Lattice Boltzmann based PDE solver on the GPU
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boltzmann拟合原理1.引言1.1 概述概述部分应该对本文所要讨论的主题进行简要介绍,概括其背景和重要性。
以下是一个可能的概述:概述:Boltzmann拟合原理是一种用于拟合数据的统计学方法,在各个领域的研究和应用中都得到了广泛的运用。
它的基础是Boltzmann分布原理,该原理描述了粒子在热平衡条件下的分布规律。
通过应用Boltzmann拟合方法,我们可以从实际数据中提取出与Boltzmann分布相对应的参数,进而对数据进行分析和预测。
本文旨在介绍Boltzmann拟合原理的基本概念和具体方法,分析其在实际问题中的应用及其优势。
通过深入理解Boltzmann拟合原理,我们可以更好地理解数据的分布规律,从而为科学研究和工程应用提供有力的支持。
在下文中,我们将首先介绍Boltzmann 分布原理,然后详细讨论Boltzmann拟合方法的具体步骤和应用场景,并对其在不同领域的潜在应用进行展望。
文章结构部分的内容如下:1.2 文章结构本文将分为三个主要部分来介绍Boltzmann拟合原理。
首先,我们将在"引言"部分提供对本文的概述,并描述文章的目的。
随后,在"正文"部分的"2.1 Boltzmann分布原理"中,将详细介绍Boltzmann分布原理的概念和背景知识。
我们将解释Boltzmann分布原理在统计物理学和热力学中的重要性,并介绍其在不同领域中的应用。
接着,在"2.2 Boltzmann拟合方法"中,将深入探讨Boltzmann拟合方法的原理和技术细节。
我们将介绍Boltzmann拟合方法在数据拟合和模型优化中的作用,并提供相关的实际案例和应用场景。
通过实例分析和数学推导,读者将能够理解Boltzmann拟合方法的实际操作和数学原理。
最后,在"结论"部分的"3.1 总结"中,我们将对本文进行总结,并回顾Boltzmann拟合原理的关键点和应用价值。
SOFC多孔阳极内微尺度流动的Lattice-Boltzmann模拟谢龙;王玉璋;于建国【摘要】为了模拟阳极支撑固体氧化物燃料电池多子阳极内的微尺度流动,利用Lattice-Boltzmann方法建立了多组分气体流动模型,结合多孔介质的数值构造方法,验证了复杂多孔介质内微尺度流动模型的有效性,研究了多孔阳极微结构和燃料组分对质量传递和浓差极化的影响.计算结果表明:Lattice-Boltzmann方法适用于多孔电极内的微尺度流动模拟,阳极浓差极化与燃料组分、电流密度以及阳极微结构相关.%in order to simulate the micro-scale flow in porous anode of anode-supported solid oxide fuel cell, a multi-component gas flow model was built through Lattice-Boltzmann method and the porous media was reconstructed by numerical technique. Based on the validation of micro-scale flow model of complex porous media, the effect of microstructure and fuel component on mass transport and concentration polarization was analyzed. Conclusions are drawn out: Lattice-Boltzmann method is competent for the simulation of micro-scale flow in porous electrode, and the concentration polarization of SOFC anode is highly related to fuel component, current density and anode microstructure.【期刊名称】《电源技术》【年(卷),期】2013(037)002【总页数】5页(P209-213)【关键词】固体氧化物燃料电池;格子-波尔兹曼方法;微尺度流动;浓差极化【作者】谢龙;王玉璋;于建国【作者单位】上海交通大学动力机械与工程教育部重点实验室,上海200240;上海交通大学动力机械与工程教育部重点实验室,上海200240;上海交通大学动力机械与工程教育部重点实验室,上海200240【正文语种】中文【中图分类】TM911.4固体氧化物燃料电池 (SOFC)一次发电效率高,易实现SOFC-GT联合循环,电极材料成本低,燃料适用面广,全固态结构适合进行模块化设计和放大,是中长期最具吸引力的新型、绿色能源解决方案[1]。
格子Boltzmann方法的原理与应用1. 原理介绍格子Boltzmann方法(Lattice Boltzmann Method)是一种基于格子空间的流体模拟方法。
它是通过离散化输运方程,以微分方程的形式描述气体或流体的宏观运动行为,通过在格子点上的分布函数进行更新来模拟流体的动态行为。
格子Boltzmann方法的基本原理可以总结为以下几点:1.分布函数:格子Boltzmann方法中,将流场看作是由离散的分布函数表示的,分布函数描述了在各个速度方向上的分布情况。
通过更新分布函数,模拟流体的宏观行为。
2.离散化模型:为了将连续的流场问题转化为离散的问题,格子Boltzmann方法将流场划分为一个个的格子点,每个格子点上都有一个对应的分布函数。
通过对分布函数进行离散化,实现流场的模拟。
3.背离平衡态:格子Boltzmann方法假设流体运动迅速趋于平衡态,即分布函数以指定的速度在各个方向上收敛到平衡分布。
通过在更新分布函数时引入碰撞过程,模拟流体的运动过程。
4.离散速度模型:分布函数描述了流体在各个速度方向上的分布情况,而格子Boltzmann方法中使用的离散速度模型决定了分布函数的更新方式。
常见的离散速度模型有D2Q9、D3Q15等。
2. 应用领域格子Boltzmann方法作为一种计算流体力学方法,已经在各个领域得到了广泛的应用。
以下是一些常见的应用领域:2.1 流体力学模拟格子Boltzmann方法具有良好的可并行性和模拟精度,适用于复杂流体流动的模拟。
它可以用于模拟包括自由表面流动、多相流动、多物理场耦合等在内的各种复杂流体力学问题。
2.2 细胞生物力学研究格子Boltzmann方法在细胞力学研究中也有广泛应用。
通过模拟流体在细胞表面的流动,可以研究细胞运动、变形和介观流的形成机制。
格子Boltzmann方法在细胞生物力学领域的应用已成为一个重要的研究方向。
2.3 多相流模拟格子Boltzmann方法在多相流动模拟中的应用也非常广泛。
Theory of the lattice Boltzmann method:From the Boltzmann equationto the lattice Boltzmann equationXiaoyi He 1,2,*and Li-Shi Luo 2,3,†1Center for Nonlinear Studies,MS-B258,Los Alamos National Laboratory,Los Alamos,New Mexico 875452Complex Systems Group T-13,MS-B213,Theoretical Division,Los Alamos National Laboratory,Los Alamos,New Mexico 875453ICASE,MS 403,NASA Langley Research Center,6North Dryden Street,Building 1298,Hampton,Virginia 23681-0001͑Received 29April 1997;revised manuscript received 26August 1997͒In this paper,the lattice Boltzmann equation is directly derived from the Boltzmann equation.It is shown that the lattice Boltzmann equation is a special discretized form of the Boltzmann equation.Various approxi-mations for the discretization of the Boltzmann equation in both time and phase space are discussed in detail.A general procedure to derive the lattice Boltzmann model from the continuous Boltzmann equation is dem-onstrated explicitly.The lattice Boltzmann models derived include the two-dimensional 6-bit,7-bit,and 9-bit,and three-dimensional 27-bit models.͓S1063-651X ͑97͒12512-0͔PACS number ͑s ͒:47.10.ϩg,47.11.ϩj,05.20.DdI.INTRODUCTIONIn the last few years,we have witnessed a rapid develop-ment of the method known as the lattice Boltzmann equation ͑LBE ͓͒1–6͔.Although only in its infancy,the LBE method has demonstrated its ability to simulate hydrodynamic sys-tems ͓1–5͔,magnetohydrodynamic systems ͓7͔,multiphase and multicomponent fluids ͓8͔including suspensions ͓9͔and emulsions ͓10͔,chemical-reactive flows ͓11͔,and multi-component flow through porous media ͓12͔.Together with modern computers of massively parallel processors,the LBE method has become a powerful computational method for studying various complex systems.The obvious advantages of the LBE method are the parallelism of the method,the simplicity of the programming,and the capability of incor-porating model interactions.However,a rigorous framework of the LBE method is still lacking in spite of the great inter-est in the method.Historically,the models of the lattice Boltzmann equation directly evolve from the models of the lattice-gas automata ͑LGA ͓͒13͔.While the LGA models are Boolean ones,the LBE models are indeed the floating-number counterpart of the corresponding LGA models—a particle in the LGA model ͑represented by a Boolean number ͒is replaced by the single-particle distribution function ͑represented by a real number ͒.Even though the LBE models appear to be rather different from their LGA counterparts because various approximations,such as the linearization of the collision op-erator ͓2͔and the Bhatnagar-Gross-Krook ͑BGK ͓͒15–17͔approximation ͓3͔,have been applied,the theoretical frame-work of the LBE method nevertheless rests upon the Chapman-Enskog analysis of the LGA method ͓14͔.Al-though the connection between the LBE models and the con-tinuous Boltzmann equation are discussed in various places ͓18,19͔,so far there exists no rigorous result in this direction.The lack of a thorough understanding of the LBE method has some immediate implications.For instance,the LBE method has not been very successful in simulating thermohydrody-namic systems ͓19–22͔,nor has the LBE method been able to be implemented on arbitrary mesh grids ͓5,23͔,even though considerable effort has been applied in these direc-tions.As we have shown recently ͓24–26͔,substantial progress can be made in the aforementioned areas once a better understanding of the LBE method is attained.Further-more,through the derivation we can directly show the con-nection between the LBE method and other newly developed gas kinetic methods ͓27–33͔.In this paper we will show that the lattice Boltzmann equation can be directly derived from the continuous Boltz-mann equation discretized in some special manner in both time and phase space.Our analysis shows that theoretically the lattice Boltzmann equation is independent of the lattice-gas automata.The lattice Boltzmann equation is a finite-difference form of the continuous Boltzmann equation.We provide a detailed account of an a priori derivation of the lattice Boltzmann equation from its continuous counter-part—the continuous Boltzmann equation.A general proce-dure to derive lattice Boltzmann models from the continuous Boltzmann equation is established.A number of LBE models in both two-and three-dimensional ͑2D,3D ͒space are de-rived to illustrate the procedure.The kinetic-model equation used in this paper is the BGK equation with a single relax-ation time ͓15–17͔.Although the BGK equation has its in-herent limitations and shortcomings,such as fixed Prandtl number,the equation can be generalized to remedy the short-comings ͓34͔.Therefore,it is sufficient to use the BGK equation for the purpose of studying hydrodynamics of simple fluids.This paper is organized as follows.In Sec.II we discuss the discretization of time for the Boltzmann BGK equation.In Sec.III we discuss the low Mach number ͑small velocity ͒expansion and the discretization of the phase space.In Sec.IV we derive the lattice Boltzmann 6-bit,7-bit,and 9-bit models in two-dimensional space and the 27-bit model in three-dimensional space.Section V concludes the paper.*Electronic address:xyh@†Electronic address:luo@PHYSICAL REVIEW E DECEMBER 1997VOLUME 56,NUMBER 6561063-651X/97/56͑6͒/6811͑7͒/$10.006811©1997The American Physical SocietyII.DISCRETIZATION OF THE BOLTZMANN EQUATIONIn the following analysis,we shall use the Boltzmann equation with the BGK,or single-relaxation-time,approxi-mation ͓15–17͔:ץfץt ϩ•ٌf ϭϪ1͑f Ϫg ͒,͑1͒where f ϵf (x ,,t )is the single-particle distribution function,is the microscopic velocity,is the relaxation time due to collision,and g is the Boltzmann-Maxwellian distribution function:g ϵ͑2RT ͒D /2exp ͩϪ͑Ϫu ͒22RT ͪ,͑2͒where R is the ideal gas constant,D is the dimension of the space,and ,u ,and T are the macroscopic density of mass,velocity,and temperature,respectively.The macroscopic variables,,u ,and T are the ͑microscopic velocity ͒mo-ments of the distribution function,f :ϭ͵f d ϭ͵gd ,͑3a ͒u ϭ͵f d ϭ͵gd ,͑3b ͒ϭ12͵͑Ϫu ͒2f d ϭ12͵͑Ϫu ͒2gd .