Article(id=1243220386037481579, tenantId=1146029695717560320, journalId=1242844143416819734, issueId=1243220377556595432, articleNumber=null, orderNo=null, doi=10.15959/j.cnki.0254-0053.2025.03.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742227200000, receivedDateStr=2025-03-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1774336437948, onlineDateStr=2026-03-24, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1774336437948, onlineIssueDateStr=2026-03-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774336437948, creator=13701087609, updateTime=1774336437948, updator=13701087609, issue=Issue{id=1243220377556595432, tenantId=1146029695717560320, journalId=1242844143416819734, year='2025', volume='46', issue='3', pageStart='541', pageEnd='810', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1774336435926, creator=13701087609, updateTime=1774336648182, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1243221267898282005, tenantId=1146029695717560320, journalId=1242844143416819734, issueId=1243220377556595432, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1243221267898282006, tenantId=1146029695717560320, journalId=1242844143416819734, issueId=1243220377556595432, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=775, endPage=785, ext={EN=ArticleExt(id=1243220386817622137, articleId=1243220386037481579, tenantId=1146029695717560320, journalId=1242844143416819734, language=EN, title=Optimization of Continuous Casting Mold Pull Velocity Based on Numerical Simulation, columnId=null, journalTitle=Chinese Quarterly of Mechanics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Currently, over 90% of crude steel production is achieved through continuous casting. Increasing the casting speed during continuous casting can significantly enhance production efficiency, but it also impacts the flow field, exacerbating slag entrapment and argon bubble entrainment, which lead to a series of quality defects. These issues have become critical constraints on the development of high-speed continuous casting molds. This paper establishes a multiphase numerical model of the continuous casting mold by coupling Large Eddy Simulation (LES) with the Volume of Fluid (VOF) method and a two-way coupled Discrete Phase Model (DPM). By analyzing flow field variations, steel-slag interface velocity, interface fluctuations, and slag entrapment ration under three different casting speeds, the internal correlation mechanisms are revealed. The study finds that appropriately increasing casting speed can improve production quality, providing a reference for optimizing casting speed in continuous casting processes.

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目前超过90%的粗钢生产是通过连铸完成的,而在连铸生产过程中增加拉速能够显著提高生产效率,但会对流场造成影响,加剧卷渣、气泡卷吸的发生,会引起一系列的质量缺陷,成为了制约高拉速结晶器发展的关键问题.本文基于大涡模拟(Large Eddy Simulation,LES)耦合了流体体积模型(Volume of Fluid,VOF)和双向耦合的离散相模型(Discrete Phase Model,DPM)建立了连铸结晶器的多相流数值模型,针对三种拉速变化,从流场变化、钢-渣界面速度、钢-渣界面波动和卷渣量等角度进行分析,揭示了内部相关的变化规律,发现一定程度地提高拉速反而能够提高生产质量,为连铸生产的拉速优化提供了参考.

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陈硕,博士,教授.研究方向:计算流体力学.E-mail:
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夏海涛,硕士生.研究方向:计算流体力学.E-mail:

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夏海涛,硕士生.研究方向:计算流体力学.E-mail:

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夏海涛,硕士生.研究方向:计算流体力学.E-mail:

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Numerical settings

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Material propertiesSteel density/(kg/m3)7 066
Slag density/(kg/m3)2 500
Steel specific heat/(J/kg·K)826
Slag specific heat/(J/kg·K)3 000
Steel viscosity/(kg/m·s)0.006 3
Slag viscosity/(kg/m·s)0.18
Steel-slag surface tension[21]/(N/m)1.3
Steel-air surface tension[21]/(N/m)1.6
Slag-air surface tension[21]/(N/m)0.5
Geometric conditionsMold width/mm1 000
Mold thickness/mm250
Mold height/mm3 000
Nozzle inlet diameter/mm80
Casting speed/(m/min)1.1, 1.3, 1.5
Argon flow rate/(L/min)10
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仿真设置

