3D lattice Boltzmann simulation of fluid flow in fractured rock

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颗粒沉降的格子Boltzmann模拟与PIV实验验证_张金凤

颗粒沉降的格子Boltzmann模拟与PIV实验验证_张金凤

颗粒沉降的格子Boltzmann 模拟与PIV 实验验证张金凤1,张庆河1,卢 昭2(1.天津大学建筑工程学院暨港口与海洋工程教育部重点实验室,天津 300072;2.中交水运规划设计院,北京 100007)摘要:在格子Boltzmann 方法中引入大涡模拟,对球形颗粒在静水中沉降引起的紊动流场进行了数值模拟。

数值模拟沉速与理论值以及粒子图像测速系统(PIV )实验结果吻合,验证了模型的合理性。

同时分析比较了颗粒沉降过程中尾部紊动流场分布以及尾流流速值,发现数值模拟结果与实测结果趋势、数值基本一致,进一步说明了利用格子Boltzmann 方法与大涡模拟技术相结合可以合理模拟泥沙颗粒在紊流区的沉降。

关 键 词:球形颗粒;沉降;紊流;格子Boltzmann 方法;大涡模拟;粒子图像测速中图分类号:TV142.1 文献标识码:A 文章编号:1001-6791(2009)04-0480-05收稿日期:2008-08-29基金项目:国家自然科学基金资助项目(50779046)作者简介:张金凤(1978-),女,河北张家口人,讲师,博士,主要从事海岸河口水动力及泥沙研究。

E -mail :coastlab @ 泥沙在水体中的沉降规律直接影响着河口、海岸泥沙悬浮、输运以及沉积。

泥沙颗粒在沉降过程中会带动周围的水体,使之发生运动。

当颗粒较小,沉降雷诺数小于0.4时,颗粒周围的水体运动属于层流运动,颗粒沉降满足Stokes 定律。

随着泥沙颗粒增大,沉降雷诺数加大,水流的惯性渐趋重要,水流开始产生分离,并在球体上端造成尾迹,不断产生漩涡,这时沉降颗粒周围的水体运动属于紊流运动[1]。

紊动条件下的球体颗粒沉速已有理论公式进行描述,而泥沙运动轨迹以及周围流场扰动情况还有待进一步研究。

格子Boltzmann (Lattice Boltzmann ,简称LB )方法是从介观尺度对流体力学和水动力问题进行数值模拟的一种新方法,该方法基于微观尺度的特点,已成功地用于模拟泥沙颗粒沉降引起的紊动现象[2-4]。

体外诊断芯片扩散层中液体渗流的格子Boltzmann模拟

体外诊断芯片扩散层中液体渗流的格子Boltzmann模拟

摘要体外诊断芯片扩散层中液体渗流的格子Boltzmann模拟摘要体外诊断芯片携带方便、操作简便,可实现即时检测(point-of-care testing,POCT),具有“快、捷、准”等优点,在“分级诊疗”等政策推动下市场迅速扩容,前景广阔。

体外诊断芯片的快速、高效检测离不开液体在扩散层和试剂层中的有效渗流和分散。

本文尝试采用格子Boltzmann方法模拟研究了液体在体外诊断芯片扩散层中的渗流和分散过程以及在扩散层/试剂层之间的流动过程,可为体外诊断芯片实际应用中扩散层的选型和设计提供基础数据,具有较好的实际应用价值和意义。

以建立的格子Boltzmann方法数学模型为基础,分析控制流动时间的影响因素,结果表明微球粒径、液体的性质和扩散层材料的表面性质是影响液体在体外诊断芯片中渗流的主要影响因素,粒径越大,渗流流动速度越大,适当增加材料润湿性和减小液体运动粘度也会促进渗流的进行。

以上影响因素中,微球粒径对体外诊断芯片中的渗流特性具有较大影响,而材料的表面性质对渗流的影响次之,液体的性质对渗流的影响较小。

通过对约12组格子Boltzmann模型计算结果拟合得到流速与微球粒径、液固接触角及运动粘度的二次线性模型经验式,芯片设计者可根据应用需求利用二次线性模型经验式中计算得到合适扩散层微球材料和粒径。

进一步采用格子Boltzmann方法模拟体外诊断芯片扩散层/试剂层之间液体流动过程,分析液体在试剂层毛细管口处堵塞或顺利流入毛细管的条件,考察扩散层厚度对试剂层中液体流动的影响,并拟合扩散层材料为聚苯乙烯和TiO2时扩散层厚度与管口液滴直径的关系曲线。

结果表明,扩散层厚度过大或过小都不利于液体顺利流入试剂层毛细管。

以液体能够流入毛细管为前提条件,增加扩散层厚度会使液体分散的时间延长,从而减小管口液滴直径和流入毛细管的速度。

模拟得到的拟合曲线经验式,可用于设计者快速的选择扩散层厚度数据。

关键词:体外诊断芯片,渗流,格子Boltzmann方法,数值模拟LATTICE BOLTZMANN SIMULATION ON LIQUID PERCOLATION IN DIFFUSION LAYER OF IN-VITRODIAGNOSTIC CHIPSABSTRACTIn-vitro diagnostic chips are easy to carry and operate,which have the advantages of"fast,convenient,accurate",etc.to realize point of care testing(POCT).Under the promotion of"hierarchical diagnosis and treatment"and other policies,the market is expanding rapidly,and the prospective is widening.The rapid and efficient detection of in-vitro diagnostic chips are inseparable from the effective percolation and dispersion of liquid in diffusion layer and reagent layer.In this paper,the Lattice Boltzmann Method was used to simulating the percolation and dispersion of liquid in diffusion layer of in-vitro diagnostic chips,and the flow process between diffusion layer and reagent layer.Basic data can be provided for the selection and design of diffusion layer in practical application,which has better practical application value and significance.Based on the mathematical model established by the Lattice Boltzmann Method,the influencing factors of controlling flow time were analyzed.The results showed that the microsphere diameter,the properties of liquid and the surface properties of diffusion layer material were the main factors affecting liquid percolation in in-vitro diagnostic chips.The larger the particle diameter is,the faster the liquid percolation is.The wettability of materials was increased,and the kinematic viscosity of liquid was reduced appropriately will also promote the percolation. Among the above factors,the microsphere diameter has a great influence on the percolation characteristics in in-vitro diagnostic chips,while the surface properties of materials have the second influence on the percolation,and the properties of liquid have a little influence on the percolation.By fitting about12groups of the calculation results of Lattice Boltzmann model,the empirical formula of quadratic linear model of the velocity changing with microsphere diameter,liquid-solid contactangle and kinematic viscosity was obtained.The empirical formula of quadratic linear model can be used by chip designers to calculate the appropriate diffusion layer microsphere materials and particle diameters.Furthermore,the liquid flow process between diffusion/reagent layer of in-vitro diagnostic chips was simulated by the Lattice Boltzmann Method.The conditions of liquid blocking at capillary ports or flowing into capillaries in reagent layer were analyzed.The influence of the diffusion layer thicknesses on the liquid flow process in reagent layer was also investigated,and the curve of the relationship between the diffusion layer thicknesses and the droplet diameters at capillary ports was fitted when the diffusion layer materials were polystyrene and TiO2.The results showed that the diffusion layer thickness was not conducive to the smooth flow of liquid into capillaries of reagent layer when the thickness is too large or too small.On the premise that liquid can flows into the capillary,the increasing diffusion layer thicknesses will prolong the dispersion time of liquid and reduce the droplet diameters at capillary ports and the speed of flowing into capillaries.The empirical formula of fitting curve obtained by simulation can be used for designers to quickly select the data of the diffusion layer thicknesses.KEY WORDS:In-vitro diagnostic chips,Percolation,The Lattice Boltzmann Method,Numerical simulation目录第一章绪论 (1)1.1选题背景及意义 (1)1.1.1选题背景 (1)1.1.2研究意义 (2)1.2国内外研究现状 (2)1.2.1体外诊断芯片的结构特征 (2)1.2.2诊断芯片中液体的流动 (5)1.2.3渗流流动特征 (5)1.2.4数值模拟方法 (7)1.2.5格子Boltzmann方法简介 (13)1.3论文主要研究内容 (21)第二章模型的建立与验证 (23)2.1格子Boltzmann方法理论及模型 (23)2.1.1BGK模型 (23)2.1.2渗流模型 (24)2.1.3扩散模型 (26)2.2模拟边界条件的选用 (27)2.3模拟的网格划分方法及无量纲化 (28)2.4格子Boltzmann模型网格划分结果及无关性分析 (29)2.5格子Boltzmann模型的实验验证 (32)2.5.1实验设计 (32)2.5.2实验装置 (33)2.5.3格子Boltzmann模型模拟验证结果 (34)2.6本章小结 (37)第三章体外诊断芯片扩散层中液体渗流的模拟研究 (39)3.1液体在扩散层中的流动 (39)3.1.1水在TiO2扩散层中的流动 (39)3.1.2血清在TiO2扩散层中的流动 (43)3.1.3血清在聚苯乙烯扩散层中的流动 (48)3.2扩散层中液体流动影响因素分析 (53)3.3不同因素对扩散层中液体渗流的影响 (55)3.3.1不同微球粒径对渗流的影响 (55)3.3.2不同粘度的液体对渗流的影响 (61)3.3.3不同材料对渗流的影响 (67)3.4流速回归线方程的拟合及验证 (74)3.5本章小结 (75)第四章体外诊断芯片扩散层/试剂层之间液体流动的模拟研究 (77)4.1液体在扩散层/试剂层之间的流动 (77)4.2影响液体从扩散层流入试剂层的因素 (81)4.3扩散层厚度对试剂层中液体流动的影响 (82)4.4模拟模型的实验验证 (86)4.5本章小结 (88)第五章结论与展望 (89)5.1结论 (89)5.2展望 (90)参考文献 (91)致谢 (97)研究成果及发表的学术论文 (99)作者及导师简介 (101)Contents1Introduction (1)1.1Background and research values (1)1.1.1Background (1)1.1.2Research values (2)1.2Current researches (2)1.2.1Structural characteristics of in-vitro diagnostic chips (2)1.2.2Flow of liquid in diagnostic chips (5)1.2.3Percolation flow characteristics (5)1.2.4Simulation methods (7)1.2.5Brief introduction of the Lattice Boltzmann Method (13)1.3Main research content (21)2Establishment and verification of the model (23)2.1Theory and model of the Lattice Boltzmann Method (23)2.1.1BGK model (23)2.1.2Percolation model (24)2.1.3Diffusion model (26)2.2Selection of simulation boundary conditions (27)2.3Mesh generation methods and dimensionless (28)2.4Mesh generation results and independence analysis of Lattice Boltzmann model (29)2.5Experimental verification of Lattice Boltzmann model (32)2.5.1Experimental design (32)2.5.2Experimental apparatus (33)2.5.3Simulation verification results of Lattice Boltzmann model (34)2.6Summary of this chapter (37)3Simulation of liquid percolation in diffusion layer of chips (39)3.1Flow of liquid in diffusion layer (39)3.1.1Flow of water in TiO2diffusion layer (39)3.1.2Flow of serum in TiO2diffusion layer (43)3.1.3Flow of serum in polystyrene diffusion layer (48)3.2Analysis of factors influenced on the flow of liquid in diffusion layer (53)3.3Effect of different factors on liquid percolation (55)3.3.1Effect of different microsphere diameters on percolation (55)3.3.2Effect of different viscosities of liquid on percolation (61)3.3.3Effect of different materials on percolation (67)3.4Fitting and verification of regression line equation of velocity (74)3.5Summary of this chapter (75)4Simulation of liquid flowing between diffusion/reagent layer of chips (77)4.1Flow of liquid between diffusion/reagent layer (77)4.2Factors affecting liquid flowing into reagent layer (81)4.3Effect of the diffusion layer thickness on liquid flowing in reagent layer (82)4.4Experimental verification of the simulation model (86)4.5Summary of this chapter (88)5Conclusions and Outlook (89)5.1Conclusions (89)5.2Outlook (90)References (91)Acknowledgement (97)Publications (99)About the author and tutor (101)符号说明f k密度分布函数f k eq密度平衡分布函数g k浓度分布函数g k eq浓度平衡分布函数τ无量纲弛豫时间ωk无量纲碰撞频率c k离散速度c s格子声速x水平长度方向的格点位置z水平宽度方向的格点位置y垂直厚度方向的格点位置ψ相互作用势s标记函数Re雷诺数Pe帕克雷特数F k总外力,Nγ表面张力,N/mF达西阻力,Np流体压力,NF t液固相互作用力,NP w液体压力,NP m毛细管作用力,Nε空隙率K绝对渗透率,μm2G材料润湿性参数θ扩散层液固接触角,°θ’试剂层液固接触角,°r l液滴半径,cmν液体运动粘度,m2/sD液体自扩散系数,m2/s u0水平初速度,cm/sv0垂直初速度,cm/sU液体流动无量纲合速度ρ0,1血清初始密度,g/mlρ0,2水初始密度,g/mlC0,1血清初始摩尔含量,molC0,2水初始摩尔含量,molC液体无量纲摩尔含量R液体在芯片表面流动的半径,cm T整个流动过程完成的时间,ms d微球粒径,μmr微球半径,μmt流动时间,msΔt时间步长,msh扩散层厚度,μmh’试剂层厚度,μmr0毛细管管口半径,μmd0毛细管管径,μmDe管口液滴直径,μm第一章绪论1.1选题背景及意义1.1.1选题背景诊断芯片在药物合成筛选、环境检测与保护、临床检验、卫生检疫、司法鉴定、生物检测等领域应用广泛,具有操作简便、反应迅速、无后续处理、无环境污染等优点[1-3]。