͑3c ͒The energy can also be written in terms of temperature T :ϭD 02RT ϭD 02N A k BT ,͑4͒where D 0,N A ,and k B are the number of degrees of freedom of a particle,Avogadro’s number,and the Boltzmann con-stant,respectively.In Eqs.͑3͒,an assumption of Chapman-Enskog ͓16͔has been applied:͵h ͑͒f ͑x ,,t ͒d ϭ͵h ͑͒g ͑x ,,t ͒d ,͑5͒where h ()is a linear combination of collisional invariants ͑conserved quantities ͒h ͑͒ϭA ϩB •ϩC •.͑6͒In the above equation,A and C are arbitrary constants,and B is an arbitrary constant vector.A.Discretization of timeEquation ͑1͒can be formally rewritten in the form of an ordinary differential equation:d f dt ϩ1f ϭ1g ,͑7͒whered dt ϵץץtϩ•“is the time derivative along the characteristic line .The above equation can be formally integrated over a time step of ␦t :f ͑x ϩ␦t ,,t ϩ␦t ͒ϭ1e Ϫ␦t /͵␦te t Ј/g ͑x ϩt Ј,,t ϩt Ј͒dt Јϩe Ϫ␦t /f ͑x ,,t ͒.͑8͒Assuming that ␦t is small enough and g is smooth enoughlocally,the following approximation can be made:g ͑x ϩt Ј,,t ϩt Ј͒ϭͩ1Ϫt Ј␦tͪg ͑x ,,t ͒ϩt Ј␦tg ͑x ϩ␦t ,,t ϩ␦t ͒ϩO ͑␦t 2͒,0рt Јр␦t .͑9͒The leading terms neglected in the above approximation are of the order of O (␦t 2).With this approximation,Eq.͑8͒be-comesf ͑x ϩ␦t ,,t ϩ␦t ͒Ϫf ͑x ,,t ͒ϭ͑e Ϫ␦t /Ϫ1͓͒f ͑x ,,t ͒Ϫg ͑x ,,t ͔͒ϩͩ1ϩ␦t͑e Ϫ␦t /Ϫ1͒ͪϫ͓g ͑x ϩ␦t ,,t ϩ␦t ͒Ϫg ͑x ,,t ͔͒.͑10͒If we expand e Ϫ␦t /in its Taylor expansion and,further,neglect the terms of order O (␦t 2)or smaller on the right-hand side of Eq.͑10͒,then Eq.͑10͒becomes f ͑x ϩ␦t ,,t ϩ␦t ͒Ϫf ͑x ,,t ͒ϭϪ1͓f ͑x ,,t ͒Ϫg ͑x ,,t ͔͒,͑11͒where ϵ/␦t is the dimensionless relaxation time ͑in the unit of ␦t ͒.Therefore,Eq.͑11͒is accurate to the first order in ␦t .Equation ͑11͒is the evolution equation of the distribu-tion function f with discrete time.Although g is written as an explicit function of t ,the time dependence of g lies solely in the hydrodynamic variables ,u ,and T ͑the Chapman-Enskog ansatz ͓16͔͒,that is,g (x ,,t )ϭg (x ,;,u ,T ).Therefore,one must first compute ,u ,and T before constructing the equilibrium distribution function,g .Thus,the calculation of ,u ,and T becomes one of the most crucial steps in discretizing the Boltzmann equa-tion.B.Calculation of the hydrodynamic momentsIn order to numerically evaluate the hydrodynamic mo-ments of Eq.͑3͒,appropriate discretization in momentumspace must be accomplished.With appropriate discretiza-tion,integration in momentum space ͑with weight function g ͒can be approximated by quadrature up to a certain degree of accuracy,that is,681256XIAOYI HE AND LI-SHI LUO͵͑͒g͑x,,t͒dϭ͚␣W␣͑␣͒g͑x,␣,t͒,͑12͒where͑͒is a polynomial of,W␣is the weight coefficient of the quadrature,and␣is the discrete velocity set or the abscissas of the quadrature.Accordingly,the hydrodynamic moments of Eqs.͑3͒can be computed byϭ͚␣f␣ϭ͚␣g␣,͑13a͒uϭ͚␣␣f␣ϭ͚␣␣g␣,͑13b͒ϭ12͚␣͑␣Ϫu͒2f␣ϭ12͚␣͑␣Ϫu͒2g␣,͑13c͒wheref␣ϵf␣͑x,t͒ϵW␣f͑x,␣,t͒,͑14a͒g␣ϵg␣͑x,t͒ϵW␣g͑x,␣,t͒.͑14b͒It should also be noted that f␣͑or g␣)has the unit of f d͑or gd).III.DERIVATION OF THE LATTICE BOLTZMANN EQUATION AND ITS EQUILIBRIUM DISTRIBUTIONFUNCTIONThe lattice Boltzmann equation has the following ingredi-ents:͑1͒an evolution equation,in the form of Eq.͑11͒with discretized time and phase space of which configuration space is of a lattice structure and momentum space is re-duced to a small set of discrete momenta;͑2͒conservation constraints in the form of Eq.͑13͒;͑3͒a proper equilibrium distribution function which leads to the Navier-Stokes equa-tions.In what follows,the low Mach number expansion is first applied to the Boltzmann-Maxwellian distribution func-tion.Then quadrature to approximate the integration in the momentum spaceis discussed in detail for a variety of LBE models in both2D and3D space.Through the quadra-ture,the lattice structure,and the equilibrium distribution function of the LBE are constructed.A.Low-Mach-number approximationIn the lattice Boltzmann equation,the equilibrium distri-bution function is obtained by a truncated small velocity ex-pansion͑or low-Mach-number approximation͓͒14͔.The same can be done here:gϭ2RT D/2exp͑Ϫ2/2RT͒exp͕͑•u͒/RTϪu2/2RT͖ϭ͑2RT͒D/2exp͑Ϫ2/2RT͒ϫͭ1ϩ͑•u͒RTϩ͑•u͒22͑RT͒2Ϫu22RTͮϩO͑u3͒.͑15͒With the equilibrium distribution function g of the aboveform,Eq.͑11͒bears a strong resemblance to the latticeBoltzmann equation,and what remains to be accomplished isthe discretization of phase space.For convenience,the fol-lowing notation for the equilibrium distribution function withtruncated small velocity expansion shall be used in what fol-lows:f͑eq͒ϭ͑2RT͒D/2exp͑Ϫ2/2RT͒ϫͭ1ϩ͑•u͒RTϩ͑•u͒22͑RT͒2Ϫu22RTͮ.͑16͒Although f(eq)only retains the terms up to O(u2),it is trivialto maintain high-order terms of u in the above expansion ifnecessary.B.Discretization of phase spaceThere are two considerations in the discretization of phasespace.First of all,the discretization of momentum space iscoupled to that of configuration space such that a latticestructure is obtained.This is a special characteristic of thelattice Boltzmann equation.Second,the quadrature must beaccurate enough so that not only the conservation constraintsof Eq.͑13͒are preserved exactly,but also the necessary sym-metries required by the Navier-Stokes equations are retained.In deriving the Navier-Stokes equations from the Boltz-mann equation via the Chapman-Enskog analysis͓16,17͔,thefirst two order approximations of the distribution func-tion͑i.e.,f(eq)and f(1)͒must be considered.Therefore,giventhe equilibrium distribution function,f(eq)of Eq.͑16͒,thequadrature used to evaluate the hydrodynamic moments mustbe able to compute the following moments with respect tof(eq)exactly::1,i,ij,͑17a͒u:i,ij,ijk,͑17b͒T:ij,ijk,ijkl,͑17c͒wherei is the component ofin Cartesian coordinates.͑Wehave assumed that the particle only has the linear degree offreedom,i.e.,D0ϭD.͒Thus,to obtain the Navier-Stokesequations,one must be able to evaluate the moments of1,,...,6with respect to weight function exp(Ϫ2/2RT)ex-actly,owing to the small u expansion of g.For isothermalmodels,which are discussed in what follows,the momentsthat are to be evaluated are1,,...,5.Calculating the hydrodynamic moments of f(eq)is equiva-lent to evaluating the following integral in general:Iϭ͵͑͒f͑eq͒dϭ͑2RT͒D/2͵͑͒exp͑Ϫ2/2RT͒ϫͭ1ϩ͑•u͒RTϩ͑•u͒22͑RT͒2Ϫu22RTͮd,͑18͒where͑͒is a polynomial of.The above integral has thefollowing structure:566813THEORY OF THE LATTICE BOLTZMANN METHOD:...͵e Ϫx 2͑x ͒dx ,which can be calculated numerically with Gaussian-type quadrature ͓35͔.Our objective is to use quadrature to evalu-ate the hydrodynamic moments ͓,u ,and T ,given by Eq.͑3͔͒.Given proper discretization of phase space,we can evaluate the above integral with desirable accuracy.In the meantime,the lattice Boltzmann equation with appropriate equilibrium distribution function can also be derived.TTICE BOLTZMANN MODELS IN TWO-AND THREE-DIMENSIONAL SPACE A.Two-dimensional 6-bit and 7-bit triangular lattice modelThe 7-bit model is constructed on a two-dimensional (D ϭ2)triangular lattice space.The triangular lattice has the necessary rotational symmetry required by the hydrodynam-ics ͓14͔.The polar coordinates of space,͑,͒,are used here.For the sake of simplicity but without losing generality,assumingm ,n ͑͒ϭ͑ͱ2RT ͒m ϩn m ϩncos m sin n,͑19͒where ϭ/ͱthe integral in Eq.͑18͒becomesI ϭ͵m ,n ͑͒f͑eq ͒d ϭ͑ͱ2RT ͒m ϩn͵02͵ϱe Ϫ2m ϩn cos m sin n ϫͭ1ϩ2͑eˆ•u ͒ͱ2RTϩ2͑eˆ•u ͒2RT Ϫu 22RT ͮd d ,͑20͒where eˆϭ(cos ,sin ).To obtain the 7-bit lattice Boltzmann equation on the triangular lattice space,the angular variable must be discretized evenly into six sections in the interval ͓0,2͒,that is,␣ϭ(␣Ϫ1)/3,for ␣ϭ͕1,2,...,6͖.With the discretization of ,we have͵2cos m sin n d ϭͭ3͚␣ϭ16cos m ␣sin n ␣,͑m ϩn ͒even 0,͑m ϩn ͒odd͑21͒for (mϩn )рing the above result,we obtainI ϭΆ3͑ͱ2RT ͒m ϩn͚␣ϭ16cos m ␣sin n ␣ͭͩ1Ϫu 22RT ͪI m ϩn ϩ͑eˆ␣•u ͒2RTI m ϩn ϩ2ͮ,͑m ϩn ͒even3͑ͱ2RT ͒m ϩn͚␣ϭ16cos m ␣sin n ␣2͑eˆ␣•u ͒ͱ2RT I m ϩn ϩ1,͑m ϩn ͒odd,͑22͒where eˆ␣ϭ(cos ␣,sin ␣),and I m ϭ͵ϩϱ͑e Ϫ2͒m d ͑23͒is the m th moment with respect to the weight functione Ϫ2.Since the 7-bit model only has two speeds ͑i.e.,n ϭ2͒and one of them is fixed at 0,it is clear that the abscissas of the quadrature to evaluate I m should be 0ϭ0and 1ϭ␥Ϫ1,where ␥is a positive parameter to be adjusted later.The Radau-Gauss formula ͓35͔is a natural choice to evaluate the integral I m :I m ϭ00m ϩ͚j ϭ1nj j m .For the 7-bit model,n ϭ1,and the integrals needed to be evaluated are I 0,I 2,and I 4.͓I 1and I 3do not play any role because of the symmetry of the integral I ,as shown in Eq.͑22͒.͔Then,we have the following three equations:I 0ϭ0ϩ1ϭ1/2,͑24a ͒I 2ϭ1␥Ϫ2ϭ1/2,͑24b ͒I 4ϭ1␥Ϫ4ϭ1,͑24c ͒of which,the solutions are0ϭ1/4,͑25a ͒1ϭ1/4,͑25b ͒␥ϭ1/&.͑25c ͒Therefore,we haveI m ϭ14͑0m ϩ1m͒,m ϭ0,2,4.͑26͒Note that the above quadrature for I m is exact for m ϭ0,2,and 4.Consequently,the following equality is expected to be exact for (m ϩn )р5:681456XIAOYI HE AND LI-SHI LUOIϭ12͑ͱ2RT͒mϩn͚␣ϭ16cos m␣sin n␣ͭͩ1Ϫu22RTͪϫ͑0mϩnϩ1mϩn͒ϩ2͑eˆ␣•u͒ͱ2RT͑0mϩnϩ1ϩ1mϩnϩ1͒ϩ͑eˆ␣•u͒2RT͑0mϩnϩ2ϩ1mϩnϩ2͒ͮϭ2m,n͑0͒ͭ1Ϫu22RTͮϩ12͚␣ϭ16m,n͑␣͒ϫͭ1ϩ͑␣•u͒RTϩ͑␣•u͒22͑RT͒2Ϫu22RTͮ,whereʈ0ʈϭͱ2RT0ϭ0and␣ϭͱ2RT1eˆ␣ϭ2ͱRT eˆ␣.It becomes obvious that the equilibrium distribution function for the7-bit model isf␣͑eq͒ϭw␣ͭ1ϩ4͑e␣•u͒c2ϩ8͑e␣•u͒2c4Ϫ2u2c2ͮ,͑27͒where␣͕0,1,2,...,6͖,cϭ␦x␦t͑28͒is‘‘the speed of light’’in the system͑which is usually set to be unity in the literature͒,e␣ϭͭ͑0,0͒,␣ϭ0͑cos␣,sin␣͒c,␣ϭ͑␣Ϫ1͒/3,␣ϭ1,2,...,6͑29͒andw␣ϭͭ1/2,␣ϭ01/12,␣ϭ1,2, (6)͑30͒Note that the substitution of RTϭc s2ϭc2/4,where c s is the sound speed of the system,has been made in Eq.