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Material propertiesSteel density/(kg/m3)7 066
Slag density/(kg/m3)2 500
Steel specific heat/(J/kg·K)826
Slag specific heat/(J/kg·K)3 000
Steel viscosity/(kg/m·s)0.006 3
Slag viscosity/(kg/m·s)0.18
Steel-slag surface tension[21]/(N/m)1.3
Steel-air surface tension[21]/(N/m)1.6
Slag-air surface tension[21]/(N/m)0.5
Geometric conditionsMold width/mm1 000
Mold thickness/mm250
Mold height/mm3 000
Nozzle inlet diameter/mm80
Casting speed/(m/min)1.1, 1.3, 1.5
Argon flow rate/(L/min)10
), ArticleFig(id=1243220407533290283, tenantId=1146029695717560320, journalId=1242844143416819734, articleId=1243220386037481579, language=EN, label=Tab.2, caption=

Boundary conditions and initial conditions

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Boundary nameBoundary typeVariableValue
InletVelocity inletVelocity Temperature1.3, 1.54, 1.77 m/s
1 834 K
OutletOutflowFlow rate weighting1
Wide and narrow wallsNo-slip moving wallVelocity Temperature1.1, 1.3, 1.5 m/min
1 796 K
Top surfaceFree-slip wallShear stress Temperature0 Pa Adiabatic
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边界条件和初始条件

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Boundary nameBoundary typeVariableValue
InletVelocity inletVelocity Temperature1.3, 1.54, 1.77 m/s
1 834 K
OutletOutflowFlow rate weighting1
Wide and narrow wallsNo-slip moving wallVelocity Temperature1.1, 1.3, 1.5 m/min
1 796 K
Top surfaceFree-slip wallShear stress Temperature0 Pa Adiabatic
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基于数值模拟的连铸结晶器拉速优化
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夏海涛 1 , 李启良 2 , 陈硕 1, 3 , 谢玉 4 , 范正洁 4
力学季刊 | 2025,46(3): 775-785
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力学季刊 | 2025, 46(3): 775-785
基于数值模拟的连铸结晶器拉速优化
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夏海涛1 , 李启良2, 陈硕1, 3 , 谢玉4, 范正洁4
作者信息
  • 1.同济大学 航空航天与力学学院,上海 200092
  • 2.同济大学 汽车学院,上海 201804
  • 3.同济大学 上海市地面交通工具空气动力与热环境模拟重点实验室,上海 201804
  • 4.宝山钢铁股份有限公司 中央研究院,上海 201900
  • 夏海涛,硕士生.研究方向:计算流体力学.E-mail:

通讯作者:

陈硕,博士,教授.研究方向:计算流体力学.E-mail:
Optimization of Continuous Casting Mold Pull Velocity Based on Numerical Simulation
Haitao XIA1 , Qiliang LI2, Shuo CHEN1, 3 , Yu XIE4, Zhengjie FANG4
Affiliations
  • 1.School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
  • 2.School of Automotive Studies, Tongji University, Shanghai 201804, China
  • 3.Shanghai Key Laboratory of Vehicle Aerodynamics and Vehicle Thermal Management Systems, Tongji University, Shanghai 201804, China
  • 4.Central Research Institute, Baoshan Iron & Steel Co., Ltd., Shanghai 201900, China
出版时间: 2025-09-25 doi: 10.15959/j.cnki.0254-0053.2025.03.018
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目前超过90%的粗钢生产是通过连铸完成的,而在连铸生产过程中增加拉速能够显著提高生产效率,但会对流场造成影响,加剧卷渣、气泡卷吸的发生,会引起一系列的质量缺陷,成为了制约高拉速结晶器发展的关键问题.本文基于大涡模拟(Large Eddy Simulation,LES)耦合了流体体积模型(Volume of Fluid,VOF)和双向耦合的离散相模型(Discrete Phase Model,DPM)建立了连铸结晶器的多相流数值模型,针对三种拉速变化,从流场变化、钢-渣界面速度、钢-渣界面波动和卷渣量等角度进行分析,揭示了内部相关的变化规律,发现一定程度地提高拉速反而能够提高生产质量,为连铸生产的拉速优化提供了参考.

