931
捐赠科研通 5258074
什么是DOI,文献DOI怎么找? 2807599
邀请新用户注册赠送积分活动 1787997
关于科研通互助平台的介绍 1656644