͑27͒,and RTϭc s2ϭc2/4is equivalent toʈ␣ʈϭc for␣ 0.The coef-ficient W␣defined in Eq.͑14͒is explicitly given byW␣ϭ͑2RT͒D/2e␣2/2RT w␣.͑31͒Similarly,the equilibrium distribution function for the6-bit LBE model,which is a degenerate case of the7-bit model,can be obtained easily by solving two equations for one abscissa and the weight coefficient of the Hermite-Gauss formula͓35͔with modification for the integral on half real axis͓36͔.The solutions are1ϭ12and␥ϭ1.Then,we havef␣͑eq͒ϭ6ͭ1ϩ2͑e␣•u͒c2ϩ4͑e␣•u͒2c4Ϫu2c2ͮ,͑32͒where␣͕1,2,...,6͖.The sound speed of the6-bit LBE model is c sϭc/&,and w␣ϭ16.B.Two-dimensional9-bit square lattice modelTo recover the9-bit LBE model on square lattice space, the Cartesian coordinate system is used and,accordingly,͑͒can be set tom,n͑͒ϭx my n,wherex andy are the x and y components of.The integral of the moments,defined by Eq.͑18͒,becomesIϭ͵m,n͑͒f͑eq͒dϭ͑ͱ2RT͒mϩnͭͩ1Ϫu22RTͪI m I n ϩ2͑u x I mϩ1I nϩu y I m I nϩ1͒ͱ2RTϩu x2I mϩ2I nϩ2u x u y I mϩ1I nϩ1ϩu y2I m I nϩ2RTͮ,͑33͒whereI mϭ͵ϪϱϩϱeϪ2m d,ϭ/ͱ2RTis the m th order moment of the weight function eϪ2on the real axis.Naturally,the third-order Hermite formula͓35͔is the optimal choice to evaluate I m for the purpose of deriving the9-bit LBE model:I mϭ͚jϭ13jj m.The three abscissas of the quadrature are1ϭϪͱ3/2,2ϭ0,3ϭͱ3/2,͑34͒and the corresponding weight coefficients are1ϭͱ/6,2ϭ2ͱ/3,3ϭͱ/6.͑35͒Then,the integral of the moment in Eq.͑33͒becomesIϭ͚i,jϭ13ij͑i,j͒ͭ1ϩ͑i,j•u͒RTϩ͑i,j•u͒22͑RT͒2Ϫu22RTͮ,͑36͒wherei,jϭ(i,j)ϭͱ2RT(i,j).Obviously,we can identify the equilibrium distribution function withf i,j͑eq͒ϭijͭ1ϩ͑i,j•u͒RTϩ͑i,j•u͒22͑RT͒2Ϫu22RTͮ.͑37͒Employing the notations of566815THEORY OF THE LATTICE BOLTZMANN METHOD:...e ␣ϭͭ͑0,0͒,␣ϭ0͑cos ␣,sin ␣͒c ,␣ϭ͑␣Ϫ1͒/2,␣ϭ1,2,3,4&͑cos ␣,sin ␣͒c ,␣ϭ͑␣Ϫ5͒/2ϩ/4,␣ϭ5,6,7,8͑38͒andw ␣ϭi jϭͭ4/9,i ϭj ϭ2,␣ϭ01/9,i ϭ1,j ϭ2,...,␣ϭ1,2,3,41/36,i ϭj ϭ1,...,␣ϭ5,6,7,8,͑39͒and with the substitution of RT ϭc s 2ϭc 2/3͑or ʈͱ2RT 1ʈϭͱ3RT ϭc ͒,we obtain the equilibrium distribution function of the 9-bit LBE model:f ␣͑eq ͒ϭw ␣ͭ1ϩ3͑e ␣•u ͒c 2ϩ9͑e ␣•u ͒22c 4Ϫ3u 22c2ͮ.͑40͒C.Three-dimensional 27-bit square lattice modelThe 27-bit LBE model in 3D square lattice space is a straightforward extension of the 2D 9-bit model.The abscis-sas i ,as well as the corresponding weight coefficients i ,of the quadrature to evaluate the moments remain the same.Therefore,I ϭ3/2͚i ,j ,k ϭ13i j k ͑i ,j ,k ͒ϫͭ1ϩ͑i ,j ,k •u ͒RT ϩ͑i ,j ,k •u ͒22͑RT ͒2Ϫu 22RTͮ,͑41͒where i ,j ,k ϭ(i ,j ,k )ϭͱ2RT (i ,j ,k ).With the fol-lowing notations similar to the 9-bitmodel,e ␣ϭΆ͑0,0,0͒,␣ϭ0͑Ϯ1,0,0͒c ,͑0,Ϯ1,0͒c ,͑0,0,Ϯ1͒c ,␣ϭ1,2,...,6͑Ϯ1,Ϯ1,0͒c ,͑Ϯ1,0,Ϯ1͒c ,͑0,Ϯ1,Ϯ1͒c ,␣ϭ7,8,...,18͑Ϯ1,Ϯ1,Ϯ1͒c ,␣ϭ19,20,...,26͑42͒and w ␣ϭi j k3/2ϭΆ8/27,i ϭj ϭk ϭ2,␣ϭ02/27,i ϭj ϭ2,k ϭ1,...,␣ϭ1,2,...,61/54,i ϭj ϭ1,k ϭ2,...,␣ϭ7,8,...,181/216,i ϭj ϭk ϭ1,...,␣ϭ19,20, (26)͑43͒we obtain the equilibrium distribution function of the 27-bit LBE model:f ␣͑eq ͒ϭw ␣ͭ1ϩ3͑e ␣•u ͒c 2ϩ9͑e ␣•u ͒22c 4Ϫ3u 22c2ͮ.͑44͒Note that the f ␣(eq)of the 27-bit model is exactly the same as that of the 9-bit model except for the values of the coeffi-cients w ␣.Also,the sound speed of the 27-bit model is identical to that of the 9-bit model because in both modelsRT ϭc s 2ϭc 2/3.V.CONCLUSIONRecently,a number of gas kinetic methods have been de-veloped for solving Navier-Stokes equations of simple andcomplex fluids ͓27–33͔.All these methods are based uponthe solution of the BGK equation ͑1͒.In particular,these gas kinetic methods are basically a finite-volume solution of Eq.͑8͒and they do not require discretization of the velocity space .While the lattice Boltzmann equation is in the in-compressible limit due to the small-Mach-number expansion,the gas kinetic methods are particularly suitable for shock capture.In conclusion,we have derived lattice Boltzmann models from the Boltzmann BGK equation,which is completely in-dependent of lattice-gas automata.The derivation directly connects the lattice Boltzmann equation to the Boltzmann equation;thus,the framework of the lattice Boltzmann equa-tion can rest on that of the Boltzmann equation and the rig-orous results of the Boltzmann equation can be extended to the lattice Boltzmann equation via this explicit 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Printed by Jouve, 75001 PARIS (FR)(19)E P 3 528 146A 1TEPZZ¥5 8_46A_T(11)EP 3 528 146A1(12)EUROPEAN PATENT APPLICATION(43)Date of publication:21.08.2019Bulletin 2019/34(21)Application number: 19158256.8(22)Date of filing: 20.02.2019(51)Int Cl.:G06F 17/50(2006.01)(84)Designated Contracting States:AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR Designated Extension States: BA MEDesignated Validation States: KH MA MD TN(30)Priority:20.02.2018US 201862632584 P13.02.2019US 201916274403(71)Applicant: Dassault Systemes Simulia Corp.Johnston, RI 02919 (US)(72)Inventors:•GOPALAKRISHNAN, Pradeep Westford, MA 01886 (US)•ZHANG, RaoyangBurlington, MA 01803 (US)•CHEN, HudongNewton, MA 02465 (US)(74)Representative: Conroy, JohnFish & Richardson P.C. Highlight Business Towers Mies-van-der-Rohe-Straße 880807 München (DE)(54)LATTICE BOLTZMANN BASED SOLVER FOR HIGH SPEED FLOWS(57)Techniques for simulating fluid flow on a com-puter that involve a stable entropy solver are described.The techniques include simulating activity of a fluid across a mesh, the activity of the fluid being simulated so as to model movement of particles across the mesh,storing, in a computer accessible memory, a set of state vectors for each mesh location in the mesh, each of the state vectors comprising a plurality of entries that corre-spond to particular momentum states of possible mo-mentum states at a corresponding mesh location, simu-lating a time evolution of entropy of the flow by collecting incoming set of distributions from neighboring mesh lo-cations for the collision operation, calculating by the com-puter scalar values in each location, determining outgo-ing distributions as a product of the collision operation and addition of a heat source, and modifying the flow by the computer performing for a time interval, an advection of the particles to subsequent mesh locations.EP 3 528 146A12510152025303540455055DescriptionClaim of Priority under 35 U.S.C. §119[0001]This application claims priority under 35 U.S.C. §119 to U.S. Provisional Patent Application Serial No.62/632,584, filed February 20, 2018, and entitled "Lattice Boltzmann based solver for high speed flows", the entire contents of which is incorporated herein by ttice Boltzmann Based Solver for High Speed FlowsBACKGROUND[0002]Lattice Boltzmann methods (LBM) is a class of computational fluid dynamics (CFD) methods for fluid simulation.Instead of solving the Navier-Stokes equations, the discrete Boltzmann equation is solved to simulate the flow of a Newtonian fluid with collision models such as Bhatnagar-Gross-Krook (BGK). By simulating streaming and collision processes across a limited number of particles, the intrinsic particle interactions evince a microcosm of viscous flow behavior applicable across the greater mass.SUMMARY[0003]According to an aspect, a method for simulating fluid flow on a computer includes simulating activity of a fluid across a mesh, the activity of the fluid being simulated so as to model movement of particles across the mesh, storing,in a computer accessible memory, a set of state vectors for each mesh location in the mesh, each of the state vectors comprising a plurality of entries that correspond to particular momentum states of possible momentum states at a corresponding mesh location, simulating a time evolution of entropy of the flow by collecting incoming set of distributions from neighboring mesh locations for the collision operation, calculating by the computer scalar values in each location,determining outgoing distributions as a product of the collision operation and addition of a heat source, and modifying the flow by the computer performing for a time interval, an advection of the particles to subsequent mesh locations.[0004]One or more of the following features may be included in the above aspect.[0005]Simulating activity of the fluid flow includes simulating the fluid flow based in part on a first set of discrete lattice speeds, and the method further includes simulating time evolution of the entropy quantity, based in part on a second set of discrete lattice speeds. The second set of discrete lattice speeds are the same lattice speeds as the first set of discrete lattice speeds. The method further includes calculating by the computer, higher order error terms from incoming lattice set vectors, determining average values of the higher order error terms, and subtracting the average values of the higher order error terms from the collision operator. The additional heat source term is computed and added to the second states, and the method further includes calculating by the computer, the effect of heating by fluid viscosity and heating by fluid conduction and calculating by the computer, the entropy diffusion and removing this from the additional heat source term. The method further includes calculating by the computer, a set of physical quantities for the mesh locations in the mesh. The method provides a Lattice Boltzmann entropy solver that avoids a second order velocity term.The collision operator involves a non-equilibrium computation without any second order terms in velocity. The method further includes solving for entropy by providing an additional set of lattice vectors, q i , to represent the specific entropy,s,andwiththetimeevolutionofthosestatesbeinggivenbywhere q i are lattice vectors,x is direction c i is velocity of states, Δt is a change in time t,is lattice vectors at equilibrium, τq is the relaxationtime, V isis the non-equilibrium contribution, p is pressure, and Q s is the additional heat source. The methodwherein the collision operator is related to:[0006]Other aspects include computer program products and apparatuses such as computers and data processing。