连铸结晶器  /  拉速  /  卷渣量  /  多相流耦合模型

Currently, over 90% of crude steel production is achieved through continuous casting. Increasing the casting speed during continuous casting can significantly enhance production efficiency, but it also impacts the flow field, exacerbating slag entrapment and argon bubble entrainment, which lead to a series of quality defects. These issues have become critical constraints on the development of high-speed continuous casting molds. This paper establishes a multiphase numerical model of the continuous casting mold by coupling Large Eddy Simulation (LES) with the Volume of Fluid (VOF) method and a two-way coupled Discrete Phase Model (DPM). By analyzing flow field variations, steel-slag interface velocity, interface fluctuations, and slag entrapment ration under three different casting speeds, the internal correlation mechanisms are revealed. The study finds that appropriately increasing casting speed can improve production quality, providing a reference for optimizing casting speed in continuous casting processes.

continuous casting mold  /  pull velocity  /  slag entrapment ration  /  multiphase coupled model
夏海涛, 李启良, 陈硕, 谢玉, 范正洁. 基于数值模拟的连铸结晶器拉速优化. 力学季刊, 2025 , 46 (3) : 775 -785 . DOI: 10.15959/j.cnki.0254-0053.2025.03.018
Haitao XIA, Qiliang LI, Shuo CHEN, Yu XIE, Zhengjie FANG. Optimization of Continuous Casting Mold Pull Velocity Based on Numerical Simulation[J]. Chinese Quarterly of Mechanics, 2025 , 46 (3) : 775 -785 . DOI: 10.15959/j.cnki.0254-0053.2025.03.018
钢铁工业是国民经济的重要行业基础,是建设现代化强国的重要支撑.当前,全世界90%以上的钢铁产品采用连铸工艺生产.高品质连铸宽厚板已成为重要的战略资源,被广泛用于国防大型舰艇、核电站安全壳、深海钻井平台、高压锅炉管、轴承套、高速列车轮毂等领域[1].连铸结晶器内高温熔体多相流是一个复杂的非稳态、非线性、非平衡多物理场.从理论的角度去解析连铸结晶器内的复杂现象还有很大困难;而且高温钢液不易直接观察和测量,使得基于计算流体力学的数值模拟成为了研究高温熔融多相流重要的研究手段.