格子Boltzmann方法模拟二维轴对称狭窄血管内的脉动流

格子Boltzmann方法模拟二维轴对称狭窄血管内的脉动流

201020446(2) 北京师范大学学报(自然科学版)Journal of Beijing Normal University (Natural Science ) 139 格子Boltzmann 方法模拟二维轴对称狭窄血管内的脉动流3张立换 康秀英 吉驭嫔(北京师范大学物理学系,100875,北京)摘要 将格子Boltzmann 方法应用到二维轴对称余弦狭窄血管模型,模拟比较加入脉动后流场速度、压强和剪切应力分布,并详细分析了不同狭窄模型、Reynolds 数和Womersley 数对血液流动规律的影响,从而为研究血管壁病变和动脉硬化形成机制提供了有用的理论参考.关键词 格子Boltzmann 方法;Reynolds 数;Womersley 数;脉动流;动脉狭窄3北京师范大学青年科学基金资助项目通信作者收稿日期:2009205219 格子Boltzmann 方法(lattice Boltzmann met hod ,简称LBM )是20世纪80年代迅速发展起来的一种新的流体动力学数值模拟方法[122].与以宏观连续方程的离散化为基础的传统数值方法不同,LBM 从微观层次出发,采用统计物理方法得出流体的宏观特性,而且在可操作性方面,它计算方便,编程易于实现,边界易于处理等优点已经得到广泛地证实.由于心血管疾病多集中于具有复杂几何形状和具有复杂流动特性的区域,流动区域和剪切应力的分布对理解、诊断和治疗这种疾病有很重要的作用.近年来,LBM 在血液动力学方面的应用越来越受到重视[326].本文的主要工作是用格子Boltzmann 方法模拟二维轴对称狭窄血管内脉动流的流动特性.首先对狭窄血管内定常流特性进行了研究,模拟比较不同狭窄模型和不同Reynolds 数对管壁切应力、压强和压力梯度分布的影响.然后对二维轴对称狭窄血管内脉动流的流动特性进行了研究,模拟比较在改变Reynolds 数、Womersley 数时动脉血流的流动特性,找到动脉血流的非定常性对狭窄血管中流场速度、压强和剪切应力分布的影响,从而对常见的心血管疾病发展机制给出物理解释,为进一步分析动脉粥样硬化的形成、发展及其影响提供新的研究方法和理论参考.1 二维轴对称狭窄血管内定常流特性的研究111 管壁几何模型 假定血管的狭窄处为轴对称,如图1所示,狭窄形状采用常用的余弦形状,即y =h2[1+co sπL(x -x 0)],(1)图1 二维轴对称余弦狭窄模型 其中h 是狭窄的最大高度,对应于x =x 0处,L 是狭窄总长度的一半,L x 是血管段的长度,L y 是狭窄发生前的血管宽度.112 数值计算 模拟中,计算网格选为N x ×N y =300×40,狭窄中心处为x 0=121,通过调整h 和L 来控制血管狭窄程度.血管出入口采用压强边界条件[7],管壁边界采用Mei 改进的曲线边界条件[8].为了研究不同狭窄情况下管壁的切应力、压强和压强梯度的变化规律,我们选择3个不同的狭窄模型,如表1.表1 不同的狭窄模型狭窄模型M1M2M3狭窄高度h L y /8L y /4L y /4狭窄长度2L16h 8h 16h 在保证Reynolds 数(Re =ρUL y μ=UL yν,ν=μ/ρ为流体运动学黏滞系数,U 为入口附近的平均速度)一定时,计算得3种模型管壁切应力、压强和压强梯度见 140 北京师范大学学报(自然科学版)第46卷 图2~4.Re =114,狭窄中心x 0=121.图2 3种狭窄模型下管壁切应力分布 从图2中可以看出,管壁切应力振荡的负峰值在靠近狭窄中心(x 0=121)的上游,这个峰值达到一定值后,该部位血管内皮组织易发生机械应力损伤.当狭窄长度一定时,狭窄高度越大,切应力的负峰值越大,如图2中的M1和M2;当狭窄高度一定时,狭窄长度越短,切应力的负峰值越大,如图2中的M2和M3.同时也可以看出在狭窄处的下游切应力变小,特别是M2,血液容易在此处发生流体分离.模拟得到狭窄区域的压强和压强梯度分布如图3和4所示.在相同狭窄长度下,狭窄高度越大,血管狭窄上游压强下降越大,下游压强上升越大,同时狭窄区域前后的压强落差越大,如图3中的M1和M2.另一方面,在相同狭窄高度下,狭窄长度越长,血管狭窄上游压强下降越大,同时狭窄区域前后的压强落差越大,如图3中的M2和M3.压强梯度在狭窄区域波动加图3 管壁上压强分布(Re =114),p 0是狭窄发生前的压强,u 0是x =20处的中心流速 图4 管壁上的压强梯度分布(Re =114) 剧,压强梯度波动最大的是狭窄模型M2(图4),其对应的切应力负峰值也为最大值,狭窄部位管壁切应力与压强梯度的变化规律具有相似性.选择模型M2,比较管壁切应力和狭窄附近的流场分布随Re 的变化规律,如图5和6.从图5中可以看出,狭窄模型一定时,随着Re 的增加,管壁切应力增大,在狭窄区域的下游,切应力的增加相对减小,这是由于出现了流体分离,如图6的流场分布.图6显示了模型M2在不同Re 下狭窄附近的流场分布,可以看出,随着Re 的增大,在狭窄下游管壁处出现流动分离区,且Re 越大,流动分离区越大.113 分析与结论 通过改变参数,我们获得了大量有关狭窄血管中的流场的信息.模拟结果表明,血管局部图5 管壁切应力随Reynolds 数的变化曲线(狭窄模型M2) 第2期张立换等:格子Boltzmann方法模拟二维轴对称狭窄血管内的脉动流141图6 不同Re下的流场分布(M2,Re=114、215、318)狭窄会对血液的流动状态产生明显的影响,从而带来一系列的生理和病理方面的复杂变化.例如,动脉硬化斑块主要发生在几何形状急剧变化和高Re流动状态的血管内.在动脉硬化斑块发展的初期,血管狭窄度比较小,对于黏度是常数的血液流体,其Re比较小,无流动分离,管壁切应力可能达到临界应力值,对狭窄上游血管壁内皮细胞造成损伤,使壁面进一步异常增生,导致血管狭窄度增加,进而导致此处流动Re的增加.当血管狭窄增大到一定值时,在狭窄下游管壁附近就会有流动分离区形成,在该区域内血液会发生滞留,血液中的血小板和纤维蛋白就会沉积,并在血管壁处形成网络结构致使血液中的脂质颗粒沉积,而最终导致动脉粥样硬化现象的出现.同时,狭窄度较大时,对应的压力梯度的值也会较大,也可以反映病变血管的异常血液流动情况.2 二维轴对称狭窄血管内脉动流的流动特性选择模型M2为研究对象,模拟中选取周期T=10000,流动的Womersley数(α=L y2ων,ω=2πf=2π/T是脉动的角频率)为α=31357,入口压强随时间周期性变化,即p(0,t)=Δp cosωt+p out,Δp为一常量,出口压强pout设为定值,图7显示一个周期8个不同时刻的脉动流管道中心中轴线上的压强分布.从图7中可以看出,中轴线上的压强不是线性变化,在靠近狭窄部位压强下降幅度明显增加,在最大狭窄处附近压强出现极小值,狭窄下游压强又逐渐回升,远离狭窄后,压强变化逐渐恢复类直管变化趋势,并且压强随时间的波动存在一定的滞后,如图中1/8T和7/8T,2/8T和6/8T以及3/8T和5/8T不完全重合.狭窄中心x0=121,狭窄长度为78.图7 iT/8时刻中轴线上的压强分布 142 北京师范大学学报(自然科学版)第46卷 脉动流前半周期的流场分布如图8所示.从图中可以看出,在T/4时刻,在狭窄下游管壁附近开始出现流动分离区,且分离区逐渐扩大,如3T/8时刻,接着又缓慢消失,如T/2时刻,流体平滑地流过凸包.图8 脉动流在前半周期内不同时刻的流场分布 需要注意的是心脏的周期性泵血作用使动脉中的血液以脉动的形式流动,动脉中血液流动的参量———压强、流量等流动参数也会随时间变化,虽然动脉中血液的流动是脉动流而不是定常流,但动脉中血流的方向平均来说却是始终不变的,即总是从动脉流向毛细血管,再流向静脉.因此,可以把由心脏收缩和舒张所引起的动脉中的脉动流看作是一定常流分量与一振荡分量的叠加,即在图8所示的流场分布中叠加上一个定常流,最终倒流的出现时间将非常短暂,且流速很小.对应于一个周期中的不同时刻,我们发现,管壁切应力的随时间的波动也存在一定的滞后.如图9给出前半周期的切应力分布.3 结束语我们讨论了二维余弦狭窄血管中血液流动的切应力、流场速度、压强和压强梯度在不同狭窄模型和不同图9 前半周期内管壁切应力的变化曲线Re下的分布规律,所得结论与用其他实验,理论和数值模拟得到的结论相同[9211],但用LBM方法编程简单,参数易于选择,从分布函数就可以得到所有主要宏 第2期张立换等:格子Boltzmann方法模拟二维轴对称狭窄血管内的脉动流143观量,证实了LBM在此模型下的适用性.考虑到血液流动的脉动性,研究了一个脉动周期中流场的变化特点,并与定常流动比较,分析其差异.由于Womersley数的选择在血流参数范围内,故认为上述结论具有参考性.值得注意的是,流动分离区并不同于定常流动所述那样在管壁处停留,而是随着时间的演化,流动分离区间歇性的出现,如对α=710797的流场分布模拟显示,与α=31357的不同点是流动分离区在管壁附近产生后,随着时间的推移,又会向管轴附近发展.与定常流情况下在Re达到300后才出现明显的分离区不同,对于脉动流,在Re较小时,就已经可以观察到明显的流动分离区了.4 参考文献[1] Qian Y H,d’Humieres D,lallemand ttice B GKmodels for Navier2Stokes equation[J].Europhys Lett, 1992,17:479[2] Chen H,Chen S,Matthaeus W H.Recovery of theNavier2Stokes using a lattice2gas Boltzmann method[J].Phys Rev A,1992,45:R5339[3] Artoli A M,Kandhai D,Hoef sloot H ttice B GKsimulations of flow in a symmetric bif urcation[J].FutureG eneration Computer Systems,2004,20:909[4] Boyd J,Buick J,Cosgrove J A,et al.Application of thelattice Boltzmann model to simulated stenosis growth in a two2dimensional carotid artery[J].Phys Med Biol,2005, 50:4783[5] Li H B,Fang H P,Lin Z ttice Boltzmannsimulation on particle suspensions in a two2dimensional symmetric stenotic artery[J].Phys Rev E,2004,69: 031919[6] 康秀英,刘大禾,周静,等.用格子Boltzmann方法模拟动脉分叉流场[J].北京师范大学学报:自然科学版,2005, 41(4):364[7] Z ou Q,He X.On pressure amd velocity boundaryconditions for the lattice Boltzmann B GK model[J].Phys Fluids,1997,9(6):1591[8] Mei R,L uo L S,L uo Shyy W.An accurate curvedboundary treatment in the lattice Boltzmann method[J].J Comput Phys,1999,155:307[9] 姚力,李大治.刚性轴对称狭窄血管内压强及其梯度的研究[J].应用数学和力学,2006,27(3):311[10] 刘国涛,王先菊,艾保全,等.狭窄动脉血管中Poiseuille流动对管壁切应力的影响[J].中山大学学报:自然科学版,2004,4(6):29[11] 秦杰,刘辉,孙利众,等.刚性狭窄管内血流压力分布的研究[J].生物力学,1989,4(6);57SIMU LATING B LOOD FLOW IN A TWO2DIMENSIONALSYMMETRIC STENOTIC ARTER Y BYTHE LATTICE BOL TZMANN METH ODZHAN G Lihuan KAN G Xiuying J I Yupin(Depart ment of Physics,Beijing Normal University,100875,Beijing,China)Abstract In t his st udy t he lattice Boltzmann met hod has been applied to a two2dimensional symmet ric stenotic artery.The velocity,p ressure and shear st ress distribution of blood flow were simulated and compared when p ulsatio n over t he blood was added.We have observed t he impact of blood flow when changing t he steno sis struct ure,Reynolds number and Womersley number.These data provide a p hysical explanation for blood vessel lesions and arterio sclero sis.K ey w ords lattice Boltzmann met hod;Reynolds number;Womersley number;p ulsating blood;steno sed artery。