任意复杂流-固边界的格子boltzmann处理方法格子Boltzmann方法(Lattice Boltzmann Method,简称LBM)是一种基于纳维-斯托克斯方程的数值模拟方法,常用于模拟流体力学问题。
与传统的有限差分或有限元方法相比,LBM具有计算效率高、易于并行化、适用于复杂流动及多相流问题等优势。
本文将介绍LBM中的复杂流-固边界处理方法。
复杂流问题通常包含流动边界条件的变化和障碍物的存在。
在LBM中,复杂流问题的处理可以通过适当的边界条件和碰撞模型来实现。
其中流动边界条件可以分为两类:无滑移条件和有滑移条件。
对于无滑移条件,例如在固壁上的边界,可以通过在碰撞模型中使用零速度处理。
这意味着在碰撞过程中,与固边界接触的格子在没有外力作用下速度为零,从而达到无滑移的效果。
另外,可以使用对流边界条件将流体粒子反弹回正常流动区域,以实现边界的没有渗漏。
对于有滑移条件,例如在光滑壁面上的边界,可以通过引入边界反弹修正来模拟流体在边界上发生的滑移。
边界反弹修正的思想是,将反弹的粒子在碰撞过程中根据碰撞方向和法线方向进行修正。
通过与周围格子的动量交换,能够保持正确的边界斜率,从而实现流体在光滑壁面上的滑移效果。
对于存在障碍物的问题,可以通过在碰撞过程中将格子标记为障碍物,从而阻挡流体粒子通过。
在流体粒子逼近障碍物时,可以根据格子状态调整流体粒子的速度或方向,模拟粒子在障碍物上的反射、散射和吸附等作用。
此外,对于复杂几何形状的障碍物,可以使用体网格方法或层次网格方法进行建模,提高对障碍物的模拟精度。
总之,格子Boltzmann方法能够有效处理任意复杂流-固边界问题。
通过适当的边界条件和碰撞模型,能够模拟出流体在无滑移和有滑移边界上的行为,并模拟出流体与障碍物的相互作用。
对于复杂几何形状的障碍物,可以使用不同的建模方法来提高模拟精度。
格子Boltzmann方法的这些特性使其成为模拟复杂流动的有力工具,广泛应用于流体力学和多相流领域。
基于Lattice-Boltzmann方法的多孔介质真空绝热特性阚安康;吴亦农;徐志峰;张安阔;王为【摘要】作为真空绝热板的芯材,多孔介质微尺度空间形貌结构及物性参数对其绝热性能影响较大.为研究多孔介质真空下的导热性能,选择颗粒状、纤维状和泡沫状3种典型多孔介质材料,并基于Lattice-Boltzmann(LBM)提出了一种随机构造多孔介质物理模型的方法.模型中重要参数结合多孔介质电镜扫描图像处理获取.采用D3Q15 LBM模型进行数值模拟,并分析了真空度及颗粒/纤维/泡孔等效直径对导热系数的影响规律.模拟与实验的对比结果揭示了多孔介质真空下的导热系数随真空度及多孔介质物性参数的变化规律.%As the core material of the vacuum insulation panel,micro structure and physical properties of porous materials have great influence on the thermal insulation performance.To study the thermal con-ductivity of porous material under vacuum condition,three typical porous materials,that is,granular, fibrous and foamy materials are involved in this paper,and a physical model based on the Lattice-Boltz-mann method (LBM)is proposed.The key parameters of the model are obtained by scanning electron microscope image from real porous materials.Numerical simulation and analysis are made to study the influence of the vacuum degree and the equivalent diameter of the particles/fiber/cell hole versus thermal conductivity by D3Q15 model based on parison of simulation and experimental results ex-plores the principles of the thermal conductivity variation versus physical properties and vacuum degree. The work is significant to optimize thestructure of core materials and to improve the accuracy of effec-tive thermal conductivity prediction.【期刊名称】《南京航空航天大学学报》【年(卷),期】2017(049)001【总页数】7页(P17-23)【关键词】真空绝热板;多孔介质材料;Lattice-Boltzmann方法;导热系数;真空度【作者】阚安康;吴亦农;徐志峰;张安阔;王为【作者单位】中国科学院上海技术物理研究所,上海,200083;上海海事大学商船学院,上海,201306;中国科学院上海技术物理研究所,上海,200083;上海海事大学商船学院,上海,201306;中国科学院上海技术物理研究所,上海,200083;上海海事大学商船学院,上海,201306【正文语种】中文【中图分类】TK121真空绝热板(Vacuum insulation panel, VIP)以其导热系数极低、质轻、厚度小等诸多优点,而被广泛应用于家用电器、冷藏运输、建筑等领域[1-6]。
坡面流运动方程的latticeboltzmann 方法求解
坡面流(Slope Flow)是地球物理学领域研究的对象,其主要是分析非均匀电
荷质的运动行为特点,包括非线性稳定性、发展性和耗散等特征。
Lattice Boltzmann方法(LBM)作为一种高效而高精度的数值处理技术,被广泛用于解决
坡面流运动方程。
由于坡面流涉及特殊复杂的流体动力学结构,与传统的物理学理论有较大的不同,因此,使用习惯的数学计算方法往往会发现一些困难。
这不是说传统的研究方法完全不灵活或没有可行性,而是传统研究方法在处理坡面流方程时存在着计算复杂度大、工程量大的问题。
因此,开发一种高效的研究方法来解决坡面流方程……
Lattice Boltzmann方法(LBM)可以有效解决上述问题。
它是一种以离散技
术解决流体动力学问题的古典方法,利用自由粒子以定义的速度在节点之间传播,最终可以获得精确的流场信息。
LBM具有计算高效率、数值精确度高以及易于实现
等特点,从而可以有效地解决坡面流运动方程。
基于此,近年来,LBM在解决坡面流运动方程上被广泛应用。
无论是坡度大小、天然环境还是人为影响因素,LBM的数值模拟处理都非常准确,可以有效预测坡面
流的不稳定运动,为坡面流的研究提供了较高的可信度和准确性。
除此之外,LBM
的实现还很方便,精度也可以得到满意的控制,所以更具有实际应用价值。
总而言之,Lattice Boltzmann方法(LBM)拥有非常强大的数值模拟处理能力,具有高效率、易实现和高精度等优势,因此得到了有关研究者的大力推广,应用于坡面流运动方程的求解,取得了非常可观的成果。
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDSInt.J.Numer.Meth.Fluids2007;53:333–351Published online19June2006in Wiley InterScience().DOI:10.1002/fld.1282Lattice Boltzmann-based single-phase method for free surfacetracking of droplet motionsXiu Qing Xing,David Lee Butler∗,†and Chun YangSchool of Mechanical and Aerospace Engineering,Nanyang Technological University,50Nanyang Avenue,Singapore639798,SingaporeSUMMARYA2D single-phase free surface tracking model,based on the lattice Boltzmann method(LBM)is developed for simulating droplet motion.In contrast to the conventional multi-phase models,it is not necessary to simulate the motion of the gas phase using this2D single-phase algorithm,and thus improves the computational efficiency without sacrificing the underlying physics.A method for special treatment of the relevant boundary conditions in the single-phase algorithm is proposed and also validated.Numerical simulations are carried out for the motion of a free falling droplet with or without considering gravity and droplet spreading under gravity.The simulations of the LBM are found to be consistent with the results obtained from commercial computationalfluid dynamics(CFD)software Fluent.Copyright2006John Wiley&Sons,Ltd.Received1February2006;Revised20April2006;Accepted23April2006KEY WORDS:lattice Boltzmann method;free surface tracking;boundary conditions1.INTRODUCTIONLattice Boltzmann method(LBM)started to attract an attention in the CFDfield only recently and it has been particularly successful in simulations offluidflow applications involving complicated boundaries and complexfluids.Examples can be found for the Rayleigh–Taylor instability between twofluids in Reference[1],flows with substantial shock waves in Reference[2],processes with chemical reaction in References[3,4],turbulent combustion in Reference[5],particles suspended influids performed by Ladd[6–8],Ladd and Verberg[9],Behrand[10],Aidun and Lu[11],and Masselot and Chopard[12],dynamics of colloidal systems in References[13–16],flow dynamicsCorrespondence to:David Lee Butler,School of Mechanical and Aerospace Engineering,Nanyang Technological University,50Nanyang Avenue,Singapore639798,Singapore.†E-mail:mdlbutler@.sgContract/grant sponsor:Agency for Science,Technology and Research;contract/grant number:0421010018Copyright2006John Wiley&Sons,Ltd.334X.Q.XING,D.L.BUTLER AND C.YANGin porous media in References[17–19],polymer solutions in Reference[20],mass/heat transferflows in References[21–25].The simulation offlows with free surfaces is important in a variety of technical applicationssuch asflow through porous media,boiling dynamics and etching processes.Difficulties associatedwith the simulation of these types of problems lie in modelling the interface dynamics and dealingwith the complex boundaries,as well as chemical reactions betweenfluid and solid surfaces.Theobjective of this paper is therefore to develop an LBM-based single-phase model for simulationofflows with free surfaces.In the literature,several LBM models to simulate the interfacial dynamics have been successfullydeveloped in the past decade.A brief review is provided here.(a)Colour model.The colour model proposed by Gunstensen and Rothman[26]for simulatingimmiscible binaryfluids is based on Lattice Gas model of Rothman and Keller[27].Thedistribution function f i on link i is assumed to be the sum of the two colour distributionfunctions denoted by superscript r(red)and b(blue),f i=f r i+f b i.The distribution function is collided according to the lattice Boltzmann equation to give the new distribution functionf .The new distribution function f is then perturbed to an alternative value f according tothe colour gradient g(x)and the angle between the colour gradient vector and the coordinateaxes.g(x)is defined byg(x)=ije j[f r j(x+e i)−f b i(x+e i)](1)where e is the velocity vector.New distribution functions for the red and bluefluid can be then obtained by solving a maximization problem subjected to the conservation of mass and conservation of colour.Grunau et al.[28]extended this model to allow for a variation of density and viscosity.D’Ortona et al.[29]studied the surface tension and wetting properties by modifying this model and obtained good agreement with theory.Nie et al.[30]extended the model of Gunstensen and Rothman[26]and Grunau et al.[28]for studying the two-dimensional Rayleigh–Taylor instability,and good agreement with experimental and analytical studies was obtained.