当前基于雷诺平均Navier-Stokes(Reynolds-averaged Navier-Stokes Equations,RANS)的k-ε的是工程中运用广泛的湍流数值模型,其具有较高的计算效率,在采用经验壁面边界条件处理粗网格下边界层内急剧速度梯度时,能够为稳态流场预测提供合理的精度[2].但大涡模拟(Large Eddy Simulation,LES)方法在瞬态流动细节解析、铸机内非对称现象高精度解析等方面表现更优,与实验测量数据相符得更好[3-5].Zhao等[6]采用LES对板坯结晶器内部单相流模拟发现对称结构内存在射流非对称行为,并观测到宽面间存在强振荡现象.Yuan[7]将这种非对称性划分为间歇性短期和长期非对称,分别由入口流动扰动和湍流固有的特性驱动.对于单相流动,基于顶部表面压力分布预测的自由液面波动特性已被证实可准确表征不同浇铸条件下的实际液面行为[4,8-10]
为了更精确表征表面张力及重力和动量之间复杂相互作用对多相流体系的影响,将流体体积模型(Volume of Fluid,VOF)与湍流模型耦合以处理此类多相流动问题.经过大量的工业实践和实验研究,发现卷渣机理主要分为三类[11-14]:剪切卷渣、漩涡卷渣和气泡冲击卷渣.剪切卷渣的实质是渣层和钢液之间的剪切流动引起的相界面不稳定性,其直接影响因素是钢液表面流速;漩涡卷渣是由结晶器内的涡旋不对称引起的,在流动较弱的一侧会形成涡旋,将保护渣抽吸进钢液之中;气泡冲击卷渣是指气泡进入渣层并在钢液-渣界面或渣层内破裂,其冲击力致使保护渣剧烈波动并被卷入钢液.Cho等[15]通过构建LES-VOF耦合模型揭示了水口倾角对保护渣卷入行为的影响机制,研究表明单辊流模式会显著加剧结晶器内液面波动,进而提升卷渣发生比例.Li团队[16,17]通过LES-VOF-DPM(Discrete Phase Model,DPM)多模型耦合研究发现,渣滴更倾向于在高涡量区域而非高速流场中形成,其模拟结果中出现三种典型的保护渣卷入机制.
综上所述,关于连铸结晶器内部流场研究已有了一定的研究,但对于连铸结晶器内的铸造参数优化的研究仍然不足.本文通过数值仿真,对连铸结晶器的关键铸造参数之一的拉速进行对比优化,重点关注内部流场、速度分布、界面波动和卷渣量的变化,从多角度研究了流速的影响,为连铸生产的拉速优化提供了参考.
本文基于有限体积法,使用LES+VOF+DPM的多相耦合模型,基于Ansys Fluent对结晶器内部流场进行数值计算,进过大涡模拟滤波后耦合的控制方程如下
其中,为大涡模拟的滤波后的变量,um,i为不同方向上的混合相速度,p为静压,重力加速度g指向-y方向,表面张力FT在不同界面时具有不同的数值.VOF模型引入体积分数并依此计算每个计算单元内的变量,ρm为混合相密度,,通过体积分数控制,下标1,2和3分别代表钢液、保护渣和空气.
有效粘度μeff=μm+μt,m,前者是混合相的动力粘度,本文使用Smagorinsky-Lily的亚格子模型来求解混合相的湍流粘度μt,m
其中,为亚格子的混合长度,Ls=min(κdwCsΔ),Sij为变形率张量,,其中,κ=0.41为von Kármán常数,dw为距离最近壁面的距离,Cs=0.23为Smagorinsky常数,Δ为局部网格尺度,Vcell为计算网格体积.
混合相的显焓hs,m和湍流热传导系数kts可以表示为
其中,参考温度Tref为297.15 K,Tlocal为计算得到的局部网格温度,本文中普朗特数Prt恒为0.9,cp,mkm分别是混合相的比热和热传导率系数,作为材料的属性,列在表1中.
在本算例中,通过对控制方程添加相应的源项来实现钢水的凝固过程所导致的质量、动量和能量损失,这些源项添加在了靠近壁面处的第一层网格内,这些源项可以表示为[18-19]
其中,Ay为局部网格贴近壁面的面在铸造方向(-y)上的投影面积,Vc为拉速.
通过耦合基于拉格朗日法的DPM模型来反应离散相的氩气气泡与连续相之间的相互作用,氩气气泡的流动行为通过求解以下的力平衡方程得到
其中,mb为氩气气泡质量,ub为氩气气泡速度,ρb为氩气气泡的密度,db为气泡直径,FVM为加速气泡附近流体的虚拟质量力并且虚拟质量力系数Cvm为0.5,FB为浮力与重力的合力,FP为由于流体中的压力梯度所引起的压力梯度力,Fdrag为流体作用在气泡上的曳力,Cd为球型拽力系数[20],Reb=(ρmdb|um-ub|)/μm为气泡相对雷诺数.