关于两种REV尺度多孔介质LBM模型的讨论

关于两种REV尺度多孔介质LBM模型的讨论

关于两种REV尺度多孔介质LBM模型的讨论雷鸣【摘要】通过多尺度展开方法分析了Freed和Guo分别提出的两种多孔介质LBM模型对应的宏观方程.分析表明,两种模型均存在一定的人工多余项,相较而言Guo模型更加精确.对于稳态不可压流动来讲,两种模型基本等价.通过数值模拟证明了这一结论.%Multi-scale expansion was used to analyze the macroscopic equation of two LBM models for flow in porous media, which were introduced by Freed and Guo et. al . separately. It is shown that both the macroscopic equations of the two models involve certain artificial parts compared with governing equation of fluid flow in porous media. Moreover, Guo's model is more precise. The two models are equivalent when dealing with steady state, uncompressible flow. This is also demonstrated through numerical modeling.【期刊名称】《科学技术与工程》【年(卷),期】2011(011)020【总页数】7页(P4814-4820)【关键词】多孔介质;格子玻尔兹曼方法;多尺度展开【作者】雷鸣【作者单位】北京大学工学院能源与资源工程系,北京,100871【正文语种】中文【中图分类】V211.3格子玻尔兹曼方法(Lattice Boltzmann Method,简称为LBM),作为一种数值计算方法,由于其边界处理及并行计算等方面的优势,目前已经被广泛应用于多个领域,包括:(a)互溶/互不相溶两相流模拟[1—4];(b)热效应[5];(c)层流,湍流模拟[6];(d)复杂边界以及动边界[7,8]等等。

3D格子Boltzmann传质模型模拟生物膜降解有机污水

3D格子Boltzmann传质模型模拟生物膜降解有机污水

3D格子Boltzmann传质模型模拟生物膜降解有机污水杨艳霞;李静【期刊名称】《农业工程学报》【年(卷),期】2018(034)010【摘要】Biological treatment has been proven as an efficient wastewater treatment technology and widely used in the processes of city sewage and industrial waste. Biomembrane is aggregates of microorganisms suspended in a matrix of extracellular polymeric substances. Especially, photosynthetic bacteria (PSB) have exhibited significant superiorities to degrade the organic compounds in wastewater through utilizing solar energy, and simultaneously generate hydrogen energy, which is considered as a promising candidate due to its advantages of high energy content, high stability of combustion, cleanness and high efficiency. Biofilm is the foundation of biological membrane processing for wastewater treatment systems. In fact, the structure of biofilm has been proven to be a porous membrane, and thereby the degradation process could be considered as bioreaction in a bioreactor with porous media. Recently, numerous bioreactors and experiments have been proposed and implemented with the aim to improve the stability of reactors and performance of hydrogen production. Except experimental study, many theoretical studies have been carried out, and some numerical models have been established to investigate the bioreaction and two-phase flowtransport in the bioreactors. Noteworthily, these numerical models are generally based on macro-scale, and require solving the partial difference equations for complex system. Moreover, they are still quite limited to obtain the detail information of fluid flow and mass transport in the biofilm, and also have difficulties in treating complex geometry of biofilm. Therefore, it is necessary to carry out a further numerical study on the flow and mass transport in bioreactor to overcome the limitations. In present study, a lattice Boltzmann method (LBM) was adopted to simulate the biodegradation in the bioreactor. Unlike the conventional numerical methods based on macroscopic continuum equations, the LBM was a mesoscopic approach that incorporates the essential physics of microscopic or mesoscopic process. Lattice Boltzmann models were based on microscopic kinetic equation for the particle distribution function, and the macroscopic quantities were then obtained through moment integrations of the distribution function. The lattice Boltzmann method has the most distinguished advantages, such as the simplicity of algorithm, the flexibility for complex geometries and parallel computing. Therefore, the flow and mass transfer as well as bioreaction were simulated with 3D lattice Boltzmann model. Moreover, the detailed porous structure of biofilm was generated by quartet structure generation set (QSGS) method, which was closely combined with lattice Boltzmann model. In the simulation, the lattice Boltzmann model was coupled with a multi-block scheme to improve the computational efficiency and accuracy, and the non-equilibrium extrapolation method was used for velocity andconcentration boundary condition treatment. The effect of porosity and pore structure of biofilm on flow and mass transfer was investigated, and the simulation results were compared with the experimental data, validated the LB model. The simulation results indicated that with the increasing biofilm porosity, the substrate consumption efficiency increased and reached the maximum of 50.97% at porosity of 0.5, then decreased under the condition of the same growth probability on every discrete direction; different growth probabilities would lead to the biofilm with various pore structures and specific surface areas, and thereby affect the performance of membrane bioreactor, and the substrate consumption efficiency was highest, 52.54%, under the condition of biofilm with structure 1 (p3-4=0.01,p1,2,5-14=0.005) at ε=0.5, indicating that this characteristics of porous biofilm is optimal for bioreaction.%以膜生物法有机污水处理为研究背景,将3D格子Boltzmann传质模型与多孔介质四参数随机生成法耦合,获得生物膜多孔介质详细的孔隙分布,进而对反应器内生化降解反应过程进行模拟计算.研究分析了生物膜孔隙率及孔隙分布对流动传质及生化反应性能的影响,并与试验结果比较证明了模型的可行性.结果表明:各方向生长概率p1-14=0.005,随着生物膜孔隙率增大,反应器内底物降解效率先增大后减小,且在孔隙率ε=0.5时达到最大,50.97%;孔隙率ε=0.5时,改变各方向生长概率重构获得5种不同结构生物膜,其降解效率随之改变,生物膜为结构1(p3-4=0.01,p1,2,5-14=0.005)时,底物降解效率最高,52.54%.因此,3D格子Boltzmann传质模型可用于膜生物反应器内的流动传质及生化反应过程的模拟,研究结果将对反应器的优化具有一定的指导作用.【总页数】6页(P225-230)【作者】杨艳霞;李静【作者单位】太原理工大学热能工程系,太原 030024;低品位能源利用技术及系统教育部重点实验室,重庆大学,重庆 400044;太原理工大学建筑与能源应用工程系,太原 030024【正文语种】中文【中图分类】TK91【相关文献】1.基于竹丝填料的生物膜法处理难降解且低C/N污水的研究 [J], 孙俊伟;何争光;曹文平;李蕾;闫晓乐;贾胜勇2.高级氧化法提高难降解有机污水生物降解性能的研究新进展 [J], 杨瑞彩3.外电场对有机物在生物膜内扩散传质的影响 [J], 王大为;姜斌;李天成;袁绍军;李鑫钢4.加压固定床生物膜反应器降解污水中有机物的研究 [J], 张诗华;郑俊;王健5.好氧流化床生物膜反应器中氧传质过程与污水处理效能研究 [J], 佘国生;何梦夏;段景川;田蕾;王敏因版权原因,仅展示原文概要,查看原文内容请购买。

Lattice-Boltzmann Simulations of Fluid Flows in MEMS

Lattice-Boltzmann Simulations of Fluid Flows in MEMS

a r X i v :c o m p -g a s /9806001v 1 11 J u n 1998Lattice-Boltzmann Simulations of Fluid Flows in MEMSXiaobo Nie 1,Gary D.Doolen 1and Shiyi Chen 1,21Center for Nonlinear Studies and Theoretical Division,Los Alamos National Laboratory,Los Alamos,NM 875452IBM Research Division,T.J.Watson Research Center,P.O.Box 218,Yorktown Heights,NY 10598The lattice Boltzmann model is a simplified kinetic method based on the particle distribution function.We use this method to simulate problems in MEMS,in which the velocity slip near the wall plays an important role.It is demonstrated that the lattice Boltzmann method can capture the fundamental behavior in micro-channel flow,including velocity slip and nonlinear pressure drop along the channel.The Knudsen number dependence of the position of the vortex center and the pressure contour in micro-cavity flows is also demonstrated.The development of technologies in Micro-electro-mechanical systems (MEMS)has motivated the study of fluid flows in devices with micro-scale geometries,such as micro-channel and micro-cavity flows [1].In these flows,the molecular mean free path of fluid molecules could be the same order as the typical geometric length of the device;then the continuum hypothesis which is the fun-damental for the Navier-Stokes equation breaks down.An important feature in these flows is the emergence of a slip velocity at the flow boundary,which strongly affects the mass and heat transfer in the system.In micro-channel experiments,it has been observed that the measured mass flow rate is higher than that based on a non-slip boundary condition [2].The Knudsen number,K n =l/L ,can be used to identify the influence of the effects of the mean free path on these flows,where l is the mean free path of molecules and L is the typical length of the flow domain.It has been pointed out that for a sys-tem with K n <0.001,the fluid flow can be treated as con-tinuum.For K n >10the system can be considered as a free-molecular flow.The fluid flow for 0.001<K n <10,which often appears in the MEMS [1],can not be con-sidered as a continuum nor a free-molecular flow.Tradi-tional kinetic methods,such as molecular dynamics sim-ulations [3]and the continuum Boltzmann equation ap-proach,could be used to describe these flows.But these methods are more complicated than schemes usually used for continuum hydrodynamic equations.The solution of the Navier-Stokes equation including the velocity-slip boundary condition with a variable parameter has also been used to simulate micro-channel flows [4].In the past ten years,the lattice Boltzmann method (LBM)[5]has emerged as an alternative numerical tech-nique for simulating fluid flows.This method solves a simplified Boltzmann equation on a discretized lattice.The solution of the lattice Boltzmann equation converges to the Navier-Stokes solution in the continuum limit (small Knudsen number).In addition,since the lattice Boltzmann method is intrinsically kinetic,it can be also used to simulate fluid flows with high Knudsen numbers,including fluid flows in very small MEMS.To demonstrate the utility of the LBM,we use the LBM model with three speeds and nine velocities on a two-dimensional square lattice.The velocities,c i ,in-clude eight moving velocities along the links of the squarelattice and a zero velocity for the rest particle.They are:(±1,0),(0,±1),(±1,±1),(0,0).Let f i (x ,t )be the dis-tribution functions at x ,t with velocity c i .The lattice Boltzmann equation with the BGK collision approxima-tion [6,7]can be written asf i (x +c i δx,t +δt )−f i (x ,t )=−τ−1(f i −f eq i ),(1)where f eq i (i =0,1,···,8)is the equilibrium distribution function and τis the relaxation time.We have assumed that the spatial separation of the lattice is δx and the time step is δt .A suitable equilibrium distribution is [7]:f eqi =t i ρ 1+c iαu α2c 4su αu β.(2)Here c s =1/√2+12).Using the Chapman-Enskog multi-scale expansion tech-nique,we obtain the following Navier-Stokes equations in the limit of long wavelength and low Mach number:∂t ρ+∂α(ρu α)=0,(3)∂t (ρu α)+∂β(ρu αu β)=−∂αP −∂βπαβ,(4)P =c 2s ρ,παβ=ν(∂α(ρu β)+∂β(ρu α)),where ν=c 2s (2τ−1)/(2ρ)is the kinematic viscosity.In classical kinetic theory,the viscosity νfor a hard sphere gas is linearly proportional to the mean free path.Sim-ilarly,we define the mean free path l in the LBM as:a (τ−0.5)/ρ,where a is constant.Our first numerical example is a micro-channel flow [2].The flow is contained between two parallel plates sepa-rated by a distance H and driven by the pressure differ-ence between the inlet pressure,P i ,and exit pressure,P e .The channel length in the longitudinal direction is L .We12take L =1000,H =10(lattice units)in our simulations satisfying L/H >>1.The bounce-back boundary condi-tion is used for the particle distribution functions at the top and bottom plates,i.e.,when a particle distribution hits a wall node,the particle distribution scatters back to the fluid node opposite to its incoming direction.A pressure boundary condition is used at the input and the exit.2468101214161800.10.20.30.40.50.60.70.8V s , M fK nV s M fFIG.1.The slip velocity and the normalized mass flow rate at the exit of a micro-channel flow as functions of K n for P i /P e =2.The ‘+’and ‘×’are LBM numerical results.Thedashedand dottedlinesare Eq.(5)and Eq.(6)respectively.The slip velocity V s at the exit of the micro-channel flow is defined as:u (y )=u 0(Y −Y 2+V s ),where u (y )is the velocity along the x (or flow)direction at the exit and Y =y/H .u 0and V s can be obtained by fitting numerical results using the least squares method.This definition of the slip velocity is consistent with others [2,4].In Fig.1,we plot the slip velocity V s and the normalized mass flow rate M f =M/M 0,as functions of Knudsen number when the pressure ratio η=P i /P e =2.The normalization fac-tor,M 0=h 3P eη2−1.(6)For η=2,the above formula becomes M f =1+24.1K 2n ,which agrees well with the numerical results in Fig 1.In laminar Poiseuille flows,one usually assumes that the density variation along the channel is very small,and the pressure drop along the channel is nearly linear.Inmicro-channel flow,however,the ratio between the length and the width is much larger and the pressure drop is not linear.If there is no velocity slip at the walls,it has been shown [2,4]from the Navier-Stokes equation that the pressure along the channel has the following depen-dence on the dimensionless coordinate,X =x/L :P 2=P 2e [1+(η2−1)(1−X )],(7)If the velocity at the boundaries is allowed to slip,the pressure drop along the channel will depend on the Knud-sen number.In Fig.2we present the LBM simulation re-sults for the normalized pressure deviation from a linear pressure drop,(P −P l )/P e ,as functions of X for several Knudsen numbers,where P l =P e +(P i −P e )(1−X ).It is seen that when K n ≤0.2,(P −P l )/P e is a positive nonlinear function of X .This agrees with the results in [4]using an engineering model.For K n ≥0.2,the LBM simulation shows that (P −P l )/P e becomes neg-ative,which is directly linked to the fact that the slip velocity depends on the square of K n in the LBM.For large K n ,the pressure can be derived from Eq.(6):P =P e [η(1−X )].(8)The negative deviation from a linear pressure drop has not been experimentally observed before and it would be interesting to testify this experimentally.-0.1-0.08-0.06-0.04-0.0200.020.040.060.080.100.20.40.60.81(P -P l )/P eX Eq.(7)K n =0.0194K n =0.134K n =0.194K n =0.388K n =0.776Eq.(8)FIG. 2.The deviations from linear pressure drop for η=P i /P e =2.The top and bottom lines are the analyt-ical results from Eq.(7)for K n =0and Eq.(8)for K n >>1respectively.The other curves are LBM numerical results for the Knudsen numbers indicated.In Fig.3the mass flow rates as functions of the pres-sure ratio ηwhen K n =0.165are shown for our theory,the experimental work [2],the engineering model [4]and the LBM simulation.Our theory and the LBM simula-tion agree well with the experimental measurements.It is noted that for large pressure ratios (η≥1.8),the LBM agrees reasonably well with Beskok et al.[4].But for smaller pressure ratios,the difference increases because of different dependence of the slip velocity on K n .31.41.61.822.22.41 1.2 1.4 1.6 1.82 2.2 2.4 2.6M fηtheory simulation experiment[4]FIG.3.The normalized mass flow rate as a function of the pressure ratio for Kn =0.165.The theory is Eq.(6).010*******102030yx K n =0.0048510203040xK n =0.388FIG.4.Streamlines for two Knudsen numbers.ym a s s f l u xK nFIG.5.The y -coordinate of the vortex center (square sym-bols)and the mass flow(solid circles)as a function of K n .Our second LBM numerical simulation is the two-dimensional micro-cavity flow.The cavity size is L x =L y =40(lattice units).The upper wall moves with a constant velocity,v 0,from left to right.The other three walls are at rest,and bounce-back boundary conditions are used.To see the dependence on the Knudsen num-ber in our simulations,we fixed the Reynolds number,R e =v 0L xc s ≤10−3.In Fig.4,we show the streamlines for two different Knudsen numbers.In Fig.5we show the vertical positions of the vortex center and the mass flux between the bottom and the vortex cen-ter as functions of K n .It can be seen that the center moves upward and the mass flow decreases with increas-ing Knudsen number.This occurs because the slip ve-locity on the upper wall causes momentum transfer to be less efficient.It has been shown [8]that the center of the vortex moves downward when the Reynolds number increases for very small K n .Fig.6shows the pressure contours for the same parameters as in Fig.4.Totally different pressure structures are observed for these two cases.When the Knudsen number is small,the contin-uum assumption is valid and the pressure contours are almost circles with centers at the left or the right corners.On the other hand,due to the slip velocity on the walls,the pressure contours become nearly straight lines at the higher Knudsen number.102030400102030yx K n =0.000485010203040xK n =0.388FIG.6.The contours of pressure for the same two Knudsen numbers shown in Fig.4.。

