Article(id=1241321988816433728, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241321979433767757, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2024.02.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1697040000000, receivedDateStr=2023-10-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883824781, onlineDateStr=2026-03-19, pubDate=1711900800000, pubDateStr=2024-04-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883824781, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883824781, creator=13701087609, updateTime=1773883824781, updator=13701087609, issue=Issue{id=1241321979433767757, tenantId=1146029695717560320, journalId=1235980550691926019, year='2024', volume='44', issue='2', pageStart='1', pageEnd='191', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883822544, creator=13701087609, updateTime=1773884556149, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241325056454881881, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241321979433767757, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241325056454881882, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241321979433767757, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=13, endPage=17, ext={EN=ArticleExt(id=1241321989185532508, articleId=1241321988816433728, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Numerical Simulation of Characteristics of Motion for Polymetallic Nodules in Swirling Flow During Hydraulic Lifting, columnId=1236276106018484431, journalTitle=Mining and Metallurgical Engineering, columnName=MINING, runingTitle=null, highlight=null, articleAbstract=

The lifting of polymetallic nodules was simulated by using a CFD-DEM coupling approach, and the influence of different swirling flow on hydraulic lifting of polymetallic nodules in vertical pipelines was explored. The results show that as swirl ratio increases, the intensity of swirl will be enhanced significantly, and both the maximum fluid velocity and the average axial velocity of particle group will increase. It is found that intensity of swirl can bring obvious impact to the distribution of particles with different velocity in the pipe, showing that particles with high velocity are gradually distributed around the pipe wall, while the particles with low velocity in the center of the pipe. Swirling flow can reduce the local concentration of particles, which is beneficial to reducing the risk of pipeline blockage due to higher concentration of particles caused by retention effect.

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采用CFD-DEM耦合方法对多金属结核提升进行模拟,研究了垂直管道中不同旋转流场旋流比对水力提升多金属结核输运特性的影响。结果表明,随着旋转流场旋流比增大,旋转强度显著增强,流体流速最大值增大,颗粒群轴向平均速度增大。旋转流场的旋转强度明显影响管道内颗粒的速度分布:速度大的颗粒逐步分布在管壁周围,速度较小的颗粒分布在管道中心处。旋转流场可降低颗粒群局部浓度,有利于降低因颗粒滞留效应引起局部浓度升高造成的输送管堵塞风险。

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沈义俊(1964—),男,湖北人,教授,研究方向为深海资源开发技术与装备。Email:
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陈云(1989—),男,湖北人,硕士研究生,主要研究方向为深海矿产资源开采与输送技术。Email:

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陈云(1989—),男,湖北人,硕士研究生,主要研究方向为深海矿产资源开采与输送技术。Email:

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(a)SR=0;(b)SR=0.1;(c)SR=0.25;(d)SR=0.5

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(a)SR=0;(b)SR=0.1;(c)SR=0.25;(d)SR=0.5

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序号切向速度W/(m·s-1轴向速度U/(m·s-1旋流比(SR=W/U
103.20
20.323.20.1
30.83.20.25
41.63.20.5
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模拟条件

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序号切向速度W/(m·s-1轴向速度U/(m·s-1旋流比(SR=W/U
103.20
20.323.20.1
30.83.20.25
41.63.20.5
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材料相互作用材料恢复系数静摩擦系数滚动摩擦系数
颗粒-颗粒0.450.60.05
颗粒-管壁0.500.10.01
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物相相互影响系数

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颗粒-颗粒0.450.60.05
颗粒-管壁0.500.10.01
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监测区域轴向平均速度/(m·s-1
SR=0SR=0.1SR=0.25SR=0.5
A2.862.793.003.02
B2.762.692.953.04
C2.982.973.053.18
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颗粒群轴向平均速度