由于巨大的温度梯度,氩气气泡在吹入喷口至流入结晶器内的过程中会发生剧烈的膨胀,其密度变化服从理想气体状态方程,p=ρaRaTρaRa分别是氩气密度和氩气气体常数.
此外,DPM模型与LES模型是双向耦合的,因此在动量方程中的源项SDPM表示离散相气泡和连续相之间的动量交换
其中,为氩气流量,Δt为计算的时间步长.
结晶器几何和网格划分如图1所示,采用了结构化的六面体网格划分,对多相界面和近壁处等存在剧烈梯度变化区域进行了网格加密,其余部分通过1.05的网格增长率控制.模型的材料属性和几何参数见表1.所有的材料属性都是在实际高温环境下测试得到的数据,在当前约1 800 K的操作环境下,中间包倾倒出的钢液与钢水凝固的液相线温差约38 K,鉴于较小的温差,忽略了材料属性的变化,因此流体的密度与粘度设置为了常数.
模型的边界条件和初始条件如表2所示,采用了速度入口和出流边界,其中入口速度和壁面移动速度依照拉速进行换算.入口处的温度是根据中间包测量的温度数据得到的,因为此处壁面代表的是钢水的凝固前沿,壁面的温度为钢水凝固的液相温度,上端面采用了自由滑移界面和绝热的设置.
采用了相同的边界条件在不同数量的网格上进行计算,考察计算中一条水平线上的速度分量的变化.如图2所示,当网格数为200万时,在0~0.1 m时,速度分量明显偏小,而400万与600万的计算结果基本保持一致,存在的误差也小于5%,因此认为网格数达到400万时计算结果与网格数无关,为了节约计算开销,后续计算使用400万的网格.
在本研究中使用了源项法的方式简化相变模型以提高计算效率.如图3所示,Zhang等[22]对比了源项法与完全凝固模型的计算结果,发现当计算域在钢水-保护渣界面下方3 m内时,两种方法的差异小于5%,而随着计算域的进一步增大,此时源项法的计算结果会导致数值偏差.本论文中的模型计算域高度满足这个条件,此时的误差基本可以忽略,因此运用源项法可以保证足够的精度.
为了验证数值模型的精确和可靠性,将模拟结果与文献[6]中数据对比,文献数据为连铸结晶器的水模型和单相流场模拟,依照相同条件设置对称仿真模型,进行了结果对照.
图4所示,首先对比了中截面的流线图,在浸没式喷口(Submerged Entry Nozzle,SEN)射流上下共形成了三个涡旋,三个涡旋的位置、大小和形貌都基本一致.其次,在中截面中距离窄面292 mm处的一条垂直线上对比了速度分布结果,模拟得到的结果与文献结果展现了相同的变化趋势,速度峰值的大小和位置也保持一致,存在一定误差在可接受范围内,验证了此数值模型的准确性和可靠性.
压力-速度耦合计算采用了SIMPLE算法,并使用二阶迎风的离散格式.为了满足库朗数和计算花费,时间步长设置为0.001 s,当计算残差达到10-6时,认为计算收敛.为了提高计算的收敛性,先通过稳态计算得到了稳定的速度场和温度场,然后再开始注入了氩气气泡并总共了计算了36 s的瞬态流动.对最后的12 s结果采样计算时均值.每个算例在高性能计算机上使用144核,计算一次花费约12天.
仿真模型中计算了整个域,考虑到内部流场整体是对称的,选择其中一侧的流场进行直观地对比分析.在图5中对比了不同拉速下结晶器中截面上一侧的流线和矢量变化,在一个双开口的SEN结构下,一侧的内部流场基本形成了一个双涡旋的结构,这是由SEN的射流在撞击到窄面后形成的上下回流驱动形成的.首先,随着拉速的增加,即连铸时的通钢量增加,为满足质量守恒,入口的速度也随之提升,因此在SEN内的速度也得到了明显提升,而且SEN喷出后的高速区域面积也明显增加;其次,随着拉速的增加,在粘性力的作用下,整个流场加速了循环流动速率,受制于几何结构,中截面上的涡旋逐步向类似三角形的形貌演变,涡旋中心逐步向窄面靠近;再次,在流场内除了两个大的涡旋外,在SEN上方和窄面与弯月面附近分别有两个涡旋,其中前者是由于氩气气泡的上浮聚集在保护渣-钢液的界面处,这是因为保护渣粘度具有更高的粘度,气泡穿透速率下降,受到钢水流动的拖曳力和气泡的浮力共同作用形成了小的涡旋,后者是由于保护渣层的约束,向上的回流偏转一定角度转向流向SEN方向,但粘性力驱动下形成了一个小的涡旋,这个涡旋对于传热起到了重要的作用[6],随着拉速的增加,这样两个小涡旋的范围都被上回流涡旋挤占.最后,在图中可以发现下回流区内部也存在小的涡旋,这也是由于气泡的流动形成的,部分小气泡受到钢水的拖曳力大于自身的浮力,随着喷口射流一起移动,向下回流的部分随着射流速度的减小,浮力逐渐大于拖曳力,形成了上浮的趋势,而在上浮过程中被射流所裹挟,这样的流动过程又形成了一个涡旋,而随着拉速的增加,拖曳力远大于气泡的浮力,这种流动现象基本消失.