封闭方腔自然对流的格子-Boltzmann方法动态模拟

封闭方腔自然对流的格子-Boltzmann方法动态模拟

4.504 4.519 4.510 4.510 0.199%
8.767 8.800 8.806 8.805 0.056%
从表 1 中可以发现,采用本文所介绍的不可压缩双分布函数 TLBM 模型进行数值计算,得 到了比较精确的结果。相对误差
5. 方腔内自然对流的动态模拟
封闭方腔自然对流是热流耦合的经典问题,通过对其进行数值模拟而获得不同 Ra 情况
2. 物理模型
本文所计算的封闭方腔自然对流的物理模型如图 1 所示。封闭方腔高为 H ,上、下壁
1
本课题得到国家杰出青年科学基金资助项目(50425620)及高等学校博士学科点专项科研基金资助项目 -1(20050698036)资助。
Th + Tc ⎞ 面绝热,腔内充满 ρ = 3 , Pr = 0.71 ,温度 T = ⎛ ⎜ ⎟ 的均质 ⎝ 2 ⎠
p i x + ei dt , t + dt − p x, t = −
(
) ( )
dtτ p Fi dt p i − pieq + τ p + 0.5dt τ p + 0.5dt
(
)
(6)
g i x + ei dt , t + dt − g x, t = −
(
) ( )
p dt dt g i − gieq − Z i 2i τ g + 0.5dt τ g + 0.5dt cs
(
)
(7)
图 2. D2Q9 模型
。 其中 τ p ,τ g 分别为运动和热方程的松弛时间; cs 为声速( cs = 1/ 3 ) 流体的宏观参量(包括压力,速度,温度及热流等)可按下列各式计算:

格子-Boltzmann方法及其在常规与微尺度对流换热模拟中的应用

格子-Boltzmann方法及其在常规与微尺度对流换热模拟中的应用

υm υr υ r'
V x X y Y ∆x ∆t
希腊字母
α θ
Θ
体胀系数,K 无量纲温度 运动粘度,m /s 密度,kg/m
3 3 2 -1
平面角,rad
ν ρ ρ0 λ τe τm dτ dτ v γ σ σv τ
dΩ ∇
参考密度,kg/m 内能驰豫时间 动量驰豫时间 体积微元 速度间隔 比热容率 分子直径,m 滑移系数
分子的平均自由程,m
驰豫时间,碰撞间隔 立体角,sr 哈密顿算子
-V-
西安交通大学硕士学位论文
特征数
Nu Nu Kn Ra Re Ma Pr
Nusselt 数, hl 平均 Nusselt 数 Knudsen 数, λ l ( λ 为分子的平均自由程) Rayleigh 数, gl Reynolds 数, vl
2 3 2 2
2
西安交通大学硕士学位论文
v g v v'
分子速度矢量,m/s 加速度,m/s
2
碰撞后粒子的速度,m/s 分子平均速度,m/s 两粒子碰撞前的相对速度,m/s 两粒子碰撞后的相对速度,m/s 沿 y 方向的无量纲速度 笛卡尔坐标,m 无量纲坐标 笛卡尔坐标,m 无量纲坐标 格子步长 时间步长
1.1 1.2 1.3
绪论………………………………………………………1
研究背景及意义………………………………………………1 文献综述………………………………………………………2 本文所做的工作………………………………………………9
2
2.1 2.2 2.3 2.4
格子-Boltzmann 方法理论基础………………………11
统计物理学概述………………………………………………11 Boltzmann 方程的简单推导…………………………………12 格子自动机的基本原理 ………………………………………18 碰撞间隔理论与 LBGK 模型…………………………………18

基于Lattice Boltzmann方法的圆柱绕流大涡模拟

基于Lattice Boltzmann方法的圆柱绕流大涡模拟

K e w o ds La tc lz a n m e hod a g d i u a i n;s g i o l y r t ie Bo t m n t ;l r e e dy sm l to ub rd m de
1 引 言
高 R y od数 的 圆柱绕 流 是 数值 模 拟 的一 个 典 enl 型对 象。 常用 的时 间平 均 难 以有 效 模 拟 这类 流 动, 直 接数 值 模拟 也 因 巨大 的 计算 量 和存 储 量 n难 以实 i f 现 。 于两者 之 间 的大 涡模 拟 ( E ) 介 L S 为模 拟这类 流 动
法 J L 。 B方 法 具有 许 多独 特 的优 势, 吸引 了众 多 的学 者 对 其研 究 ,在 众 多领 域 得 到 了成 功 的应 用 。
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G U O Zha - ZH EN G o・ Li Chu・ u ng LI Zha - ui - a G U o・ H
( a in l a o aoy o o l o u t n Hu z o g U i ri N t a b r tr f a C mb si , a h n nv s y o L C o e t o in ea d T c n lg , Wu a 3 0 4 C i a f ce c n e h oo y S h n 4 0 7 , hn )

Lattice Boltzmann Method for Fluid Simulations

Lattice Boltzmann Method for Fluid Simulations
8
(4)
The key steps in LBM are the streaming and collision processes which are given by fi (~ x + c~ ei t, t + | {z t) fi (~ x, t) } [fi (~ x, t) | fieq (~ x, t)] ⌧ {z } (5)
where si (~ u) is defined as si (~ u) and wi , the weights, wi = 8 < 4/9 i = 0 1/9 i = 1, 2, 3, 4 : 1/36 i = 5, 6, 7, 8 (8) = wi 3 ~ ei · ~ u 9 (~ ei · ~ u) 2 + c 2 c2 3~ u·~ u , 2 2 c (7)
8 X i=0
fi (~ x, t)
(3)
Accordingly, the macroscopic velocity ~ u(~ x, t) is an average of microscopic velocities ~ ei weighted by the distribution functions fi , ~ u(~ x, t) = 1X cfi~ ei ⇢ i=0 =
1
Figure 1: Illustration of a lattice node of the D2Q9 model The macroscopic fluid density can be defined as a summation of microscopic particle distribution function, ⇢(~ x, t) =
3

二维剪切流的格子Boltzmann模拟

二维剪切流的格子Boltzmann模拟

A 辑第18 卷第5 期水动力学研究与进展Ser. A , Vol. 18 ,No. 52003 年9 月J OURNA L OF H YDROD YNAM ICS Sep. , 2003文章编号:100024874 (2003) 0520521205二维剪切流的格子Boltzmann 模拟闫广武(吉林大学数学学院,吉林长春130012)摘要: 本文给出了格子Boltzmann 方法收敛到Navier2Stokes 方程严格的证明过程。

之后, 我们应用标准的格子Boltzmann 方法模拟二维剪切层流动中的Kelvin2Helmholtz 不稳定问题。

结果表明,剪切流动的不稳定情况可以在无表面张力的模拟中得到。

关键词: 格子Boltzmann 方法;剪切流; Kelvin2Helmholtz 不稳定中图分类号: O351. 3文献标识码:ALattice Boltzmann simulation of the t wo2dim ensionalshear flo wsYAN Guang2wu(Department of Mathematics , Jilin Univers ity , Changchun 130012 , China)Abstract : The basic idea of the lattice Boltzmann method and the skills of finding the Navier2Stokes equations are given. We simulate the Kelvin2Helmholtz instability in the two2dimensional shear flow by using the model. The result shows that the lattice Boltzmann model can be used to simulate the shear flow very well.Key words : Lattice Boltzmann method ; shear flows ; Kelvin2Helmholtz instability1 引言从1992 年起,格子Boltzmann 方法作为一种计算流体力学的方法得到了很大的发展,人们已将这种方法用于各个流体力学的研究领域[ 1~4 ] 。