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监测区域轴向平均速度/(m·s-1
SR=0SR=0.1SR=0.25SR=0.5
A2.862.793.003.02
B2.762.692.953.04
C2.982.973.053.18
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旋流比轴向速度横截面分布
A区域横截面B区域横截面C区域横截面
0
0.1
0.25
0.5
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不同旋流比时轴向速度的横截面分布

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旋流比轴向速度横截面分布
A区域横截面B区域横截面C区域横截面
0
0.1
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旋转水力提升多金属结核运动特性数值模拟
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陈云 1, 2 , 杜燕连 1, 3 , 李萌 1, 3 , 梁栋 1, 2 , 沈义俊 1, 2
矿冶工程杂志 | 采矿 2024,44(2): 13-17
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矿冶工程杂志 | 采矿 2024, 44(2): 13-17
旋转水力提升多金属结核运动特性数值模拟
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陈云1, 2 , 杜燕连1, 3, 李萌1, 3, 梁栋1, 2, 沈义俊1, 2
作者信息
  • 1.海南大学 南海海洋资源利用国家重点实验室,海南 海口 570228
  • 2.海南大学 机电工程学院,海南 海口 570228
  • 3.海南大学 信息与通信工程学院,海南 海口 570228
  • 陈云(1989—),男,湖北人,硕士研究生,主要研究方向为深海矿产资源开采与输送技术。Email:

通讯作者:

沈义俊(1964—),男,湖北人,教授,研究方向为深海资源开发技术与装备。Email:
Numerical Simulation of Characteristics of Motion for Polymetallic Nodules in Swirling Flow During Hydraulic Lifting
Yun CHEN1, 2 , Yanlian DU1, 3, Meng LI1, 3, Dong LIANG1, 2, Yijun SHEN1, 2
Affiliations
  • 1.State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, Hainan, China
  • 2.College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, Hainan, China
  • 3.College of Information and Communication Engineering, Hainan University, Haikou 570228, Hainan, China
出版时间: 2024-04-01 doi: 10.3969/j.issn.0253-6099.2024.02.004
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采用CFD-DEM耦合方法对多金属结核提升进行模拟,研究了垂直管道中不同旋转流场旋流比对水力提升多金属结核输运特性的影响。结果表明,随着旋转流场旋流比增大,旋转强度显著增强,流体流速最大值增大,颗粒群轴向平均速度增大。旋转流场的旋转强度明显影响管道内颗粒的速度分布:速度大的颗粒逐步分布在管壁周围,速度较小的颗粒分布在管道中心处。旋转流场可降低颗粒群局部浓度,有利于降低因颗粒滞留效应引起局部浓度升高造成的输送管堵塞风险。

深海采矿  /  多金属结核  /  液固双相流  /  水力提升  /  管道输送  /  旋流比  /  CFD-DEM耦合

The lifting of polymetallic nodules was simulated by using a CFD-DEM coupling approach, and the influence of different swirling flow on hydraulic lifting of polymetallic nodules in vertical pipelines was explored. The results show that as swirl ratio increases, the intensity of swirl will be enhanced significantly, and both the maximum fluid velocity and the average axial velocity of particle group will increase. It is found that intensity of swirl can bring obvious impact to the distribution of particles with different velocity in the pipe, showing that particles with high velocity are gradually distributed around the pipe wall, while the particles with low velocity in the center of the pipe. Swirling flow can reduce the local concentration of particles, which is beneficial to reducing the risk of pipeline blockage due to higher concentration of particles caused by retention effect.