(b)Method of Shan and Chen.Shan and Chen[31,32]and Shan and Doolen[33]considered afluid with S different components on a regular lattice.In order to include the surface tension, they modified the LBM equation to:f k i(x+e i,t+1)=f k i(x,t)−f k i−f k(eq)i+e i·F k(x)(2)Here the force on the k th phase owing to a pairwise interaction between different phases is given by F k(x).f i(x,t)is the particle velocity distribution function at position x and time t along the i th direction. is the relaxation time.It should be noted that the collision operatore i·F k(x)in this model does not satisfy local momentum conservation.(c)Free energy approach.This model was proposed by Orlandini et al.[34]and Swift et al.[35,36]for simulating binaryfluids and liquid–gasfluids.In this model,the total density and the density difference are considered instead of using the densities of the two separatefluids r and b as the colour model does.Two distribution functions f i and i areCopyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351LB-BASED SINGLE-PHASE METHOD335 then used to describe the population of and ,respectively,on each of the i links.Both distribution functions are governed by the single relaxation time Boltzmann equation.This model was used by Buick[37]and Buick et al.[38]to simulate interfacial waves between twofluids,Xu et al.[39]to simulate phase-separating binaryfluids under an oscillatory shear,Dupuis and Yeomans[40,41]to simulate droplets on superhydrophobic surfaces and chemical heterogeneous surfaces,Palmer and Rector[42]to simulate thermal two-phaseflow,Takada et al.[43]to simulate bubble motion under gravity,and Zhang et al.[44]to simulate droplet movements and continuousflows in rough and hydrophobic microchannels.The drawback of the free energy model is that the liquid–gas model is not Galilean invariant.(d)Model of He et al.He et al.[1,23]proposed a lattice Boltzmann scheme for simulation ofmultiphaseflows with nearly incompressible limits.In their model,the interfacial dynamics such as phase segregation and surface tension are modelled by incorporating molecular interactions.An index function is introduced to track interfaces between different phases.Application to Rayleigh–Taylor instability yielded satisfactory results without any artifi-cial reconstruction steps.This model was also validated in Reference[45]to simulate the spreading of a liquid droplet on a heterogeneous surface.(e)Free surface models.Besides the multiphase and multi-component models,there are a coupleof free surface models developed for simulating the moving interface between immiscible gas and liquidfluids.The distinctive feature of the free surface models is that the stream and collision processes are carried out only in the lattice units or cells that are occupied partially or fully by thefluid.This significantly reduces the computational complexity of the methods.It allows a relatively simple treatment of the free surface boundary conditions with high computational efficiency but without sacrificing the underlying physics.One free surface model proposed by Ginzburg and Steiner[46]assumes that the collision only occurs on the‘active’cells which are fully or partiallyfilled withfluid.The mass fraction of a cell filled withfluid,which is between zero and one,is an additional variable.A‘re-colouring operator’similar to that in the model of Gunstensen and Rothman[26]determines the redistribution offluid mass carried by each particle population.The macroscopic variables propagate together with the particle distribution functions in the stream step.The unknown particle distribution functions at the front of free surfaces,which cannot be obtained by the usual LBM,are constructed by using thefirst-order Chapman–Enskog expansion of the distribution functions.The drawback of this algorithm is that the construction steps are quite complicated.Another free surface algorithm for gas–liquid interface simulations was proposed by Th¨u rey [47],Th¨u rey and R¨u de[48],Th¨u rey et al.[49],and K¨o rner et al.[50].It was originally developed for the simulation of metal foams to optimize and enhance the production process.The main character of this method is that the gas phase has negligible influence on the liquid phase and thus is not simulated as afluid.Then the reconstruction step becomes quite easy.Due to its simplicity,high computational efficiency and low memory request,we will use this model as our basic model.This paper is organized as follows:the single-phase free surface model is introducedfirst.Then, a method is proposed for special treatment of the relevant boundary conditions and incorporating external forces like gravity.After that the validation is carried out by simulating a free falling droplet with or without gravity and droplet spreading on a wall surface.The paper will conclude with a discussion on the results obtained.Copyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351336X.Q.XING,D.L.BUTLER AND C.YANG2.ALGORITHM2.1.General LBM algorithm D2Q9BGK modelStandard lattice topology classification D2Q9BGK model is given by[51]:f i(x+e i,t+ t)=f i(x,t)−f i−f eq i(3)where superscript eq denotes the equilibrium state.Velocity vectors e=(0,0),(1,0),(0,1), (−1,0),(0,−1),(1,1),(−1,1),(−1,−1),(1,−1)in lattice units are shown in Figure1.The density and velocity can be calculated by summation of all the distribution functions for one cell:=8i=0f i, u=8i=0e if i(4)The equilibrium distribution function can be chosen asf eq i=w i+e i·uc+(e i·u)22c−u22c(5)where c2=13,and w i=4/9for i=0,w i=1/9for i=1,2,3,4,and w i=1/36for i=5,6,7,8.2.2.Single-phase free surface modelUnlike other multi-phase models in which separate particle distribution functions are used for each phase,the single-phase free surface algorithm does not include the gas phase in the simulation. The different phases only use a same lattice,and are distinguished byflags for each lattice cell inthe grid.Each cell might have three types:filled(fluid)cell,interface cell or empty(gas)cell.TheFigure1.Topology of a D2Q9lattice.Copyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351LB-BASED SINGLE-PHASE METHOD 337complicated part of this model lies in how to deal with the interface cells,which form a closed layer between the fluid and empty cells.In this model,tracking the free surface consists of three steps:the computation of the interface movement,the reconstruction of the unknown distribution functions at the fluid interface streamed from empty cells into interface cells,and the re-initialization of the cell types.The basic idea of free surface tracking is similar to Threy’s model but the wall boundary conditions and gravity are treated using a method proposed in this study.The overview of the algorithm by Th¨u rey et al .[49]is provided below.2.2.1.Interface movement.The movement of the fluid interface is tracked by calculating the mass m contained in each cell and the fluid fraction which equals to the mass divided by the density ,i.e. =m / .The flux between two cells can be directly computed from the values that are streamed between two adjacent cells.For an interface cell at x and a fluid cell at (x +e i ),the mass flux is expressed asm i (x ,t + t )=f i opp (x +e i ,t )−f i (x ,t )(6)Here,subscript i opp denotes the value from the reverse direction of a value with subscript i .Thus f i and f i opp are the opposite distribution functions with reverse velocity vectors e i =−e i opp .For instance,if i =5,then the i opp =7,as shown in Figure 1.The first distribution function in Equation (6),f i opp (x +e i ,t )is the amount of fluid entering this cell in the current time step,and the second one,f i (x ,t )is the amount leaving the cell.The mass exchange for two interface cells has to take into account the area of the fluid interface between the two cells.This is approximated by averaging the fluid fraction values of the two cells.Hence,it is calculated bym i (x ,t + t )=[f i opp (x +e i ,t )−f i (x ,t )] (x +e i ,t )+ (x ,t )2(7)Therefore,for interface cells the mass change values for all directions are added to the current mass,resulting in the mass for the next time step:m (x ,t + t )=m (x ,t )+8i =1m i (x ,t + t )(8)As for filled (fluid)cells,no mass exchange computations are necessary.Their fluid fractions ( )are always equal to one and thus their mass equals their current density.2.2.2.Reconstruction.In this algorithm,the empty cells are never accessed.However,the interface cells always have empty neighbours.Thus during the streaming steps,only the distribution functions from filled or interface cells can be streamed normally.Those coming out of empty cells and entering into interface cells need to be reconstructed since they are unknown.If there is an interface cell at x ,and an empty cell at (x +e i ),the distribution function entering into the interface and out of the empty cell can be expressed asfi opp (x ,t + t )=f eq i ( A ,u )+f eq i opp ( A ,u )−f i (x ,t )(9)where u is the velocity of the cell at position x and time t according to standard LBM notations,while A =1by assuming the gas has a default lattice density without considering the surface tension.This can be understood in the following way:Assuming that the effect of the gas phase on Copyright 2006John Wiley &Sons,Ltd.Int.J.Numer.Meth.Fluids 2007;53:333–351338X.Q.XING,D.L.BUTLER AND C.YANGFigure 2.Reconstruct distribution functions from empty cells.fluid motion can be