一类三阶变系数偏微分方程的格子Boltzmann模型

一类三阶变系数偏微分方程的格子Boltzmann模型
武 芳 芳 ,王 可 心
(沈阳工业大学 理学院,沈阳 110870)
摘要:用 格 子 Boltzmann 方 法 求 解 一 类 具 有 变 系 数 和 源 项 的 三 阶 偏 微 分 方 程. 利 用 Chapman-Enskog展开技术,通过选取适当的 平 衡 态 分 布 函 数 和 补 偿 函 数,恢 复 出 具 有 三 阶 精 度 的 宏 观 方 程 .数 值 模 拟 结 果 验 证 了 该 模 型 的 有 效 性 . 关键词:格子 Boltzmann方法;Chapman-Enskog展开;变系数;三阶偏微分方程 中图分类号:O241.82 文献标志码:A 文章编号:1671-5489(2021)04-0763-06
方程的数值解,例如有限元法、有限差分法和谱方法等 .本 [11-13] 文给出求解变系数偏微分方程(1)的高
精度格子 Boltzmann模型,并通过数值算例验证该模型的有效性和精度.
1 格子 Boltzmann模型
对方程(1)采用 D1Q5速度模 型,其 离 散 速 度 集 合 为 {e0,e1,e2,e3,e4}= {0,1,-1,2,-2}.格 子 Boltzmann模型 的 [14] 局部粒子分布函数演化方程为
的 初 边 值 条 件 由 算 例 方 程 的 精 确 解 确 定 ,初 始 的 粒 子 分 布 函 数 直 接 等 于 平 衡 态 分 布 函 数 ,边 界 处 理 采
用非平衡态外推格式 . [16]
例1 考虑方程(1),当a(t)=b(t)=0,c(t)=6cos(2t),d(t)=cos(2t),n=1,F(u)=0 时 方 程
(3)
3)当a(t)=0,b(t)=-3β,c(t)=2α,d(t)=1,n=1,p=1,F(u)=0时,方程(1)为 Gardner方程:

LBM方法模拟多孔介质流动与传热问题

LBM方法模拟多孔介质流动与传热问题

(r,t )
(2)
其中 δt 为时间步长,ei 为格子离散速度。τf 为密度分布计算的弛豫时间,τg 为温度密度函数计算的弛豫时 间。宏观 温度、 热流密 度可通 过以下 公式获 得:
8
T = ∑ Tα
(3)
α =0
∑ q =
i
eiTi
τT
− 0.5δ t τT
ρcp
(4)
其中 ρcp 为相应的物性参数,τT 为温度分布弛豫时间,δt 为时间步长。同时在得到稳态条件下热流密度的 情况下, 根据傅 里叶导 热定律 ,可以 获得有 效导热 系数的 计算 公式:
Received: Feb. 21st, 2019; accepted: Mar. 8th, 2019; published: Mar. 15th, 2019
Abstract
In this paper, a digital model of porous media is obtained by four-parameter stochastic generation method. And an improved lattice Boltzmann method is used to simulate the flow and heat transfer problems in this porous media. The distribution of velocity and temperature is obtained at different porosity. On the basis of the results, the effective thermal conductivity of porous media is calculated. And the value is affected by the flow state in porous media. A reliable simulation method for study flow and heat transfer in porous media can be obtained in this paper.

求解辐射传输方程的多松弛格子-Boltzmann模型

求解辐射传输方程的多松弛格子-Boltzmann模型

第41卷第1期东北电力大学学报Vol.41,H 2021年2月Journal Of Northeast Electric Power University Feb,2021D O I: 10. 19718/j.issn. 1005-2992.2021-01-0048-08求解辐射传输方程的多松弛格子-Boltzmann模型刘晓川\王存海2,黄勇、朱克勇1(1.北京航空航天大学航空科学与工程学院,北京100191,2.北京科技大学能源与环境工程学院,北京100083)摘 要:针对福射传输方程,文中提出了一种多松弛格子-B o l t z m a n n模型(m u l t i p l e-r e l a x a t i o n-t i m el a t t i c e B o ltz m a n n m o d e l).基于扩散尺度下的M a x w e l l迭代,辐射传输方程可以严格地从格子B o l t z m a n n方程推导得出•一些数值案例用来验证本文提出的多松弛(M R T)格子-B o l t z m a n n模型的数值特性.结果表明本文提出的多松弛格子-B o l t z m a n n模型可以稳定及精确地求解参与性介质中的瞬态及稳态辐射传输问题.并且,该模型具有二阶精度.关键词:辐射传输方程;格子-B o l t z m a n n方法;多松弛模型中图分类号:T K121 文献标识码:A辐射传输方程描述了辐射能量在介质中的传递,在许多科学和工程领域具有重要作用,例如大气辐 射传输[1]、光学层析成像[2]、天体物理学[3]及核工程[4]等.辐射传输方程是一个高维、复杂的积分微分 方程,辐射强度涉及波长、时间、空间和角度等,求其解析解十分困难.学者们提出发展了很多种数值方 法来求解辐射传输方程,如蒙特卡洛法[5],离散坐标法[6],有限体积法[7],有限元法[8]等.近年来,利用格子-Boltzmann方法(L B M)来求解辐射传输方程吸引了许多学者的兴趣.L B M起源 于格子气自动机,已经发展成为了一种计算流体力学的有力数值工具[9].并且,L B M已经被拓展到求解 许多线性和非线性系统问题,例如声子输运[1°],波传播[11],反应扩散,对流扩散等.相比于其他的求解辐射传输方程的数值方法,L B M不需要计算大量的光线轨迹,也不需要离散复杂的偏微分方程. L B M具有容易实现,高并行效率等优点•目前,对于利用L B M来求解辐射方程还不完善,发展完善的 L B M用于求解辐射传输方程是必要的.1^3111^等[14]假定了可调节的虚拟光速和辐射平衡条件,将L B M推广到分析参与性介质中的辐射 问题.M a等[~基于辐射流体力学,提出了一维辐射的格子-Boltzmann模型.Zhang等[16]通过采用全隐 式后项差分格式处理辐射方程中的瞬态项,将L B M扩展到求解参与性介质中的一维瞬态辐射传输. Mink等[171在将P1近似应用辐射传输方程的基础上提出了一种三维的格子-Boltzmann模型,然而此模 型仅适用于光学厚介质.Y i等[18]通过引入虚拟的扩散项,将辐射传输方程视为一种特殊的对流扩散方 程,从而提出了一种二维稳态射传输方程的格子-Boltzmann模型.W a n g等[19_将瞬态辐射传输方程处 理为双曲守恒方程,然后提出了 一■种求解瞬态辅射和中子输运的格子-Boltzm ann模型.目前,求解辐射方程的多松他的格子-Boltzmann模型还未见报道.本文提出了一种多松她格子-Bo­ltzmann 模型 (multiple-relaxation-time l a t t i c e Boltzmann mode丨)■基于扩散尺度下的 Maxwell 迭代,福射传收稿日期:2020-11-09基金项目:国家自然科学基金(s is M o o w g c te o i4)第一作者:刘晓川(1992-),男,在读博士研究生,主要研究方向:航空科学与工程通讯作者:黄勇(1974-),男,博士,教授,主要研究方向:航空科学与工程电子邮箱:liuxiaochuan@(刘晓川),wangcunhai@ustb_(王存海),huangy@(黄勇),zhukeyong@buaa.edu_cn(朱克勇)第1期 刘晓川等:求解辐射传输方程的多松弛格子-Bohmiami模型 49输方程可以严格地从格子Boltzmann方程推导得出,并且不引人任何限制和近似.本文发展的多松弛格 子-Boltzmann模型可以精确地求解参与性介质内的多维瞬态及稳态辐射传输问题.数值结果表明该模 型具有二阶精度和收敛速率.并且,相比于单松弛模型,多松弛模型具有更好的稳定性.该模型可以进一 步推广到求解参与性介质内的辐射传输问题.1福射传输方程的多松她格子-B o l t z m a n n模型1-1辐射传输方程考虑吸收、发射和散射介质内的辐射传输方程,其离散坐标形式可以写为[2°]dl(r,rr,t) +f f, v/(r;i T^)+/3(r)l(r,f r,t)=S(r,n r,t),(1)cLdt公式中心为介质内的光速;/为辐射强度;r为位置坐标冶+屹为衰减系数;/r + V")+ 为离散方向,源项S可以表示为s(r,n r,t)^kaib(r,{T,t)+^J j i{r,i r')(p(n r\n n)w m',(2)47T m,=1公式中A为总的离散方向,=1,2,…,八^' = 1,2,…,yv;M;m'为对应方向的权重.考虑漫发射和反射壁面,边界条件可以写为I(rw,{r,t)^e wIb(rw,t)+^-^I(rw ,f T') \nw -HT'\w m' + (\ - p j r\rw J F",t) ,(3)17 <Q公式中:&为发射率;Pu,为反射率;广‘为外部人射辐射强度.1.2 多松弛格子-Boltzm ann模型瞬态辐射常常发生于极短的时间内,在瞬态辐射的模拟中,通常引入无量纲时间来避免过小的时间 步长.将无量纲时间T心代人方程(1)中,得到时间无量纲形式的辐射传递方程[21]di(r,n",t) +L f f. v/(r)/2m,r)= F(r,/r,f*),⑷dt'公式中F{r,n r,r) = i R s{r,{r,r)-L^i(r,f r,r),(5)公式中:心为介质的参考长度.本文提出的时间无量纲形式的辐射传输方程的格子Boltzmann方程如下/(r + c^*,t*+A t')-/(r,t*)=--^(r.t*)] + A t'X),j(6)公式中:/(r,〇为分布函数;M为变换矩阵;S = 士叫U a,…人)为松弛参数矩阵,平衡函数的表达 式为r i(r,n r x)•跑-改2c?辐射强度可以由平衡函数给出,关系如下/(r,/r,〇=-(7)(8)50东北电力大学学报第41卷L B M方法中采用D m Q n格子模型,对于一维和二维问题,本文分别采用D1Q3和D2Q9模型.对于 D1Q3模型,其格子信息为[c〇,c, ,c2] =e;c = [0 1 -l]c,c [2/3,i =0c s=—,(0: = \ll/6,i = 1,2(9)(10)M0 12 一:-1对于D2Q9模型,其格子信息为(11)M C6,C7,C8] y =.0100 1-1-111-11-1l-l- i.c, (12)「4/9,/ =:0ccs = — 〇jt=,1/9,i =],2,3,4(13).1/36,i=5,6,7,8111111111)-4-1- 1--122224-2- 2-2- 21111010-01—1一 110-20201-1-11•(14) 00 10-111-1-100 - 20211-1-101- 11-1000000 0001-11-h13从格子Boltzm ann方程到辐射传输方程本节基于扩散尺度=7(4幻2下的Maxwell迭代,不引入任何限制和假设,从多松弛格子- Boltzmarm模型严格推导得出辐射传输方程.这种扩散尺度是针对模型中的无量纲时间步长和空间步长 的尺度.首先,令/8U,r))f,w =(叫,叫,…,叫)'时间无量纲形式的 辐射传递方程(6)可以写成矢量形式f(r + ciA t,,r+A t f)-/e9(r,〇] + A t'wF(r,t m),(15)方程(15)左边应用Taylor展开,其中微分算子D' 矩阵/(r + e,A%,«* +y{Ax)2)~^ (A x)*£)s/(r,t*),s = 1〇,=y(E,dx+ E yd y)p(ydt'),P*^s p\q\’A s d i a M e o y e m…,e8,J…,e^),(16)(17)(18)第1期刘晓川等:求解辐射传输方程的多松弛格子-Bohzmami 模型51公式中和g 均为非负整数.令m = M •/>〃 = M •/%,将Taylor展开形式代入方程(15)并整理得到00工(A x )sDsm =- S [m - me tf] + y (A x )2FMco ,s= 1其中D ^-M D ^-y CE,SX +E ,3y V (y s r yI'*^sp \ q \E t =ME M'E y =M E M '1 .(21)**jJ、’c o定义算子A = X (4幻]5\方程(19)可重新写为s= 1m =m e " -S 'Lm + y (A x )2FS~'Mu , (22)基于扩散尺度下的Maxwell1221迭代,从m° = m 〜开始,方程(19)经过三次迭代得到:m = m" -S ~'[A x D ' + (A x )2D2 + (A x )3D ,]me ,1 + [A x S ^D 1 + (A x )2S ^,Lf2]2ma ,-+7(4.«)2厂5_|财如 + 0((4x )4) ,(23)根据矢量方程(23)的第零项及各算子作用结果,可以得到辐射传递方程a /(r ,/7",f } +L r H" • V /(r ,/T ,<*) = F (r ,/T ,t *) +0((A x )2) ,(24)dt *至此,我们从多松弛格子-Boltzmann模型出发,基于扩散尺度下的Maxwell迭代,严格推导得出了辐射 传输方程,并且可以从方程(24)理论上得出该模型具有二阶的精度.一般而言,对于对流扩散问题,计 算流体力学等问题的L B 模型,其中的松弛系数与宏观方程中的扩散系数,流体黏性系数等有定量关系. 需要指出的是,根据从多松弛格子-Boltzmann模型严格推导得出辐射传输方程可知,本文提出的多松 弛格子-Boltzmann模型中的松弛参数均是自由的,与其他参数无关.对于一维和二维L B 模型,我们取 如下的松弛参数矩阵(19)(20)S = diag( 1 ,ir,l ) ,(25)S = diag(l,l,l,ir,l,5r,1,1,1) ,(26)对流扩散方程的多松弛L B 模型也采用了同样的处理方法,其中一维模型中的松弛参数,二维模型中 的松弛参数h 和s 5与扩散系数有关,而其他的松弛参数均取1.由于松弛矩阵中的松弛参数有无限种组 合方式,因此出于通用性考虑,我们选择了这种处理方法.同时需要指出的是当松弛参数矩阵中的松弛 系数相同时,多松弛模型退化到单松弛模型,即松弛矩阵中的松弛参数均为V2 结果及分析2.1 具有高斯型发射场的一维无限大平板考虑一充满吸收发射性介质的一维无限大平板内的辐射传递问题,平板内具有一高斯型发射场,该 问题由如下方程控制^+/3l ^e -u -b )2/a 2,z,b e [0,1] ,(27)考虑如下边界条件52东北电力大学学报第41卷I (〇,〇 =f 3-]e -b 2/a \ ^>0,(28)该问题存在解析解形式,其表达式如下/(2〇 =/(0,f )e x p ( —,)| 2 - (^ + 6)}X [erf (|+^)-erf (f +a )l ^>0, (29)考虑方向f = 1. 〇,a =〇• 02,6 = 0. 5,采用L B M 来模拟衰减系数为/3 = 1,10和50 时介质内辐射强度的分布,取1〇〇个格子,无量纲时间步长取土‘ =0.000 1,单松弛模型得到的结果和解析解对比,如图1所示,L B M 得到的辐射强度分布和解析解得到的辐射强度分布吻合地很好.接下来,我们进一步研究一维多松弛模型的稳 〇.〇4定性和精度.为了研究稳定性,我们考虑衰减系数为 10 nT1的情况,取100个格子,研究不同松弛参数下|所允许的最大时间步长.数值解和解析解的相对误| 〇.〇2 差定乂为Er = ^-------------(30)丨稳定性标准为数值解和解析解的相对误差小于 10'表1给出了不同松弛参数下所允许的最大时间 步长,不同参数的最大时间步长得到是根据我们定 义的稳定性标准,然后通过数值实验得到的,可以发现多松弛模型允许的最大时间步长可以随松弛参数调整,尤其当松弛参数小于1时,所允许的时间步长 大于单松弛模型,结果表明相比单松弛模型,多松弛模型可以在更大的时间步长内保持稳定,具有更好 的稳定性•多松弛模型的碰撞过程发生在矩空间,与多个速度分布函数相关联,相比单松弛模型发生在 速度空间的碰撞,多松弛模型本身在稳定性方面展现了很大的优势,数值结果证明了多松弛模型在稳定 性上的优势.此外,表2给出了不同格子数下单松弛和多松弛模型的相对误差,可以看出多松弛模型相 比单松弛模型具有更高的精度.图1衰减系数为卢=1,丨〇和501^时LBM 得到的辐射强度分布和解析解对比表1衰减系数/3 = 1〇 n T 1,100个格子下,单松弛(B G K )和多松她(M R T )模型允许的最大时间步长sr =0• 6sr =0. 8 sr =l.O(BGK)sr = 1 • 2sr = l • 4y m a x 22.618.413.28.2 4.1W ax2.26 e-31 • 84 e-31.32 e-30. 82 e-30.41e-3表2衰减系数/3 = 10n不同格子数下,单松弛(B G K )和多松弛(M R T )模型的相对误差格子数sr =0. 65r =0. 85r = l .2sr = 1.4BGK MRT BGKMRT BGK MRT BGK MRT 100 4.24 e-27.72 e-3 1. 14 e -2 3.09 e-3 4. 14 e-3 1.71 e-3 5.79 e-3 3.02 e-3150 1.82 e-2 3.24 e-3 4.84 e-3 1.25 e-3 1.85 e-37.77 e-4 2.54 e-3 1.35 e-32001.01 e-21.79 e-32.68 e~36.83 e—41.04 e-34. 40 e-41.42 e-37.57 e -42.2受高斯型脉冲照射的一维纯散射介质考虑厚度为1 m 的一维半透明平板介质内的瞬态辐射传输问题.介质为各向同性散射,壁面和 介质温度均为〇K,无发射.介质边界为透明边界,环境为真空.平板介质的衰减系数为1 nT1,右侧边界 无照射,左侧边界受到如下法向平行光人射辐射的照射:第1期刘晓川等:求解辐射传输方程的多松弛格子-Boltoimmi 模型53lp(0,t ) = /〇exp [//(〇 ,(31)公式中:/。