deep sea mining  /  polymetallic nodules  /  liquid-solid two-phase flow  /  hydraulic lifting  /  pipeline transportation  /  swirl ratio  /  CFD-DEM coupling
陈云, 杜燕连, 李萌, 梁栋, 沈义俊. 旋转水力提升多金属结核运动特性数值模拟. 矿冶工程杂志, 2024 , 44 (2) : 13 -17 . DOI: 10.3969/j.issn.0253-6099.2024.02.004
Yun CHEN, Yanlian DU, Meng LI, Dong LIANG, Yijun SHEN. Numerical Simulation of Characteristics of Motion for Polymetallic Nodules in Swirling Flow During Hydraulic Lifting[J]. Mining and Metallurgical Engineering, 2024 , 44 (2) : 13 -17 . DOI: 10.3969/j.issn.0253-6099.2024.02.004
随着社会经济发展,陆地矿产资源急剧消耗,深海矿产被认为是未来最具开发潜力的资源。按照输送方式的不同,深海采矿系统主要分为4种类型:海底拖斗采矿系统、连续链斗采矿系统、穿梭船采矿系统和管道水力提升采矿系统[1-3]。目前管道水力提升采矿系统是公认的最有实际应用潜力的采矿系统[4],针对该系统,学者进行了大量实验、模拟研究,并在颗粒沉降[5]、滑移[6]、压力损失[7]等方面获得了一定成果。然而,管道水力提升稳定性问题[8],尤其是提升管道内颗粒的堵塞问题,是深海矿石提升过程中需要重点关注的内容。为了提高运输效率和防止矿浆管道堵塞,人们进行了大量优化运输管道系统的研究工作,主要集中于提升速度、给料浓度、颗粒级配等方面。但通过数值模拟方法研究各种旋流条件下颗粒的运动与分布的研究鲜有报道[9]。相比于具体的试验方法,数值模拟计算方法是研究流固耦合问题的重要手段,可以得到更多试验中无法测量的结果[10-11],有助于实现多尺度揭示液固两相耦合作用过程的深层机理。
本文采用CFD-DEM耦合的数值模拟方法,对不同旋流比下管道内颗粒群的运动进行模拟仿真,得到颗粒的运动和分布规律,研究结果对设计和优化深海管道水力提升系统有一定参考意义。
在几何模块中建立了内径200 mm、长度5 500 mm的三维管道几何模型。颗粒群进入提升管道后,与流体相互作用,一段时间后会形成较为稳定的固液两相流。该长度可以基本满足体现两相流在垂直提升管道中两相耦合运动过程的需求。管道流体计算域网格采用结构性网格,经处理得到六面体结构性网格单元,考虑网格无关性和计算经济性后,选取节点和元素的总数分别为634 569个和662 400个,如图1所示。
在耦合仿真计算中,首先在Fluent流体仿真软件中初始化流场,在一个时间步长内求解连续性方程和Navier-Stokes动量方程,将更新后的流场信息传递给DEM模块。DEM模块通过分析流体对颗粒的拖曳力以及颗粒之间的相互作用,计算出颗粒的运动方程,从而得到颗粒速度、位移等信息,并将这些信息反馈给Fluent。然后继续进行下一步迭代计算,直到得到收敛解。
连续性方程(式(1))和N-S动量方程(式(2))分别为:
式中ρ为流体密度;t为时间;vαρgαFα分别为速度、重力和外部体积力矢量在笛卡尔直角坐标系α方向的分量;T为流体的黏性应力张量。
在DEM模块中,计算域内的所有粒子都以拉格朗日方式跟踪,通过求解分别控制粒子平动和旋转运动的欧拉第一运动定律方程(式(3))和第二运动定律方程(式(4)):
式中mp为颗粒质量;vp为颗粒速度;FC为颗粒与颗粒和颗粒与壁面相互作用的接触力;Ffp为流体对颗粒的作用力;g为重力加速度矢量;Ip为颗粒的转动惯量张量;ωp为颗粒角速度矢量;MC为切向合力矩;Mfp为流体速度梯度力矩。