ignored,as the gas reaches equilibrium state much faster than the fluid does due to low density.At the interface the gas moves the same way as the fluid does,so the distribution functions that will stream from a gas (empty)cell into an interface cell can be determined by the two opposite equilibrium distribution functions of the gas and the motion of the fluid represented by f i (x ,t ).For instance,as shown in Figure 2,the cell at x is an interface cell,marked with ‘I’,with its neighbour empty cells at x +e 2and x +e 5,other neighbours are filled or interface cells.Therefore,the distribution functions f 4(x ,t + t )and f 7(x ,t + t )at position x and time t + t have to be reconstructed according to Equation (9):f 4(x ,t + t )=f eq 4( (x ,t ),u (x ,t ))+f eq 2( (x ,t ),u (x ,t ))−f 2(x ,t )f 7(x ,t + t )=f eq 5( (x ,t ),u (x ,t ))+f eq 7( (x ,t ),u (x ,t ))−f 5(x ,t )(10)where (x ,t )=1.In addition,in order to balance the forces on each side of the interface,the distribution functions coming from the interface normal direction n (i.e.when n ·e i <0)are also reconstructed using Equation (9).The interface normal direction n is calculated with the following central difference approximation:n =8i =1s i e i (x +e i )(11)where s =14[2,2,2,2,1,1,1,1]for D2Q9BGK model.2.2.3.Cell type re-initialization.All distribution functions for the interface cells are valid after reconstruction,and the standard collision can be performed.The density obtained from the collision step is now used to check whether the interface cell is new filled or new emptied during this time step.The criteria are:m (x ,t + t )>(1+ ) (x ,t + t )→new filled cellsm (x ,t + t )<− (x ,t + t )→new emptied cells(12)Copyright 2006John Wiley &Sons,Ltd.Int.J.Numer.Meth.Fluids 2007;53:333–351LB-BASED SINGLE-PHASE METHOD339 Here,an addition offset =10−3is used instead of0or1for the empty orfilling thresholdsto prevent the new surrounding interface cells from being reconverted in the following step.Thepositions of the new emptied orfilled cells are stored in a list instead of immediately convertingtheir types.The conversion will be done only when the main loop over all cells is completed.The re-initialization step mainly takes place to newfilled,new emptied cells and their neighbours.First,for the newfilled cells,all neighbouring empty cells are converted to interface cells.For eachof these cells,the average density avg and velocity u avg of the surroundingfluid and interface cellsare computed and the distribution functions of the empty cells are initialized with the equilibriumdistribution f eq i( avg,u avg).Theflags of the newfilled cells are changed intofluid orfilled. Likewise,for the new emptied cells,the surrounding cells are converted to interface cells,simplytaking the formerfluid cell’s distribution functions for each corresponding new interface cell.Secondly,the excess mass m ex is distributed among the surrounding interface cells through thefollowing formula:m ex=m− for newfilled cellsm ex=m for new emptied cells(13)Negative mass values or mass values larger than the density mean that thefluid interface moves beyond the current cell during the last time step.The excess mass is weighted according to the interface normal direction n,instead of being evenly distributed among the surrounding interface cells.The mass of the surrounding interface cells changes bym(x+e i,t+ t)=m(x+e i,t+ t)+m ex·itotal(14)wherei=n·e iif n·e i>0for newfilled cellsi= −n·eiif n·e i<0for new emptied cells(15)total=8 i=0iOnce the re-initialization is completed,the process can move on to the next time step.2.3.Wall boundary conditionsFor free surface problems,the wall boundaries can be decomposed into the walls in contact with the free surface and the walls which are submerged in the liquidfluid.The fully submerged walls can be treated in a normal way by adhering to the no-slip boundary condition.A simple way to implement no-slip boundary conditions in the LBM is the bounce-back rule.In this method,all distribution functions are reflected at the boundary sites,back along the links they arrived on.Thus at a wall parallel to the direction of e1and e3,as shown in Figure3, Copyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351340X.Q.XING,D.L.BUTLER AND C.YANGFigure3.No-slip boundary conditions(bounce-back rules).the distribution function approaching the boundary on links e4,e7and e8,will be bounced back along the links e2,e5and e6,respectively,resulting in f2=f4,f5=f7and f6=f8.Averaging the velocity at the boundary,before and after the collision step,gives the required no-slip boundary condition u=0.The walls in contact with the free surface should be treated carefully,since the free surface is moving and the bounding wall is static.No introduction for treating this kind of boundaries can be found in the literature.In this paper,we develop a compound boundary condition for the free surface contacting solid wall as follows:Nothing needs to be done when the boundary cells are empty.Reconstruction is needed to complement those distribution functions streaming into the boundary cells from solid wall when the boundary cells are interface cells.This means that the boundary cells are treated as interface cells until they arefilled.Once the boundary cells are filled,the bounce-back rule,i.e.no-slip boundary conditions will be applied.Among the wall boundary cells,as shown in Figure4,those marked with‘F’stand forfilled cells,no-slip boundary conditions as described above are applied to these cells.Those marked with‘E’stand for empty cells,thus nothing is needed to do.While those marked with‘I’stand for interface cells,all those distribution functions streamed from the wall cells into the interface boundary cells should be reconstructed according to Equation(9).2.4.Gravity forceBuick[37]proposed several methods to incorporate external forces like gravity into the LBM. They are:(a)combining the gravity term with the pressure tensor when there is only a negligibleCopyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351LB-BASED SINGLE-PHASE METHOD341Figure4.Boundary layer cells.change in density ,(b)calculating the equilibrium distribution functions with an altered velocity, (c)adding an additional term to the collision function of the Boltzmann equation,and(d)a combination of the second and third methods.The second method is adopted in this paper and is given as below.If a gravitational force F is applied,then at every time step there is a change of momentum which equals to F.To incorporate this into the LBM model,the equilibrium distribution functions are expressed as(x,t)=f eq i( ,u∗)(16)f eqiwhere u∗is given byu∗= u+ F(17) Details of this method can be found elsewhere[37,52].3.APPLICATIONSTo test the LBM-based single-phase method for free surface tracking,simulations are carried out for studying the motion of a free falling droplet with/without gravity and droplet spreading on a wall surface.Surface tension is not considered in this paper,and will be added in the future work. All the test cases are compared with the results obtained by using commercial CFD software Fluent with the VOF model.The VOF model treats all the cases mentioned above as the two-phasefluid systems and simulates the two-phasefluid motions by solving the Navier–Stokes equations and the convection equation for the volume ratio of the liquid phase.For VOF method,the interface tracking is achieved by estimating the normal vector of the interface,constructing the dividing planar surface(interface surface)in every computational grid,and propagation of the interface by theflow.In contrast to the standard VOF method,the single-phase method mainly consists of calculating the mass changes from the distribution function values of the LBM,reconstructing the unknown distribution functions streamed from empty cells into the interface cells,and propagating the interface by theflow(stream and collision steps of LBM).Details of VOF can be found in Reference[53].Copyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351342X.Q.XING,D.L.BUTLER AND C.YANGFigure5.Grid size independent study with droplet falling down without gravity:(a)100×250lattices;(b)200×500lattices;and(c)400×1000lattices.3.1.Falling droplet with/without gravity2D free falling droplet motions are simulated in and without presence of the gravity.For case1, gravity is not considered.The droplet falls with a constant velocity in stationary air.Reynolds number is chosen as Re=8.5.For case2,gravity is added.Initial velocity of the droplet is given as zero.The droplet falls down under the gravity effect.Simulation of droplet falling without gravity are carried out with three difference lattices, 100×250,200×500and400×1000lattice units,respectively,to perform the grid independence study.The results are shown in Figure5.It can be seen that the smaller the lattice size,the smoother the droplet shape,and thus the thinner the interface layer.The initial velocities are set as a constant.It can be seen that the falling velocities are kept very well with three different lattice sizes,which can be observed from the droplet positions at different times(with