REV尺度多孔介质格子Boltzmann方法的数学模型及应用的研究进展

REV尺度多孔介质格子Boltzmann方法的数学模型及应用的研究进展

CHEMICAL INDUSTRY AND ENGINEERING PROGRESS 2016年第35卷第6期·1698·化 工 进 展REV 尺度多孔介质格子Boltzmann 方法的数学模型及应用的研究进展张潇丹1,2,雍玉梅2,李文军3,赵元生4,李媛媛2,杨巧文1,杨超2(1中国矿业大学(北京)化学与环境工程学院,北京 100083;2中国科学院过程工程研究所绿色过程与工程重点实验室,北京 100190;3华北科技学院环境工程学院,河北 廊坊 065201;4中国石油化工研究院渣油加氢实验室,北京 102200)摘要:综述了多孔介质表征体元尺度(REV )格子Boltzmann 模型的研究进展,根据对多孔介质处理方式主要分为部分反弹模型和阻力模型两类,分析归纳了各类模型的优缺点。

由于阻力模型中渗流的广义格子Boltzmann 方程(GLBE )的作用力是基于GUO 等的作用力模型,可以准确得到宏观方程,不存在离散误差,且模型的平衡分布函数和作用力项中都包含反应介质特性的孔隙率,因而应用最为广泛。

本文还重点介绍了REV 尺度多孔介质LBE 模型在流动、传热、传质、化学反应及相变等过程中的具体应用,认为REV 尺度多孔介质内的三传一反数学模型中需要加入孔隙尺度因素,在更大工程尺度上应该考虑过程参数的各向异性,展望了REV 尺度多孔介质LBE 模型的发展和应用前景。

关键词:多孔介质;表征体元尺度;格子Boltzmann 方法;流动;传热;传质中图分类号:TQ021.9 文献标志码:A 文章编号:1000–6613(2016)06–1698–15 DOI :10.16085/j.issn.1000-6613.2016.06.010Models and application of lattice Boltzmann method at REV-scalein porous mediaZHANG Xiaodan 1,2,YONG Yumei 2,LI Wenjun 3,ZHAO Yuansheng 4,LI Yuanyuan 2,YANG Qiaowen 1,YANG Chao 2(1School of Chemical & Environmental Engineering ,China University of Mining & Technology (Beijing),Beijing100083,China ;2Key Laboratory of Green Process and Engineering ,Institute of Process Engineering ,Chinese Academy of Sciences ,Beijing 100190,China ;3School of Environmental Engineering ,North China Institute of Science and Technology ,Langfang 065201,Hebei ,China ;4Laboratory of Residue Hydrotreating ,Research Institute of PetroleumProcessing ,PetroChina ,Beijing 102200,China )Abstract :This paper discusses the lattice Boltzmann model at representative elementary volume (REV)scale for porous media. According to different treatments of porous media ,the lattice Boltzmann model at REV-scale for porous media can be classified into two categories ,the partially bouncing-back model and the resistance model. The advantages and disadvantages of various models are analyzed. The Generalized lattice Boltzmann equation (GLBE model) in the resistance model is most widely used. Firstly ,the force item of the GLBM model is based on the method proposed by Guo et al ,which can be第一作者:张潇丹(1989—),女,硕士研究生,主要从事化学工程数值模拟。

基于Lattice Boltzmann模型的液-液混合流模拟

基于Lattice Boltzmann模型的液-液混合流模拟
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格子boltzmann方法模拟方形腔内纳米流体的自然对流

格子boltzmann方法模拟方形腔内纳米流体的自然对流

格子boltzmann方法模拟方形腔内纳米流体的自然对流格子Boltzmann方法是一种基于分子动力学的计算方法,用于模拟纳米尺度系统的自然对流现象。

自然对流是指由于温度梯度引起的流体的自发运动。

在方形腔内纳米流体的自然对流模拟中,格子Boltzmann方法可以提供高精度和高效率的计算结果。

格子Boltzmann方法的基本思想是通过模拟流体中分子的运动来计算流体的宏观性质。

它将流体视为由大量粒子组成的离散系统,通过迭代求解碰撞和分布函数来模拟流体的运动。

对于方形腔内纳米流体的自然对流模拟,格子Boltzmann方法可以分为以下几个步骤:1. 确定流体的初始状态:包括流体的密度分布、速度分布和温度分布等。

这些初始条件可以根据实验数据或者其他模拟结果进行设定。

2. 确定边界条件:对于方形腔内纳米流体,边界条件可以包括固定壁面、恒定温度或者固定速度等。

这些边界条件可以通过数学模型或者实验数据进行设定。

3. 确定碰撞模型:格子Boltzmann方法中的碰撞模型可以通过使用Boltzmann方程和碰撞积分来描述分子之间的相互作用。

这一步骤是模拟过程中最关键的一步,需要根据实际情况进行合理的设定。

4. 进行格子更新:在格子Boltzmann方法中,流场被离散化为格子,流体的宏观性质通过迭代更新格子上的分布函数来计算得到。

格子的更新可以采用Lattice Boltzmann方程进行计算。

5. 求解宏观性质:通过对流体的速度分布和温度分布进行统计,可以求解得到方形腔内纳米流体的宏观性质,如热流、质量流和压力等。

在方形腔内纳米流体的自然对流模拟中,格子Boltzmann方法可以提供高精度和高效率的计算结果。

与传统的数值模拟方法相比,格子Boltzmann方法具有计算量小、精度高、并行化程度高等优点。

此外,格子Boltzmann方法还可以考虑纳米尺度下的非平衡效应,对于纳米流体的自然对流现象具有较好的描述能力。

参考文献:1. Shan, X., & Luo, L. S. (1993). Numerical study of anisotropic permeability in random porous media. Physical Review E, 47(3), 1815.2. He, X., & Luo, L. (1997). Theory of the lattice Boltzmann method: From the Boltzmann equation to the lattice Boltzmann equation. Physics Review E, 56(6), 6811.3. Guo, Z., Zheng, C., & Shi, B. (2002). Discrete lattice effects on the forcing term in the lattice Boltzmann method. Physical Review E, 65(4), 046308.4. Succi, S. (2001). The Lattice Boltzmann Equation for Fluid Dynamics and Beyond (Vol. 431). Oxford: Oxford University Press.。

格子 Boltzmann 模拟条纹基底上液滴的蒸发

格子 Boltzmann 模拟条纹基底上液滴的蒸发

格子 Boltzmann 模拟条纹基底上液滴的蒸发李荣娟;両角仁夫【摘要】采用二维的格子Boltzmann方法对条纹基底上液滴蒸发进行模拟。

讨论了基底润湿性区域宽度和蒸发率大小的改变对蒸发液滴三相接触线( contact line )运动的影响。

计算结果显示当处于条纹基底上的蒸发液滴边缘接触润湿性区域时,三相接触线后退的解销联( de-pinning )阶段变为基部直径保持不变液滴高度降低的销联( pinning )阶段。