CFD-DEM耦合方法结合了Fluent和DEM的优点。因此在建立耦合连接之前,需要分别设置Fluent和DEM的参数。
两相流在管道中输送时,在与DEM耦合的过程中,流场信息不断更新,因此在Fluent中选择瞬态计算。流体材料设定为水,由于水是不可压缩流体,选择基于压力的求解器。管道入口设为速度进口条件,管道出口设为压力出口条件,压力与速度耦合求解采用SIMPLE算法,重力沿流动反方向。湍流模型采用SST k-ω。旋流是通过在管道入口施加切向速度W和轴向速度U产生的,本文用旋流比[12]SR=W/U表征流动中的旋转强度,其中W为切向速度、U为轴向速度。在轴向速度U=3.2 m/s,入口切向速度分别为0.32 m/s(SR=0.1)、0.8 m/s(SR=0.25)和1.6 m/s(SR=0.5)条件下进行了模拟,如表1所示。出口设置为压力出口,出口压力值设置为一个标准大气压。入口和出口的湍流规格方法设置为强度和水力直径;湍流强度设置为5%,水力直径为0.2 m。壁面边界条件采用无穿透、无滑移壁面边界条件。
在DEM中设定颗粒密度2 000 kg/m3、泊松比0.3、杨氏模量1×108 Pa;颗粒形状设置为球形,直径20 mm;通过管道的颗粒群体积浓度10%,颗粒群质量输送速率为20.1 kg/s;管壁密度7 800 kg/m3、泊松比0.3、杨氏模量1×1011 Pa。颗粒与颗粒之间、颗粒与管壁之间的碰撞采用Hertz-Mindlin无滑动接触模型。颗粒与颗粒、颗粒与管壁间的相互影响系数见表2
按照不同的旋流比,进行流体相和单一粒径颗粒群输送5组仿真,可以基本反映不同旋流比流场下垂直管道输送过程中颗粒的运动与分布。
旋转流场流体相流动特征是颗粒旋流提升的研究基础,通过对液体单相的模拟仿真,可以研究旋流场中切向速度对轴向速度的影响。不同旋流条件下垂直管道轴向速度流线云图见图2。从图2可以明显看出,旋流比不同,流场旋转强度不同。随着旋流比增大,流场旋转强度显著增强,说明旋流比可以简单表征流场旋转强度。较高的轴向速度主要分布在管道中心,较低的轴向速度则出现在近管壁区域。随着管道流场旋流比从0增大到0.5,流体轴向流速最大值增大了7.3%,说明旋转强度明显影响管道内的速度分布。在远离管道入口处,离心力减弱,旋流沿流向方向明显衰减,这是黏性耗散和切向动量从中心区域向壁面转移的结果。切向速度增加,旋转距离增大,衰减率略有减小。
颗粒群输送时,在不同旋流比的流场中,由于颗粒群与流体之间的相互影响、颗粒与管壁的碰撞以及颗粒与颗粒之间的碰撞等,颗粒群输送存在一些不同的特点。以粒径20 mm颗粒群模拟计算,颗粒群初始以0速度进入计算域,通过提升管道的颗粒群体积浓度为10%,颗粒群质量输送速率为20.1 kg/s。为了对管道中颗粒的局部浓度、流速进行实时监测,分别在距离管道入口1 m、2 m以及4 m处设置长度为0.2 m的监测区域,分别命名为A、B、C区域。
在颗粒运动过程中,因为管道内流动为湍流,颗粒受到不规则的流体作用力;加上碰撞的影响,颗粒在管道中做不规则运动;总体而言,颗粒速度在其平均值附近波动。不同旋流比下监测区域的颗粒群轴向平均速度见表3。由表3可以发现,颗粒群轴向平均速度小于设定的轴向速度(3.2 m/s),因此颗粒会滞后于流体流动。随着旋流比变大,流场旋转强度变大,颗粒轴向平均速度增大;SR=0.1时的轴向平均速度值背离这一趋势的原因可能是旋转强度较弱,旋转流场衰减较快;这一现象也可以与旋流单相流动特性相互印证。同时较弱的旋转强度加强了流体的湍流,阻碍了颗粒的输运。
表4为颗粒在管道中输送时监测区域颗粒轴向速度的横截面分布。可以发现,分布在管道中心处颗粒的轴向速度一般要大于分布在管壁附近颗粒的轴向速度。随着旋流比增大,旋转强度增加,速度较高颗粒的位置开始从管道中心向管壁移动。这可能是随着旋转强度增大,拥有较大速度的颗粒开始随着流场做离心运动。