图6是钢液-保护渣界面中心线上的平均速度分布,速度的最大值出现在每一侧的中心位置附近,同时结晶器左右两侧的最大值基本一致,但是速度在分布上存在不对称现象,这是由于内部的湍流扰动和气泡上浮的不均匀共同导致的.在SEN上方和靠近窄面附近的涡旋部分,速度的分布上存在两个极大值,在低拉速下尤其明显.随着拉速的增加到1.5 m/min时,可以发现平均速度的最大值有了相当程度的提高,但是从1.1~1.3 m/min时,变化较小,但是界面速度上靠近角涡和气泡涡旋的极大值明显降低了.
界面波动数值是钢铁生产过程中重要的监测指标,其变化规律可以反应结晶器内部流场的变化现象,通常将±3~5 mm的周期性波动认为流场稳定,卷渣的几率最小[23].但是除了平均波动,如果出现瞬时的剧烈波动表示内部流场存在瞬时剧烈变化,极易发生卷渣、气泡卷吸的现象,会对钢坯造成严重的质量损害.
借助VOF模型可以通过在钢-渣界面建立监测点直接对界面波动的数值进行监测,监测点的位置如图7(a)所示,在图7(b)到(e)中展示了P1到P4点的界面波动,图7(f)和(g)中分别是界面波动的平均值和方差.可以发现拉速对于界面波动的影响非常剧烈,在P1点处,1.1 m/min的拉速下界面波动保持在约±2.5 mm内,1.3 m/min的拉速下界面波动保持在约±3 mm内,而1.5 m/min的拉速下界面波动甚至超过了±5 mm的范围,属于非常剧烈的波动,而随着向SEN方向靠近,P2和P3点也表现出了类似的变化,但波动的数值逐渐降低.但来到P4点时,此时1.1 m/min的界面波动达到了±7 mm左右,同样属于非常剧烈的界面波动,但拉速提高后的界面波动维持在了约±3 mm的范围内,综上所述,1.3 m/min的拉速下,界面波动实现了稳定的界面波动,流场稳定,最不易卷渣.
平均值能够很好地反应出界面发生周期性波动的位置相较于初始界面的起落现象,随着拉速的增加,发现在靠近窄面附近的P1和P8的界面平均值明显升高,这是由于上回流的速度加快,随之增加了对钢-渣界面的冲击力,致使了剧烈的界面波动,而在靠近SEN附近的P4和P5的平均值拉速低时平均值更大,这是由于气泡的上浮导致的,由于更高的拉速增大了喷口射流流速,许多氩气泡受到更大的拖曳力,随射流一起运动,降低了气泡的上浮量,反而界面位置的上升量更小.
方差数据对于瞬时的界面变化幅度更为敏感,能够对存在的瞬时波动进行监测,可以进一步对比界面波动的剧烈程度.在P1、P2、P3、P6、P7和P8处,界面波动的方差随着拉速的升高而升高,尤其是1.5 m/min的拉速,显著加剧了界面波动的幅度;而在P4和P5处是1.1 m/min拉速下的界面波动方差更大,这是因为气泡的上浮所引起的剧烈界面波动.值得注意的是1.3 m/min的拉速,在8个点处的界面波动方差数据都很均衡且方差很小,这表明当前拉速下,界面波动都保持在一个稳定的、小幅度的周期性波动.
为了更进一步地衡量不同拉速对质量的影响,描述卷渣的严重程度,引入了卷渣率(Slag Entrapment Ration,SER)[24]来描述不同平面下的渣含量,可以表示为
其中,Volslag和Volsteel分别为当前平面下保护渣含量和钢水含量的面积加权平均值.如图8所示,从弯月面下方每20 mm的平面统计其卷渣率,卷渣量的变化随着拉速的增加先降低后增加,当拉速为1.5 m/min,SER的值始终保持最高.这说明在低和高拉速下卷渣形成的机理存在差异,当拉速低时,卷渣主要是由于气泡的冲击而形成.较高的吹氩量往往会加剧气泡冲击卷渣的发生,较大的渣滴会裹挟气泡卷吸的深处,随着动能的减少,由于密度的差异渣滴也会逐渐上浮,这个过程中双向耦合的氩气气泡与渣滴会相互促进上浮,因此吹氩行为虽然会导致一定程度的卷渣发生,但是对卷渣深度的影响较小[25].而在高拉速下,卷渣主要是由于剪切流动形成的.当距离弯月面达到600 mm时,SER的值已经基本为0,而且卷渣深度随着拉速增大有了明显增加.值得注意的是,在1.3 m/min的拉速下,SER的数值始终保持在低位,表明其卷渣的剧烈程度相对较低,且最大卷渣深度只有略微升高.