另外,还发现对于同样尺寸的蒸发液滴处在宽度不同的润湿性区域的条纹基底上,销联时间和蒸发消失的时间是不同的。

%A two dimensional lattice Boltzmann simulation was performed to investigate the e-vaporating droplet on patterned substrates with different wettability .The contact line motion of an evaporating droplet on a patterned substrate with line -shaped hydrophilic region was numerically investigated .The effects of the width of hydrophilic region and the evaporation rate on the contact line motion were discussed .The calculated result showed the transition of the contact line motion from de -pinning to pinning when the contact line reaches to the edge of the hydrophilic region of patterned substrates .In addition , the difference in the pinning time and the evaporation time was obtained for variation of hydrophilic region on patterned substrates .【期刊名称】《哈尔滨商业大学学报(自然科学版)》【年(卷),期】2014(000)004【总页数】4页(P442-445)【关键词】格子Boltzmann模拟;蒸发液滴;条纹基底;三相接触线;蒸发率【作者】李荣娟;両角仁夫【作者单位】哈尔滨商业大学能源与建筑工程学院,哈尔滨150076;高知工科大学系统工程学院,高知日本782-8502【正文语种】中文【中图分类】TV131最近几年,喷墨打印技术已经被广泛应用到工业领域——电,光和生物工程等领域[1-3],这是由于这项技术能够喷射微小液滴到基底上,并且,液滴蒸发后能够形成薄膜.可是在有些情况下,喷射的液滴蒸发后形成的薄膜不能形成在基底的指定区域,这是由于液滴的尺寸大于指定区域的尺寸.例如:在工业上要求电路板上的电线宽度为10 m,而喷墨打印形成的最小液滴尺寸范围是10~20 m.这就表明利用喷墨打印技术形成小于10 m的电线宽度是很困难的.为了解决这一问题,有人设想把均一基底改成由条纹状润湿性区域和非润湿性区域组成的不均一基底——条纹基底[4].利用这个方法,液滴可能在基底的指定区域(润湿性区域)形成宽度小于10 m线性薄膜.可是液滴在蒸发过程中,在基地上能够形成薄膜受何种因素的影响,目前还处于研究状态.液滴蒸发过程中,三相接触线的运动能够反映薄膜的形状.1 数字模拟1.1 计算模型图1给出了一个液滴处在条纹基底上的物理条件和计算区域模型.计算区域是一个二维的笛卡尔坐标,横坐标和纵坐标分别是400 (Lx)和150 (Ly)的格子.基底是由中心宽度Lw,phi的润湿性条纹区域与其他部分为非润湿性条纹区域组合而成的条纹基底.首先将一个球冠形液滴放在条纹基底上,此液滴的润湿直径为dw,中心高度为h,三相接触角度为θc.图1 一个液滴处在条纹基底上的物理条件和计算区域模型1.2 两相格子Boltzmann模型本文应用Lee 和Lin[5]提出的二维九速度D2Q9的格子Boltzmann模型来模拟条纹基底上一个液滴的蒸发过程.二维格子Boltzmann模型应用了两个粒子分布函数,其中一个函数被用于区分流体的两相,另外一个函数被用于计算两相流的运动情况.在计算液滴蒸发过程中,我们把蒸发模型和润湿模型兼容到二维格子Boltzmann模型中.其中,液滴蒸发的计算模拟,采用Li和Morozumi[6]提出的液滴质量损失的蒸发模型.关于润湿模型,本文改进了Martys和Chen[7]提出的黏性力兼容到格子Boltzmann模型来描述条纹基底的润湿能力.关于边界条件:在x =-Lx/2 and Lx/2上,我们采用周期边界条件;在y = 0上,我们选用了质量保存边界条件 [8].1.3 计算条件表1给出了水的物理性质参数和模拟的计算参数.为了把水的物理性质放入到计算程序中,我们首先计算了自然界中液体水在常温条件下的Ohnesorge数(Oh)和Bond数(Bo)分别是1.18×10-2 和1.36×10-3.然后,根据模拟和计算水的Oh和Bo数相等,给定模拟水的密度参数1000.得出表1中其他的模拟参数.本次模拟中,液滴在均一的润湿性和非润湿性基底上的静态三相接触角度分别为:42.7°和121.8°[9-10].在模拟中,条纹基底的润湿性条纹区域宽度从30增大到70.蒸发率系数设为1.0×10-4 和2.0×10-4.表1 水的物理性质和模拟计算条件模拟参数物理参数液体密度(ρL)1.01 000 kg·m-3气体密度(ρG)1.2×10-31.2 kg·m-3液体/气体黏度(μL/μG)5.5655.6表面张力(σLG)2.09×10-37.2×10-2N·m-1界面宽度(D)12-液滴接触直径(dw)1002.0×10-4m重力加速度(g)1.14×10-99.8 m·s-22 模拟结果与讨论图2描述了在条纹基底上液滴形状随蒸发时间增长的变化情况.此模拟,蒸发率εev设定为1.0×10-4,条纹基底上润湿性区域的条纹宽度Lw,phi为50.从图2中,发现液滴在蒸发的过程中,液滴的体积随着时间步长的增加而减小.在蒸发开始阶段,三相接触线朝着液滴中心方向后退.当三相接触线后退到润湿性区域和非润湿区域交接线的边缘线时,三相接触线的运动处于暂时停止状态.此时,液滴的中心高度开始减小,接触直径保持不变,三相接触线的运动处于销联阶段.最后,由于液滴继续蒸发,三相接触线再次后退,液滴最后消失.图2 条纹基底上液滴形状随蒸发时间增长的变化蒸发率系数(εev=1.0×10-4)图3 条纹基底上液滴蒸发的接触直径(dw)和三相接触角随时间变化关系(模拟条件:εev=1.0×10-4)图3描述了在条纹基底上液滴蒸发的过程中,接触直径(dw)和三相接触角随时间变化关系.此模拟条件与图2相同.在图3中,根据接触直径和三相接触角的运动,我们能够很容易得出三相接触线运动的三个阶段.第一阶段(I),接触角保持不变,接触直径减小,蒸发液滴三相接触线的运动处于解销联阶段.随着时间的推移,接触直径接近基板的润湿性区域,蒸发液滴三相接触线的运动处于第二个阶段(II).在这个阶段我们能够观察到:dw的大小是51.6,这个数值与基底润湿性条纹宽度Lw,phi=50几乎一致.在第二阶段,当三相接触角减小时,润湿直径保持不变,蒸发液滴三相接触线的运动处于销联阶段.当三相接触角接近静态接触角(46°),润湿直径再次减小,蒸发液滴三相接触线的运动处于第三个阶段(III)——销联与解销联组合.在第三阶段,当润湿直径迅速减小,液滴将要消失时,三相接触角略微有所增加之后迅速减小.这一现象可能是由于模拟中界面宽度引起的计算误差所导致的.图4 比较了蒸发率系数不同的连个完全相同的液滴处在相同条纹基底上,接触直径和三相接触角随时间变化个关系.对于蒸发系数εev为1.0×10-4和2.0×10-4的两个相同液滴,三相接触线运动存在相似的三个阶段.由于接触直径接触润湿性区域,三相接触线的运动从第I阶段将转变为第II阶段.三相接触线处于第II阶段时,当三相接触角减小到与液滴处于润湿性基底的静态接触角相同时,三相接触线的运动将进入第III阶段.对于εev为2.0×10-4的液滴比εev为1.0×10-4液滴蒸发的快.这是由于当蒸发率系数增大时,液滴的质量损失率也随之增加.图4 相同条纹基底上,蒸发率系数不同的两个体积相同液滴的接触直径和三相接触角随时间变化模拟条件(εev=1.0×10-4和2.0×10-4)图5描述了处在条纹基底上,蒸发率系数为1.0×10-4的蒸发液滴接触直径和三相接触角的变化情况.模拟中,条纹基底的润湿区域宽度数值Lw,phi分别被设为30,50和70.对于不同的润湿宽度的条纹基底,液滴蒸发过程中都出现了三个阶段的三相接触线运动.当接触直径从非润湿直径后退到润湿性区域时,三相接触线的运动从第I阶段变为第II阶段.此时,液滴蒸发处于三相接触线运动的销联阶段.在第II阶段,销联时间随着润湿性区域宽度的增大而增长.另外,对于蒸发率相同,体积相同的液滴,由于润湿性条纹区域宽度的不同,液滴蒸发结束所需时间是不同的.液滴蒸发结束所需时间随着润湿性条纹区域宽度的增加而减小.出现这种结果的原因可能是由于相同体积液滴蒸发表面积不同所引起的.当三相接触线运动处于销联阶段时,相同体积的蒸发液滴的表面积大于三相接触线处于解消联阶段.图5 条纹基底上,蒸发液滴接触直径和三相接触角的变化情况,模拟条件(εev=1.0×10-4,Lw,phi=30,50和70)3 结论本文应用了一个二维的格子Boltzmann方法模拟一个处于条纹基底上液滴蒸发过程.蒸发模型和润湿模型被用到格子Boltzmann模拟中.其中蒸发模型根据模拟气液界面的质量损失给出;为了避免非物理流和密度的减小,一个修正的润湿模型被采用,并且给出了条纹基底的不同润湿能力.通过模拟,得出了以下结论:1) 对于不同蒸发率系数和润湿性区域宽度,液滴蒸发过程中,三相接触线的运动都能分为三个阶段——解销联,销联和销联与解消联混合阶段.2) 当蒸发率系数减小时,相同体积液滴蒸发结束时间增长;随着润湿区域宽度的增加,相同体积液滴蒸发结束时间减小.参考文献:[1] DANZEBRINK R, AEGERTER M A. Deposition of micropatterned coating using an ink-jet technique [J]. Thin Solid Films, 1999, 351: 115-118.[2] SIRRINGHAUS H, KAWASE T, FRIEND R H, et al. High-Resolution inkjet printing of all-polymer transistor circuits [J]. Science, 2000, 290: 2123-2126.[3] ROTH E A, XU T, DAS M, et al. Inkjet printing for high-throughput cell patterning [J]. Biomaterials, 2004, 25: 3707-3715.[4] MORITA M, YASUTAKE S, ISHIZUKA H,et al. Site-selective coating of polymer thin film prepared by the ink-jet method on the patterned fluoroalkyishilane monolayer substrate [J]. Chem. Lett., 2005, 34: 916-917.[5] LEE T, LIN C. A stable discretization of the lattice Boltzmann equation for simulation of incompressible two-phase flows at high density ratio [J]. J. Comp. Phys., 2005, 206: 16-47.[6] LI R J, MOROZUMI Y. A lattice Boltzmann simulation for contact line motion and internal fluid flows in an evaporating droplet on homogenous substrates [J]. J. Chem. Eng. Jpn., 2012, 45 (3): 155-165.[7] MARTYS N S, CHEN H. Simulation of multicomponent fluids in complex three-dimensional geometries by the lattice Boltzmann method [J]. Phys.Rev. E., 1996, 53: 743-750.[8] BAO J, YUAN P, SCHAEFER L. A mass conserving boundary condition for the lattice Boltzmann equation method [J]. J. Comp. Phys., 2008, 227: 8472-8487.[9] LI R J, MOROZUMI Y. A lattice Boltzmann simulation of drying liquid film on patterned substrates with different wettability [C]//The 17th International Drying Symposium, 2010, Magdeburg, Germany.[10] 范俊.基于格子Boltzmann方法的多相态流动模型研究[J].哈尔滨商业大学学报:自然科学版,2013,29(5):608-613.。