time intervals of 10−3s)in Figure5.The droplet falling distances increase linearly with time,suggesting a constant falling velocity.However,the computational cost is quite different.The smaller the lattice size,the higher the computational cost.The computational time using400×1000lattices is about16times long as that when using200×500lattices,and about256times long as that when using100×250 lattices.However,the quality of the droplet shape is quite poor with100×250lattices.Therefore, based on the grid independence study,the simulations of droplet falling with/without gravity wereCopyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351Figure 6.Results obtained from the LBM single-phase model:(a)droplet falling down without gravity;and (b)droplet falling down under the effect of gravity.conducted with 200lattices in horizontal direction and 500lattices in vertical direction.With 200×500lattices,the droplet shape is smooth and the computational cost is acceptable.The test cases include a water /air system,i.e.droplet is water and is surrounded by air.For the LBM single-phase model,the kinematic viscosity of water is =10−6m 2/s,grid scale x =√×10−6m and relaxation time =2.0.Since lattice viscosity ∗= ·( t / x 2),and it is related to the relaxation time through ∗=(2 −1)/6,the lattice viscosity ∗=0.5and the time step size t =10−ttice force of gravity is given by g ∗=g ·( t 2/ x )=9.81√×10−6.These parameters are fixed for all test cases including droplet falling and the droplet spreading.For droplet falling cases,no gas flow field boundaries need special treatment since the single-phase model only considers the fluid phase and ignores the gas phase.For the VOF model,the simulations are also implemented with 200grid cells in horizontal direction and 500in vertical direction.All the four boundaries are set as pressure outlet boundaries for both cases 1and 2.Figures 6and 7show the time series of snapshots of a falling droplet obtained by the LBM single-phase model and the Fluent VOF model,respectively.Figures 6(a)and 7(a)display the results of case 1,while Figures 6(b)and 7(b)depict the results of case 2.The time interval corresponds to 1000time steps,namely 10−3s for case 1and 2000time steps,namely 2.0×10−3s for case 2.From Figures 6and 7,it can be seen that the droplet motions obtained by the LBM single-phase model are quite similar to those from the Fluent VOF model,except that the shapes of the droplets differ.The droplet shape obtained from the LBM single-phase model is not very smooth and is deformed,which results from the absence of surface tension and relative coarse lattices e of finer lattices as demonstrated in the grid independence study can definitely give a smoother Copyright 2006John Wiley &Sons,Ltd.Int.J.Numer.Meth.Fluids 2007;53:333–351Figure7.Results obtained from the Fluent VOF model:(a)droplet falling down without gravity;and(b)droplet falling down under the effect of gravity.Table I.Errors and simulation efficiencies.Model Case Velocity error Mass error Computational cost Single-phase model Case13.75×10−161.94×10−15 6.53hours/6000time stepsCase21.76×10−85.48×10−1110.38hours/14000time steps VOF model Case11.0×10−51.0×10−531.5CPU hours/6000time steps Case21.0×10−51.0×10−573.5CPU hours/14000time steps interface shape,and incorporating surface tension wouldfix the deformation problem.Surface tension will be considered in our future studies.In addition,the shape of the droplet obtained from the Fluent VOF model becomesflatter as it approaches to the bottom boundary.It is also due to the fact that surface tension is not included in the simulation.It is known that the presence of surface tension maintains the round shape of the droplet.Validation has been carried out using the Fluent VOF model by the authors.Table I compares the computational errors of the velocity and mass conservation obtained by the single-phase model and the VOF model,as well as the computational cost of the two models. Velocity error is defined asvelocity error=Max ⎡⎢⎢⎣lattice or gridsimulated velocity at different time steps−lattice or gridtheoretical valuelattice or gridtheoretical value⎤⎥⎥⎦(18)Copyright2006John Wiley&Sons,Ltd.Int.J.Numer.Meth.Fluids2007;53:333–351。
热格子Boltzmann法分析及应用陈杰;钱跃竑【摘要】格子Boltzmann方法(lattice Boltzmann method,LBM)是一种基于气体动理论的介观计算方法,其物理背景清晰、边界处理简单,已成功应用于等温(或无热)流动中.简要介绍现有的几种热格子Boltzmann模型,并运用几种热格子模型求解热Couette流、方腔自然对流等典型算例,对比不同热格子模型的数值稳定性、准确性、模型的计算效率等.将两种热格子模型用于多孔介质内的流动与传热问题中,对比热格子模型在处理复杂结构时的数值特性.%Lattice Boltzmann method (LBM) is a mesoscale computational method based on the gas kinetic theory. For solving Fourier-Navier-Stokes equations, the thermal lattice model has attracted much research attention. This paper compares several thermal lattice models in terms of accuracy, stability and computational efficiency. The thermal flow in pore-scale porous is also studied using different thermal lattice models.【期刊名称】《上海大学学报(自然科学版)》【年(卷),期】2012(018)005【总页数】7页(P489-495)【关键词】格子Boltzmann方法;热格子Boltzmann方法;多孔介质【作者】陈杰;钱跃竑【作者单位】上海大学上海市应用数学和力学研究所,上海200072;上海大学上海市应用数学和力学研究所,上海200072【正文语种】中文【中图分类】O351格子Boltzmann方法(lattice Boltzmann method,LBM)是近20年发展成熟起来的一种数值计算方法.LBM基于气体动理论,通过分布函数的演化获得宏观信息.作为一种简单且能处理复杂流动问题的有效数值方法[1-2],LBM具有良好的数值稳定性、天然的并行性、简单的边界处理等优点,自出现之日起就被广泛用于多孔介质流[3]、多相流[4]、反应扩散系统[5]等诸多领域.早期的LBM只应用于等温流动(或无热流动)的模拟,但是基于这种方法具备处理复杂问题的能力以及解决传热问题的需要,研究者一直在不断地探索研究热格子Boltzmann模型,已形成了一些经过数值验证具有模拟热流动能力的热LBM[6-10],并应用于多孔介质流动与传热、燃烧及化学反应流、湍流等问题.本研究简述了不同热格子Boltzmann模型的基本理论,并通过数值分析对比了不同热格子Boltzmann模型的计算结果及数值特性,进而用于多孔介质流动传热问题中.1 等温LBM基本原理LBM中除时间、空间被离散之外,无限维的粒子速度空间也都被离散成有限的速度序列.在标准LBM模型中,物理空间被离散成正方形(体)格子,流体粒子在格点x上碰撞并按离散速度E=[e0,e1,…,eq-1]迁移到x+eiδt格点.fi(x,t)定义为t时刻在格点x上速度为ei的粒子密度,满足如下的格子Boltzmann方程:式中为平衡态函数,ω为松弛因子.通过简单地向平衡态不断趋近的过程代替真实的复杂碰撞,即BGK(Bhatnagar-Gross-Krook)近似,所以此模型也称为LBGK 模型.平衡态分布函数的选取是LBM的关键.DnQm系列[1]中均采用式中,cs为格子声速,Wi为不同速度粒子的权重.本研究在数值模拟中均采用D2Q9模型.宏观密度和速度分别定义为2 热格子Boltzmann模型现有的热格子Boltzmann模型通常可以分为两大类:第一类是流场温度场耦合统一求解的模型,如多速格子Boltzmann模型(multi-speed LBM,MSLBM)、熵格子Boltzmann方法(entropic LBM,ELBM);另一类则是对流场与温度场分别求解,如被动标量格子Boltzmann模型(passive scalar LBM,PSLBM)、双分布函数(double-distribution-function,DDF)模型,以及其他与传统计算流体动力学(computational fluid dynamics,CFD)结合的混合方法,如混合热格子Boltzmann方法(hybrid-thermal LBM,HTLBM).2.1 多速格子Boltzmann模型(MSLBM)多速格子Boltzmann模型是等温LBM模型的直接推广,其密度、速度、内能等均由速度分布函数的各阶速度矩得到.Qian[6]基于等温LBGK模型,提出了D1Q5,D2Q13,D3Q21,D3Q25热力学LBGK模型.在这些模型中,除了要满足等温模型的守恒条件外,还应满足能量守恒和平衡态热通量为0的条件:平衡态分布函数是Maxwell分布的截断形式:式中,Ap,Bp,Dp为待定参数,由满足的守恒条件确定.平衡态包含了速度的三阶项,离散速度也在D2Q9的基础上在主坐标轴上增加了4个速度.Qian[6]采用此模型对一维激波管、二维 Rayleigh-Benard对流进行了模拟,证明了该模型的有效性.MSLBM具有良好的物理基础,宏观方程绝对耦合,已成功模拟了一些传热现象,但只能模拟狭窄的温度范围和较小的Ma数,存在稳定性问题,限制了该模型的广泛应用.2.2 熵格子Boltzmann方法(ELBM)熵格子Boltzmann方法考虑了H定理,通过在守恒约束下最小化波尔兹曼H函数求解平衡态分布函数,由此得出的正定的分布函数保证了模型的稳定性和准确性[11].Prasianakis等[10]将ELBM拓展到热流动问题的求解中,证实了该方法的有效性,本研究参照此方法.H函数定义为平衡态分布函数则是在满足守恒约束条件:的情况下,求H函数最小值得到的,具体形式详见文献[10].Prasianakis等[12]采用在ELBM中加入高阶量的补偿算法,较大地提高了基于D2Q9标准格子的ELBM可模拟的温差和Ma数,但是模型实施较为复杂.2.3 双分布函数模型双分布函数模型,即存在两个分布函数:密度分布函数和内能(温度或总能)分布函数,其中密度分布函数用于模拟速度场,而内能(温度或总能)分布函数则用来模拟温度场.温度、内能或总能分布函数均通过不同的方式构造,但其演化都独立于密度分布函数.2.3.1 被动标量格子Boltzmann模型(PSLBM)被动标量格子Boltzmann模型基于如下原理:在忽略压力做的功和粘性热耗散的情况下,温度可以看作是随流体运动的一个标量,遵循对流扩散方程.由于此方程与组分浓度场的控制方程一样,于是Shan[7]提出使用两组分模型模拟单组分热流动问题:组分1模拟流体的运动;组分2模拟被动的温度场.平衡态密度函数为式中,σ表示组分,两组分共享速度,2.3.2 内能双分布函数模型内能双分布函数模型最早由He等[8]提出,其速度场仍用密度分布函数演化模拟,温度场则由内能分布函数模拟.该模型的基本思想是通过对连续Boltzmann方程进行特殊的离散得到等温LBM,如果进行同样的操作,则热LBM可以由离散内能的演化方程得到.根据内能的定义ρε=∫(ξ-u)2/2f dξ,引入内能分布函数g(r,ξ,t)=(ξ-u)2f/2,并引入新的碰撞模型,得到内能分布函数满足的演化方程:式中,q=(ξ-u)·[∂tu+(ξ·)u].然后对演化方程离散,得到可用于数值计算的离散的分布演化方程,具体的离散过程详见文献[8].相比于PSLBM,内能DDF的构造更具有物理基础,并包含了粘性热耗散和可压缩功.相比于MSLBM,DDF模型具有更好的数值稳定性,Pr数不受限制,因此被广泛用于各种近似不可压流体流动与传热问题.2.4 混合热格子Boltzmann模型(HTLBM)HTLBM是指使用 LBM解速度场,使用传统CFD解温度场,并通过一定的方式相互影响.这种方法利用了LBM能简单处理复杂流动问题的优势以及传统CFD在传热问题上的成熟技术,可以处理一些仅仅使用传统CFD较难解决的复杂流动传热问题.最初,Lallemand等[13]将多速多松弛模型和有限差分法(finite difference method,FDM)相结合,提出了混合模型,速度场用多松弛LBM求解,温度场采用FDM求解.本研究采用有限容积法(finite volume method,FVM)与LBM相结合的混合方法,即采用如下的FVM求解能量守恒方程:式中,S为广义源项,包括压力做的功和粘性热耗散.速度场与温度场的耦合通过在LBM中添加温度相关的外力项以及在FVM中添加广义源项S来实现.此外,普朗特数、比热容等热物性以及随温度变化的输运系数可以实现相应的调节.本研究中FVM与LBM采用同一套网格系统,FVM采用绝对稳定且具有与LBM相同精度的二阶迎风格式(second-order upwind scheme,SUS).PSLBM,DDF以及HTLBM这类模型的一个关键之处在于流场与温度场之间的耦合,其模型往往不满足气体完全状态方程,温度场对速度场的影响只是通过施加一个外力来实现.如Guo等[9]针对Boussinesq方程组,通过在密度分布函数演化方程中增加一个外力项以实现温度对流场的影响.Filippova等[14]基于HTLBM研究了小Ma数下高温燃烧,用温度场修正密度场以满足状态方程.3 计算结果及分析为了进一步对比各类模型,本研究采用ELBM,PSLBM,内能DDF模型以及HTLBM,对热Couette流、封闭方腔自然对流和多孔介质内非等温流动等问题进行了模拟对比.3.1 热Couette流模拟考虑两平板间热Couette流,上平板以速度U向右运动,下板静止,且上下平板分别保持恒温Th,Tc,且Th>Tc.横截面温度廓线的解析形式为式中,H为平板间距离,Pr=ν/χ为普朗特数,χ为热扩散系数,Ec=U2/[Cp(Th -Tc)]为埃克特数.热Couette流中不考虑流体可压缩性的影响,而粘性耗散效应明显,因而分别运用ELBM,内能DDF模型和HTLBM对该问题进行了模拟,网格数均为64×64.模拟中Re=UH/ν=20,计算结果如图1所示.固定Pr=4,Ec分别为1,10和20的无量纲温度廓线,散点为不同方法的计算值,曲线为解析解公式(10).由图可见,三种模型都成功模拟了粘性耗散效应,且与解析解吻合得很好.本工作进一步研究了三种模型的计算效率问题.图2给出了温度残差随CPU时间的变化曲线,可见ELBM和HTLBM明显优于内能DDF模型.3.2 封闭方腔自然对流模拟封闭方腔尺寸为H(正方形边长),左右壁面分别保持恒温Th,Tc,且Th>Tc,上下壁面绝热,四壁面速度均为无滑移边界.方腔内充满均质空气,考虑向下的重力.描述自然对流的无量纲参数Ra数定义为图1 热Couette流温度廓线Fig.1 Temperature variation of the thermal Couette flow图2 热Couette流温度残差变化曲线Fig.2 Temperature residuals variation of the thermal Couette flow式中,β为热膨胀系数.物性满足Boussinesq假设,这里通过施加外力G=-β(T-T0)g实现温度场对速度场的影响.在方腔自然对流中,可压缩效应以及粘性耗散效应可忽略不计.从模型分析可以看出,PSLBM在这种情况下与DDF模型类似,而ELBM边界实施较为复杂.