图3为某时间点颗粒在管道中较稳定流态段的轴向速度分布。图示现象表明,旋流比较低时,大量速度较小的颗粒分布在管壁周围,速度较大的颗粒主要分布在管心处;随着旋流比增大,速度大的颗粒逐步分布在管壁周围,速度较小的颗粒分布在管道中心处,这一现象和表4结果相吻合。同时可以发现,旋流比较低时,大量速度较低的颗粒在管壁处堆积,容易造成局部浓度升高从而导致输送管堵塞。管道中心处的流体流速一般都大于管壁处的流体流速,旋流比较高时,分布在管心处的低速颗粒可以充分利用这部分流体的输送能力,实现颗粒在管道中的输送。
图4为不同旋流比工况下各监测区域的时均浓度曲线,由于颗粒的无规则运动,监测区域的时均浓度呈无规律变化。为具有一般性,取一段时间内的颗粒平均浓度值,表5为3个监测区域在不同旋流比时颗粒群的平均浓度。随着旋流比增大,颗粒群局部浓度减小。这一结果表明旋转流场可以降低局部浓度,从而改善因颗粒滞留效应而产生的局部浓度升高。管道内颗粒群局部浓度升高是造成输运管堵塞的原因之一,旋转流场可以改善输送管的堵塞,有利于提高管道水力提升稳定性。结合表3表5数据可以发现,同一监测区域内颗粒群平均速度越大,其局部平均浓度越低,局部浓度值越接近设定的输送浓度(10%)。这是因为颗粒速度越大,其对应的滑移速度越小,两相流中颗粒群的滞留效应越弱。这一结果和文献[6]结果一致。
通过数值模拟,比较了不同旋流比下颗粒在管道内的运动和分布,并进一步研究了旋流比对颗粒输送特性和流动稳定性的影响。主要研究结果如下:
1)旋流比不同,流场旋转强度不同,随着旋流比增大,流场旋转强度显著增强,说明旋流比可以简单表征流场旋转强度。随着旋流比增大,流体流速最大值增大,说明旋转强度明显影响管道内的速度分布。
2)随着旋流比变大,颗粒群轴向平均速度增大;速度大的颗粒逐步分布在管壁周围,速度较小的颗粒分布在管道中心处;旋转流场可以改善颗粒的速度分布,可以充分利用流体的输送能力来实现颗粒在管道中的输送。
3)随着旋流比增大,颗粒群局部浓度变小,这有利于降低因颗粒滞留效应产生的局部浓度升高而造成的输送管堵塞风险。
  • 海南省重大科技计划(ZDKJ2021027)
  • 海南省重点研发计划(ZDYF2020207)
  • 三亚崖州湾深海科技城科研项目(SKJC-2020-01-016)
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2024年第44卷第2期
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doi: 10.3969/j.issn.0253-6099.2024.02.004
  • 接收时间:2023-10-12
  • 首发时间:2026-03-19
  • 出版时间:2024-04-01
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  • 收稿日期:2023-10-12
基金
海南省重大科技计划(ZDKJ2021027)
海南省重点研发计划(ZDYF2020207)
三亚崖州湾深海科技城科研项目(SKJC-2020-01-016)
作者信息
    1.海南大学 南海海洋资源利用国家重点实验室,海南 海口 570228
    2.海南大学 机电工程学院,海南 海口 570228
    3.海南大学 信息与通信工程学院,海南 海口 570228

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沈义俊(1964—),男,湖北人,教授,研究方向为深海资源开发技术与装备。Email:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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