氩气气泡的融合形成的大气泡会倾向于快速上浮而改变流场的流动,加剧界面波动并诱发气泡冲击卷渣的形成.而氩气气泡在保护渣层内发生破裂时会对渣层进行冲击,加剧卷渣的发生.因此,气泡的融合与破裂过程对于研究内部流动和卷渣行为都有重要影响.但是在连续吹氩的仿真模型中,计算内涵盖了近百万的气泡量,这对于计算资源的消耗提出了挑战,因此这也是未来研究的重点,通过适当的简化模型或者短时注入的方式以详细分析融合与破裂对其的影响.
当前结论中的最优拉速是针对一个既定的工况下的结果,如果其它条件的改变会影响到当前结论,例如结晶器宽度、喷口浸没深度、喷口几何结构等.因此,这个结论仅在满足当前的喷口几何条件下,对1 000 mm×250 mm的板坯结晶器的较优拉速为1.3 m/min,但是结论表明连铸生产过程中存在能够兼顾生产效率与质量的最优拉速.
本文基于数值仿真模拟,针对1 000 mm×250 mm的连铸结晶器的三种不同拉速下内部流场变化、界面波动变化和卷渣量的多角度对比,对连铸工艺的铸造参数进行优化.通过与文献数据的对比验证了数值模型的准确性.针对现有三种拉速,得出以下结论
(1)连铸结晶器内多相流场是一个非线性、非平衡的多物理场.许多数据变量与铸坯拉速非线性相关,拉速提高会增加钢-渣界面的速度,但是界面波动和卷渣量反而降低.较高的拉速下形成的更规律的双旋流场反而不利于气泡的上浮,容易加剧气泡被卷吸到更深处被凝固坯壳捕获.
(2)较高拉速会增加SEN区域钢水施加在气泡上的拖曳力,能够避免气泡在靠近SEN附近快速上浮,使界面波动方差平均值和方差减少,但也会加剧靠近窄面附近的界面波动.
(3)卷渣量变化随拉速先减少再增加,这是因为拉速可以克服气泡冲击的卷渣,但是随着速度的增加会加剧剪切卷渣的发生率,形成了卷渣量先减少再增加的趋势.综上所述,通过多角度的对比,一定程度拉速的提高不仅可以提高生产效率也能够显著提高生产质量,针对当前的结构,1.3 m/min的拉速反而比1.1和1.5 m/min的拉速更为合适.
  • 国家自然科学基金面上项目(11872283)
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2025年第46卷第3期
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doi: 10.15959/j.cnki.0254-0053.2025.03.018
  • 接收时间:2025-03-18
  • 首发时间:2026-03-24
  • 出版时间:2025-09-25
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  • 收稿日期:2025-03-18
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国家自然科学基金面上项目(11872283)
作者信息
    1.同济大学 航空航天与力学学院,上海 200092
    2.同济大学 汽车学院,上海 201804
    3.同济大学 上海市地面交通工具空气动力与热环境模拟重点实验室,上海 201804
    4.宝山钢铁股份有限公司 中央研究院,上海 201900

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陈硕,博士,教授.研究方向:计算流体力学.E-mail:
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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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