因此,本研究分别采用不包含粘性耗散效应的PSLBM和HTLBM对该问题进行了模拟,模拟中Pr=0.71,Ra数分别为104,105和106.图3和图4分别为HTLBM在不同Ra数下流动稳定后得到的流线、等温线,与以往的数值及实验结果一致.由图3可见,随着Ra数的增大,方腔中心的近似圆形的涡逐渐变成椭圆形,进而分裂成两个涡.当Ra= 106时,两个涡分别向左右壁面移动,在中心出现了第三个涡.由图4可见,随着Ra数的增大,竖直的等温线逐渐变得水平,主导的传热机理由导热变为对流.为了进一步定量考核,本研究计算了努塞尔数Nu和平均努塞尔数 Numean.表1给出了热壁面的Numean、最大Nu数Numax及相应位置的yNumax、水平中心线上最大速度vmax及相应的位置x、垂直中心线上最大速度umax以及相应的位置y.HTLBM和PSLBM求解的结果与Barakos等[15]的基准解一致.同样,本研究对HTLBM和PSLBM的计算效率进行了对比,图5所示为两种方法模拟自然方腔对流Ra=105时,速度残差随CPU时间的变化曲线.可以明显看出,两种方法中残差均呈现震荡下降趋势,且HTLBM收敛快于PSLBM,HTLBM残差收敛到10-7以下时的耗时为PSLBM的57%.图3 方腔自然对流不同Ra数的流线Fig.3 Predicted streamlines of natural convection图4 方腔自然对流不同Ra数的等温线Fig.4 Predicted temperature profiles of natural convection表1 数值解与基准解对比Table 1 Comparison of numerical results between thermal models and benchmarksRa数模型 Numean Numax(y/H) umax(y/H) vmax(x/H) PSLBM 2.247 3.538(0.141) 0.194(0.824) 0.234(0.121) Ra=104 HTLBM 2.242 3.553(0.145) 0.194(0.824) 0.234(0.121) Barakos等[16]2.2453.539(0.143) 0.193(0.818) 0.234(0.119) PSLBM4.512 7.827(0.075)0.128(0.854) 0.256(0.065) Ra=105 HTLBM 4.507 7.723(0.085) 0.134(0.854) 0.260(0.065) Barakos等[16] 4.510 7.636(0.085) 0.132(0.859) 0.258(0.066) PSLBM 8.809 17.454(0.033) 0.079(0.852) 0.261(0.037) Ra=106 HTLBM 8.792 17.435(0.040) 0.081(0.854) 0.263(0.040) Barakos等[16] 8.80617.442(0.037) 0.077(0.859) 0.262(0.039)图5 方腔自然对流速度残差变化曲线Fig.5 Velocity residuals variation of thenatural convection3.3 多孔介质非等温流动模拟多孔介质内部结构十分复杂,其流动传热现象也相当复杂.格子Boltzmann方法在模拟孔隙内的流体运动时可以方便地使用反弹格式处理复杂流场,因此,该方法在孔隙尺度模拟多孔介质内部复杂流动上有明显的优势及较高的计算率.对于多孔介质内流动与传热的问题,以往使用比较广泛的是PSLBM和内能DDF模型.本研究将HTLBM用于多孔介质流动与传热分析中,并与PSLBM进行了对比.本研究分析了分形多孔介质中的自然对流,分形结构采用Sierpinski地毯,依次对分形等级N=2和3的Sierpinski情况进行了模拟.无量纲控制参数Pr=0.71,Ra数分别为104,105和106,固体区域温度保持线性温度分布.图6为采用HTLBM计算N= 2分形结构内自然对流得到的流线图,图7为相应的等温线.由图可见,模拟结果与PSLBM一致,随Ra数的逐步增大,传热机理由导热主导变化为对流主导.图8为N=3,Ra=106时的流线图及等温线.由图可见,固体的增多明显地抑制了对流作用.同样对HTLBM在计算效率的问题上和PSLBM进行了对比.图9为Ra=106时两种方法模拟N=2分形结构时的速度残差曲线,此时HTLBM耗时为PSLBM的76%,仍具有优势.图6 多孔介质方腔自然对流流线(N=2)Fig.6 Predicted streamlines of porous cavity(N=2)图7 多孔介质方腔自然对流等温线(N=2)Fig.7 Predicted temperature profiles of porous cavity(N=2)图8 多孔介质方腔自然对流流线及等温线(N=3)Fig.8 Predicted streamlines and temperature profiles of porous cavity(N=3)4 结论本研究简要介绍了几种热格子Boltzmann模型(MSLBM,ELBM,PSLBM,内能DDF模型及HTLBM),并运用不同热格子模型求解了两个典型算例以及多孔介质流动传热问题,得到如下结论.图9 多孔方腔自然速度残差变化曲线Fig.9 Velocity residuals variation of porous cavity(1)速度场温度场耦合求解的模型还需要进一步发展才能被广泛应用.(2)相比于PSLBM和DDF模型,HTLBM在保证计算精度的前提下,具有较高的计算效率.(3)数值模拟验证了HTLBM在处理多孔介质复杂结构时可行、有效,且比PSLBM 的效率高.参考文献:[1] QIANY H,D’HUMIERESD,ttice BGK models for Navier-Stokes equation [J].Europhysics Letters,1992,17(6):479-484. [2] QIANY H,SUCCIS,ORSZAGS A.Recent advances in lattice Boltzmann computing[M]∥ DIETRICH S.Annual reviews of computational physicsⅢ.New J ersey:World Scientific Publishing Company,1995:195-224.[3] ZHAOC Y,DAIL N,TANGG H,et al.Numerical study of natural convection in porous media(metals) using lattice Boltzmann method (LBM) [J].International Journal of Heat and Fluid Flow,2010,31 (5):925-934. [4]严永华,石自媛,杨帆.液滴撞击液膜喷溅过程的LBM模拟[J].上海大学学报:自然科学版,2008,14(4):399-404.[5]李青,徐旭峰,周美莲.三维斑图形成的格子Boltzmann方法模拟[J].上海大学学报:自然科学版,2007,13(5):516-518.[6] QIANY H.Simulating thermohydrodynamics with lattice BGK models [J].Journal of Scientific Computing,1993,8(3):231-242.[7] SHANX.Simulation of Rayleigh-Bénard convection using a lattice Boltzmann method[J].Physical Review E,1997,55(3):2780-2788. [8] HEX,CHENS,DOOLENG D.A novel thermal model for the latticeBoltzmann method in incompressible limit[J].Journal of Computational Physics,1998,146 (1):282-300.[9] GUOZ,ZHENGC,SHIB,et al.Thermal lattice Boltzmann equationfor low Mach number flows:Decoupling model[J].Physical Review E,2007,75 (3):036704.[10] PRASIANAKISN I,CHIKATAMALAS S,KARLINI V,et al.Entropic lattice Boltzmann method for simulation of thermal flows[J].Mathematics and Computers in Simulation,2006,72(2):179-183. 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多图形处理器上Lattice-Boltzmann方法的加速1. 引言介绍Lattice-Boltzmann方法(LBM)及其在流体动力学领域的应用,并指出目前计算规模的挑战和图形处理器(GPU)的出现提供的解决方案。
2. 相关工作介绍过去对LBM在GPU上的应用研究情况,并总结其存在的问题及局限性。
3. 方法详细阐述LBM在GPU上的加速实现方法,包括算法设计、代码实现以及优化策略等。
4. 实验结果设计一组实验,对比CPU和GPU在运行LBM时的性能表现,并介绍LBM在GPU上加速的效果,如运行速度、计算精度等。
5. 结论对实验结果进行总结和分析,并指出目前研究中存在的问题的和未来的研究方向,以及GPU在其他科学计算领域的应用前景。
第1章节:引言近年来,计算机模拟成为研究流体力学问题的主要方式之一,而Lattice-Boltzmann方法(LBM)作为一种流体动力学模拟方法在数值模拟中得到了广泛的应用。
LBM是一种基于格点上的微观流体学模型,其本质是一种概率统计方法,能够很好地模拟分布式碰撞事件中的流场分布,并且能够消除格点布尔兹曼方程中的宏观耗散项。
然而,由于LBM在计算规模上的复杂性,使得其在优化和加速方面受到巨大挑战。
随着GPU的出现和发展,研究人员开始将GPU引入计算流体力学领域,以加速LBM的计算。
相比于传统的中央处理器(CPU),GPU具有大量的处理器核心和并行计算的优势。
而且,GPU的内存访问速度远远高于CPU,在数据并行处理上有更高的效率。
因此,将LBM方法与GPU相结合,可以更快速地进行流场计算,也可以更高效地利用现代计算硬件。
本论文将研究LBM在GPU加速方面的实现方法、优化策略,以及性能提升等方面进行探讨。
本文将从引言、相关工作、方法、实验结果和结论等五个方面进行介绍。
本文的主要贡献有:1. 提出了一种针对不同场景下的LBM计算优化算法,包括GPU加速和优化内存访问等方法;2. 设计了一组实验,评估了GPU加速LBM方法的效果,证明了GPU在LBM模拟加速方面的可行性;本论文的结构安排为:第一章,简要介绍LBM方法在计算流体力学领域的重要性和现状。
纹理合成法①PDE③本文提出的图像修补算法的评价系统不够完善,缺少计算PSNR所需的标准未受损伤图像,即没有有效客观评价指标[①的参考文献:⑴Chan T F, Kang S H, Shen J H. Euler’s elastica and curvature-based inpa①在设计图像修补各向异性扩散方程的扩散张量时,设计了一些参数g1,g2,g3,式中常量系数的设计直接些参数对于修补质量和计算时间的影响②图像修补算法的并行化实现,提出的修补方法是用Matlab软件串行 实现的,虽然该软件中具有并行计注释①PDE法LBM应用发展实例提出的LB修补方法的局限性,设计了一些参数g1,g2,g3,式中常量系数的设计直接参考了文献(1)见下中的设计,而没有设计实验具体研究这用Matlab软件串行 实现的,虽然该软件中具有并行计算工具箱,但由于操作系统为多个CPU分配数据和任务,导致理图像的时间,因此为了实现并行化,需要直接控制硬件的系统,文献(2)见下中利用GPU实现了LBM方法,在此,特性,实现LBM的并行化缺少计算PSNR所需的标准未受损伤图像,即没有有效的无参考评价方法,从而使得只能采用人为损伤修复,再得到. Euler’s elastica and curvature-based inpainting [J]. SIAM Journal on Applied Mathematics, 2002,分割 2014.3):提出的3D-LB模型从以下两个方面进一步研究的图像和目标区域与背景区域灰度有差别的三维医学图像,但是对于具有更复杂背景的图像,3D-LB模型难以实现一步研究,考虑将图像纹理特征等嵌入其中,以更好地处理复杂背景的图像,为了达到并行处理的快速效果,可通过硬件系统的支持如多核处理器来发挥LB方法易于并行化的特点R, et al. Graphics processing unit implementation of lattice Boltzmann models for flowing soft 6707.based flow simulation using GPU computing processor[J]. Computers & Mathematics with Applications, single processor performance of simple lattice Boltzmann kernels[J]. Computers & Fluids, 2006,A flexible high-performance Lattice Boltzmann GPU code for the simulations of fluid flows in ation: Practice and Experience, 2010, 22(1):1–14.],设计了一些参数g1,g2,g3,式中常量系数的设计直接参考了文献(1)见下中的设计,而没有设计实验具体研究这用Matlab软件串行 实现的,虽然该软件中具有并行计算工具箱,但由于操作系统为多个CPU分配数据和任务,导致理图像的时间,因此为了实现并行化,需要直接控制硬件的系统,文献(2)见下中利用GPU实现了LBM方法,在此,特性,实现LBM的并行化缺少计算PSNR所需的标准未受损伤图像,即没有有效的无参考评价方法,从而使得只能采用人为损伤修复,再得到. Euler’s elastica and curvature-based inpainting [J]. SIAM Journal on Applied Mathematics, 2002, d PDE solver on the GPU [J]. Visual Computer. 2008, 24 (5): 323-333.]例背景的图像(1)见下中的设计,而没有设计实验具体研究这但由于操作系统为多个CPU分配数据和任务,导致。
多相和多组分流动中热质传递的lattice boltzmann方法的
数值研究
近年来,随着先进计算方法和新型计算机技术的发展,多组分流体传热传质问题的计算技术得到了极大的改善。
Lattice Boltzmann
方法是求解多组分流体传热传质的一种新型非经典数值模拟技术,它以一种显式的方式描述了流体的传播行为,是一种有效而简单的工具。
本文采用Lattice Boltzmann方法,研究多相和多组分流动中热质的传递。
首先,本文介绍了Lattice Boltzmann方法的原理,并详细讨论了Lattice Boltzmann方法的优点和特点,以及它与其他常用数值模拟技术(如Finite Difference Method)之间的对比。
其次,本文以多相和多组分流动为研究对象,建立了流体传热传质方程,并给出了Lattice Boltzmann方法求解该方程的数值模拟流程。
同时,研究了温度场的影响和流体组分的相互作用对热传递的影响,以及多组分流体中传质现象的数值模拟。
最后,本文在真实流动情境中进行了仿真,并根据计算结果进行了分析。
结果表明,Lattice Boltzmann方法能够有效地模拟多相和多组分流动中热质传递的过程,是一种理想的数值模拟方法。
本文的研究旨在为多组分流体热传导物理过程的模拟提供参考,为加强热传递的研究和理解提供决策支持。
未来的研究将不断改进Lattice Boltzmann方法,以更高的精度来模拟更复杂的多组分流体传热传质过程。
总之,Lattice Boltzmann方法是一种有效的多相多组分流动中热质传递的数值模拟方法,具有精度高、计算量小、使用方便等优点,可以应用于工程实践中。
轻质热塑性复合片材浸渍过程中的格子Boltzmann方法模拟宗原;孙斌;戴干策【摘要】应用REV尺度的格子Boltzmann方法模拟了树脂沿轻质毡厚度方向的流动过程,考察了加压方式、载荷、温度、双钢带压机结构等对浸渍的影响,为浸渍速度和浸渍时间的预测提供了一种新方法.结果表明:与平板压机相比,双钢带压机通过施加高低交替的压力更能有效地浸渍轻质毡,缩短浸渍时间.另外发现,浸渍不仅与树脂最大浸渍速度有关,还取决于片材在高压区的停留时间.通过增加载荷或升高温度均可提高树脂最大速度,而片材在高压区的停留时间则对压机辊柱直径及载荷更为敏感,仅依靠调节钢带运行速度不能达到缩短浸渍时间的目的.%The lattice Boltzmann method at representative elementary volume (REV) scale was used to simulate transverse impregnation for LWRT (light-weight reinforced thermoplastic), and it offers a new method for the prediction of impregnation velocity and time. The effects of press manner, loading, temperature and the structure of the double-belt press on impregnation were investigated. The results showed that the double-belt press was more effective in shortening impregnation time compared with the flat press owing to its alternating high/low pressure distribution. Besides, it was found that impregnation was determined by not only the maximum impregnation velocity but also the residence time under high pressure. The maximum impregnation velocity could be improved by enhancing loading and increasing temperature as well. The residence time was more sensitive to the diameter of the roller of double-belt press and loading. However, itwas impossible to shorten the impregnation time only by adjusting the processing velocity of the belt.【期刊名称】《化工学报》【年(卷),期】2011(062)012【总页数】9页(P3545-3553)【关键词】格子Boltzmann方法;轻质热塑性复合片材;双钢带压机;浸渍【作者】宗原;孙斌;戴干策【作者单位】华东理工大学化学工程联合国家重点实验室,上海200237;华东理工大学化学工程联合国家重点实验室,上海200237;华东理工大学化学工程联合国家重点实验室,上海200237【正文语种】中文【中图分类】TB332引言轻质热塑性复合片材(LWRT)是一种多孔新型复合材料,由树脂、玻纤和大量孔隙组成,其面密度为300~2000g·m-2,体积密度为0.2~0.8g·cm-3,可以根据需要调节。
应用于非线性热传导方程的格子玻
尔兹曼方法
格子玻尔兹曼(Lattice Boltzmann)方法是一种近似求解非线性热传导方程的数值方法,它将微分方程表示为一系列的离散的布朗运动方程。
该方法利用物理量的随机变化来描述流体在多维空间中的运动,并模拟传统的热力学方法。
格子玻尔兹曼方法首先将空间划分为一系列的网格单元,并将每个网格单元内的传热和流动过程用离散的布朗运动方程来描述。
然后,基于离散布朗运动方程,根据热传导的物理原理,利用粒子的碰撞和扩散,从而得到空间上的温度场。
最后,由于温度场的不断改变,引起的流动也会改变,从而模拟出热传导的实际情况。
因此,格子玻尔兹曼方法通过将非线性热传导方程表示为离散布朗运动方程,并利用粒子的碰撞和扩散来模拟热传导,可以较好地模拟非线性热传导方程的实际情况。