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3D lattice Boltzmann simulation of fluid flow in fractured rockT.Miyoshi,T.Matsuoka,S.Murata&Y.AshidaKyoto University,Kyoto,JapanABSTRACT:It is important to estimate fluid flow in reservoir rocks for an enhanced oil recovery and evaluation of the productivities of the fracture reservoir.In this study,the Lattice Boltzmann Method(LBM),which is a new numerical computation method for the fluid flow,was applied to fracture flow.Consequently,we concluded that the flow obeys the cubic law even in the case of a very complex surface when the parallel fracture surfaces are the same shapes.The deviation from the cubic flow,however,is controlled by the standard deviation of the aperture of the fracture.This means that the variance of the topology of the fracture surface is not an important parameter compared with the variance of the aperture width.1INTRODUCTIONWe have conventionally used Reynolds equation for evaluating fluids flow behavior in single fracture,but we cannot evaluate the exact channel flow.In a simple approach using Reynolds equation,the vertical flow in the fracture cannot be evaluated.To overcome this problem,the Lattice Boltzmann Method(LBM)(Qian et al.1992,Rothman&Zaleski1997,Succi2001, Wolf-Gladrow2000,Y oshino2000)has been applied in this study.LBM has an advantage that we can evalu-ate the3D flow behavior with the complex boundary conditions.In this study,we developed2D&3D LBM programs and applied them to the fracture generated by the fractal algorithm.We have done the following simulation studies.The fluid flow generally becomes less than the theoretical quantity when the fracture aperture is narrower.This phenomenon is shown in this simulation and the deviation from the cubic law is discussed in detail.The two parameters of fracuture topology influence this deviation.2BASIC THEORIES FOR LBM2.1Lattice Boltzmann methodA finite difference method or a finite element method have conventionally been used for the simulation of fluid behavior.These methods are based on Navier-Stokes equation.In contrast to these method,the LBM approximates fluid as particles.The collision and movement of particles and boundary conditions are expressed by density distribution functions.And the fluid velocity,density and pressure(macroscopic parameters)are calculated from the distribution func-tions(microscopic parameter).The distribution func-tions must be set as if the collision,movement,and boundary conditions retained the character of con-tinuation of fluid.Various models of different lat-tice or collision rules are suggested,but we adopted the BGK(Bhatnagar-Gross-Krook)model(Bhatnagar et al.1954)and set9velocity model for2D and15 velocity model for3D.2.2Basic equationsThe concept of the LBM in the15velocity model for 3D is as follows.At first the three dimensional space is divided into unit lattices.It is assumed that the particles are on the lattices,and the number of particles cor-responding to a particle density are expressed by the distribution functions.Furthermore,the virtual par-ticles on eachlattices have15velocity,as expressed in Equation1.Figure1shows the velocity of each particle.In addition,all particles exist on the lattices and there are no particles on the way of between lattices in each calculation time step.The distribution functions according to each15velocity are given on all lattices. It is very important for the distribution functions to change with time steps in the LBM.Figure 1.The three dimension lattice is shown.The virtual particles move to each 15direction.There are four parts in this method.The first step is the collision part.The evoluation of the distribution function f i (x ,t )with the velocity c i at the point x and time t is calculated by the followingequationswhere x is a lattice spacing, t is a time step,τis a single relaxation time and f eq is an equilibrium distribution function that is given from fluid macro-scopic velocity u and density ρusing Equation 3.The equilibrium disutribution function is a particle density distribution that reached equilibrium in the finite space likelattices.The second step is the movement part.The particles on each lattice move in 15directions according to par-ticle velocity c i when a time step advances.The number of particles which move in each direction is defined as the distribution function.The third step is the evalu-ation of the boundary conditions.We will describe this in detail in the following section.The last step is the macroscopic part.The fluid velocity,density and pressure,which are macroscopic parameters on each lattice,are calculated from the distribution functions (Eq 4to Eq 6).These distribution functions are given from the movement step.When these four steps arerepeated,the evoluation of fluid flow can becalculated.2.3Boundary conditionsAll the distribution functions are not known on the sur-face of a boundary wall because the particles does not move from outside of the fluid area.So the unknown distribution functions must be determined from the known distribution functions.Several boundary con-ditions are proposed.We use the Inamuro ’s method in this study (Inamuro et al.1995).When a lower half of Figure 1is a wall,the distribution function f 3,f 8,f 9,f 11and f 14having speed of c y >0are unknown.Then each distribution function is guessed as Equation7.where u w ,v w and w w are wall velocity to each direc-tion.The wall velocity is zero in thissimulation.In Equation 7the unknown parameters,ρ ,u ,and w are given as follows,since the fluid velocity must be equal to the wall velocity and three equations on vel-ocity are made according to each direction.Moreover,the fluid density on the boundary lattices ρw is unknown quantity and is calculated by Equation 4.Equations 8–11are provided as a solution of theseequations.Next,for the boundary condition with an intersecting borderline and corner of a wall we assume a virtual boundary surface including boundary node and apply the above boundary condition on the virtual surface.A periodic boundary condition is adopted at the inlet and outlet surfaces.This boundary condition is set according to the pressure difference between the inlet and outlet.Supposing that the pressure differ-ence is generated to the x axis and the surface x =0is the inlet and x =L x is the outlet,the disutribution function f 2,f 8,f 10,f 11and f 13are unknown on the node of the inlet surface and f 5,f 9,f 12,f 14and f 15on the outlet surface.Then as f 2is known at the outlet surface,this function is introduced into the inlet side.That is,adding the pressure difference K ,f 2at the inlet side is given by Equation 12.In addition,the other un-known distribution functions obey Equation 13.The unknown functions at the outlet side are determined,as well as the inlet side (Eq 14&15).The pressure difference K is given by Equation16.3THE FLUID FLOW IN THE FRACTURE 3.1The deviation from the cubic law of fracturepermeability It is known that the flux through a single fracture is proportional to the cubic of mean aperture if a model is a parallel flat wall (Brown 1989).But as the aper-ture narrows,the flux does not obey the cubic law.We examined this result using the LBM.Since the fracture surface is generally fractal,a fracturesurfaceFigure 2.2D fracture model.This model is divided into 1000×100lattices.Figure 3.The deviation from the cubic law for the 2D fracture model.The deviation becomes larger according to narrowing the aperture.model was made by the fractal algorithm,as shown in Figure 2.The two fracture surfaces were parallel each other.The boundary conditions was set as follows.The pressure difference at two boundaries each other was set and the fluid flow in this direction was calculated.Next,the fracture aperture was changed by moving the upper surface upwards or downwards.The mean aperture is defined by Equation17.where d (x ,y )is the distribution of the fracture aper-ture,L x and L y are the length of a fracture to each direction.Now,when the flux Q is calculated by the LBM,the hydraulic aperture that conforms to the cubic law is determined by Equation18.where µis the viscosity of the fluid and p is the pressure difference to the x direction.If the ratio of d h /<d >is equal to one,this fracture model perfectly conforms to the cubic law.The cubic of this ratio means a deviation from the cubic law of fracture permeability.Figure 3shows the result of this simulation.In this Figure,the vertical axis is the cubic of the ratio (d h /<d >)3and the horizontal axis is the mean aperture normalized by standard deviation of fracture surface.It can be seen that the (d h /<d >)3deviates from the cubic law with the decrease in the aperture.This result is almost the same as Figure 10in Brown (1989).Figure4.The schematic drawings ofσf(a)andσd(b).σf is standard deviation of fracture height andσd is that of the aperture.3.2Parameters of the deviationIt is shown in the subsection3.1that the narrower the mean aperture,the larger the deviation from the cubic law.But if the model is a parallel flat wall,the devia-tion from the cubic law cannot be found theoretically no matter how narrow the aperture is.Then a roughness of fracture is considered as the parameter of the devi-ation.In this study,the standard deviation of fracture heightσf and the standard deviation of the apertureσd are used for the roughness parameter.Figure4shows schematic drawings ofσf andσd.The fracture model made by the fractal theory has almost the same value ofσf for the upper and lower surface.If the upper and lower wall are the same,σd is equal to zero because the aperture is uniform.3.3Analysis of2D modelsWe analyzed the deviation with the following2D models.Model2D-A:the upper and lower surface whose shape is a sine curve have the unifrom aperture (Figure5(a))Model2D-B:the upper surface is a sine curve and the lower is flat(Figure5(b))Model2D-C:the upper and lower surface are the same surface which is made by the fractal theory, and then,the upper wall is moved to the fracture length direction(Figure5(c))Several models of differentσf are made according to the amplitude of a sine curve in model2D-A.The bigger the amplitude,the larger the standard deviation of the surfaceσf.However,σd is equal to zero because the aperture is uniform in each fracture.In Figure6, the deviation of the models for differentσf is ploted as a function of the aperture.The legend“σ”areσf. The solid linewhose legend is“fracture”corresponds to that shown in Figure3.This figure shows that the deviation from the cubic law as a result of narrowing the aperture cannot be found even ifσf changes that.Figure5.Models for analysis of the parameter contribut-ing the deviation from the cubic law.The size of models is;333×33lattices(2D-A and2D-B);1000×100lattices (2D-C).Figure6.The lines with symbols are the rusult of model 2D-A.The solid line corresponds to Figure3.The legend “σ”is allσf.σd is zero.The deviation from the cubic law cannot be seen.In the model2D-B,the aperture changes along the profile because the lower wall is flat.Thenσf is equal toσd.The rusult of model2D-B is the three dashed lines with filled symbols in Figure7.Due not only to narrowing the aperture but also increasingσ,the deviation from the cubic law is increasing.In the model2D-C,at first the upper wall and the lower wall which are made by the fractal theory are the same.Next the upper wall is moved to the fracture length direction.Thenσf is fixed at12.6.The rough-ness of the aperture distribution increases according to the shift length of the upper wall.The result of2D-C is the three lines with unfilled symbols in Figure7.The deviation becomes larger with increasingσd.These things show that the standard deviation of the aperture distributionσd controls the deviation from the cubic law on the flux in the fracture.3.4ConclusionsThe flux in a fracture is proportional to the cubic of the mean aperture only when the aperture is uniform. That is,as the upper and lower walls are the same,the deviation from the cubic law does not occur even if the surface is too complex,but the deviation becomes larger withσd.The separation is strongly controled not by the roughness of the fracture surface but by theFigure 7.The result of model 2D-B &model 2D-C.The legend “σ”is σd .The dashed lines with filled symbols are the result of model 2D-B.The dashdotted lines with unfilledsymbols are the result of model 2D-C.The solid line corres-ponds to Figure 3.The deviation from the cubic law becomes larger with increasing of the σd .roughness of the aperture.As a conclusion,σd is a more important parameter to be considered in the deviation from the cubic law,and the deviation is larger with increasing roughness of distribution of aperture.3.53D fracture modelWe described the 2D models in the previous section but when we compare the results for this simulation to the experimental results,3D analysis is more valuable.In this section,we analyzed 3D fracture models using the 3D LBM,and compared them to 2D.In the 2D analysis,the 2D models were made cut-ting out from the 3D fractured model whereas in the 3D analysis the 3D fracture model is used as it is.We made various models for the same fractal dimension 1.2.And we paid attention to the σf and σd and exam-ined them as well as in the 2D analysis.Figure 8is a lower surface which is divided into 256×256×15lattices.And in Figure 9,the visualized 3D fluid flow is shown.The Axis value is a lattice number.The solid lines are streamlines and the gray plate is a section of the fracture.The vertical fluid flow which cannot be seen by Reynolds equation can be seen.3.6Analysis of 3D modelsTwo kinds of models are used for the 3D analysis.Figure 10shows the 2D section of the following models.Model 3D-A:(the tensile type fracture)The rough-ness of aperture σd is 0.65for all but the roughness of the fracture surface,σf ,is varied in each model.Model 3D-B:(the strike-slip type fracture)Using one of the 3D-A models,the upper surface is moved to the horizontal direction.The σd increases from 0.65according to the shift length but σf is constant.Figure 8.The lower surface of the 3D fracture model made by the fractal algorithm,which is divided into 256×256×15.Figure 9.The visualized fluid flow through the 3D fracture.A solid line is a streamline for fluid flow.A gray plate is a section of the fracture.Figure 10.The schematic drawings of the 2D section for two fracture types.In model 3D-A,we made three models whose σf is 2.97,1.88and 2.34,respectively.And the standard deviation of aperture σd is about 0.65.(d h /<d >)3for these three models are calculated using the 3D LBMFigure11.The model3D-A.The legend“σ”isσf.Theσd is constant.The trend of the deviation is the same in eachmodel.Figure12.The model3D-B.The legend“σ”isσd.Theσf is constant.The deviation becomes larger according to theσd.and plotted as a function of the aperture normalized by the standard deviation of the aperture(Figure11). Even if the roughness of surface(σf)is different, the trend of the deviation from the cubic law is almost the same.This result is almost the same as the2D analysis.In model3D-B,the surface which is one of model 3D-A(σf=1.88)is not changed as the shape,but it is moved in the direction of fluid flow.This model is assumed to be the fracture with strike-slip.Firstσd is 0.65,and it increases according to the shift length.The lower surface is fixed,and the upper surface is moved with X to the x direction.Then the upper wall does not exist at0≤x≤X.So using the periodic boundary condition,the empty area is filled by the upper surface data at L x−X≤x≤L x.In this condition,σd is maximum as L x/2shifts because the two surfaces do not fit.In this simulation,the shift length is4–8%of the fracture length.In the Figure12,the legendσis allσd,andσf is equal to1.88.As the fracture surface is almost equal,the deviation of the cubic law is larger with the increase inσd.3.7ConclusionsThis is the same result as the2D analysis.The deviation from the cubic law is almost the same whenσd is fixed andσf is changed.Whenσf is fixed andσd is changed, the separation becomes larger asσd increases.4CONCLUSIONSIn this study,we confirm that the lattice Boltzmann metheod is valuable for the simulation of fluid flow through the reservoir rock like the porous media or ing this method,the fluid flow in a complete3D space can be calculated more correctly.From our study,we concluded that when the paral-lel fracture surfaces are the same topology,fluid flow obeys the cubic law even in the case of a very complex surface.This means that the variance of the topology of the fracture surface is not an important parameter, compared to the variance of the aperture.The deviation from the cubic law is controlled by the standard devi-ation of the aperture of the fracture.In the tensile type fracture,the permeability will be near to that estimated from the cubic law because the upper surface and lower surface fit each other very well whereas in the strike-slip type fracture,the permeability will become less because the distribution of the aperture is disturbed. Therefore at the tensile type fracture,more amount of fluid can flow than the strike-slip type fracture which has the same mean aperture.REFERENCESBhatnagar,P.L.,Gross,E.P.&Krook,M.1954.A model for collision processes in gases.I.Small amplitude processes in charged and neutral one-component systems.Phys.Rev.94:511–525.Brown,S.R.1989.Transport of fluid and electric current through a single fracture.J.Geophys.Res.94:9429. Inamuro,T.,Y oshino,M.&Ogino,F.1995.A non-slip bound-ary condition for lattice boltzmann simulations.Phys.Fluids7:2928–2930;Erratum:8(1996):1124.Qian,Y.H.,d’Humieres,D.&Lallemand,ttice BGK models for Navier-Stokes equation.Europhys.Lett.17:479.Rothman,D.H.&Zaleski,ttice-gas cellular automata-simple models of complex hydrodynamics.Cambridge:Cambridge University Press.Succi,S.2001.The Lattice Boltzmann Equation for Fluid Dynamics and Beyond.New Y ork:Oxford University Press.ttice-gas cellular automata and lat-tice Boltzmann models.LNM1725Berlin:Springer.Y oshino,M.2000.Numerical analysis of transport phenom-ena in porous structure by the lattice Boltzmann method: Ph.D.thesis.Kyoto University.。

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