Numerical simulation of interaction during the top blow in a steel-making converter

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朱之丽00814 博士生导师1974 年入西北工业大学航空发动机设计专业,1979年毕业后留西北工业大学703教研室任教,1979年考入北京航空学院航空发动机专业硕士研究生,1981年毕业后留校至今。

一直从事航空发动机总体性能优化与故障诊断方面的研究工作,曾获民航总局科技进步一等奖一项,航空部科技进步三等奖四项。

刘火星06962 硕士生导师1.个人情况简介(1)主要学历:1989年7月:国防科技大学航天技术系固体火箭发动机专业获学士学位1994年7月:北京航空航天大学能源与动力工程学院,获硕士学位1999年12月:北京航空航天大学能源与动力工程学院流体机械及流体动力工程专业获博士学位,研究方向为叶轮机气体动力学,指导教师陈懋章教授2000.1~2001.12 北航流体所博士后流动站(2)主要工作经历1989年8月-1991年7月:包头铁合金厂测量助理工程师2001.12至今北京航空航天大学能源与动力工程学院流体机械系,副教授。

从事教学和叶轮机内复杂流动方向研究2.教学及人才培养情况,科研项目情况(1)主要教学工作2001年至今主讲本科生必修课程《c语言程序设计》2004年至今主讲本科生选修课程《计算方法》2005年至今主讲研究生实验课程《叶轮机特性与流场测量》2005年至今参与讲授本科生专业基础课《气体动力学》2006年至今参与讲授本科生专业课《叶轮机原理》2002年至今指导本科生课程设计2000年至今指导硕士研究生7名(两人毕业,1人转博,4人在读)2002年至今协助指导博士研究生4名(两人毕业,2人在读)(2)主要科研工作作为项目负责人,已完成国家自然科学基金项目、航空基础科学基金项目和国防重点实验室基金项目各一项:a.国家自然科学基金项目:压气机叶片非定常气动负荷机理研究b.航空基础科学基金项目;(项目名称略)c.国防重点实验室基金项目;(项目名称略)作为主要技术骨干,参加了某重大基础研究项目、某计划、攀登计划、国防预研、各类基金及国际合作等十余项课题的研究:a.国家重点基础研究发展规划:高效洁净能源-动力系统及热-功转换过程内部流动研究,项目名称:内流湍流及复杂带激波流动研究b.攀登计划课题(国家基础性研究重大关键项目):能源利用中的气动热力学前沿问题和新设计体系的研究c.国防973项目1,(项目名称略)d.国防973项目2,(项目名称略)e.APTD计划课题, (项目名称略)f.S863项目(项目名称略)g.国防基础科研课题(项目名称略)h.英国R. R.公司-北航合作项目(BUAA and Rolls-Royce Collaborative Project)项目名称:吊架对低压压缩系统影响的实验研究i.法国SNECMA公司-北航合作项目:处理机匣内部流动得实验和数值研究j.国防重点实验室基金项目(四项,项目名称略)k.航空608所横向合作项目3. 发表学术论文情况[1] 压气机叶片前缘分离流动,刘火星,蒋浩康,陈懋章,工程热物理学报[2] 低雷诺数条件下低压涡轮气动设计,工程热物理学报,2005年,Vol.26,No.2: 228-230[3] 涡轮内外涵联立数值模拟,工程热物理学报,2006年,Vol.27,No.1: 39-41,[4] Effect of Leading-Edge Geometry on Separation Bubble on A Compressor Blade,Liu Huoxing, Liu Baojie, Li Ling, Jiang Haokang ,国际会议,ASME 2003-GT-38217[5] Evolution of The Tip Leakage Vortex In An Axial Compressor Rotor,Baojie Liu, Xianjun Yu, Hongwei Wang, Huoxing Liu Haokang Jiang, Maozhang Chen,国际会议,ASME 2004-GT-53703[6] Flow Analysis of a Single-Stage Axial Compressor With a Splitter Rotor,Li Haipeng, Zou Zhengping*, Liu Huoxing,国际会议,ASME 2004-GT-53265[7] 压气机叶片前缘分离流动,刘火星*,蒋浩康,陈懋章,中国工程热物理学会热机气动热力学学术会议论文集,上海,2003年,pp321-328[8] 尾迹对涡轮叶栅边界层分离影响的流动显示,刘火星*,邹正平,刘强,中国工程热物理学会热机气动热力学学术会议论文集,上海,2003年,pp329-333[9] 涡轮叶栅边界层分离流动显示,刘火星*,邹正平,宁方飞,中国航空学会第十二届叶轮机学术讨论会论文集,四川新都,2003年10月[10] 低雷诺数条件下涡轮气动设计,杨琳,刘火星,邹正平*,李维,中国工程热物理学会热机气动热力学学术会议论文集,西安,2004年,pp291-295[11] 涡轮内外涵联立数值模拟,杨琳, 刘火星, 邹正平*, 李维,中国工程热物理学会热机气动热力学学术会议,2004 年10 月,113-118[12] 某大涵道比风扇级内部流动数值模拟,于海海,刘火星*,邹正平,中国工程热物理学会热机气动热力学学术会议论文集,北京,2005年,75-81[13] 大小叶片叶栅的稠度特性分析,李海鹏,刘火星*,中国工程热物理学会热机气动热力学学术会议论文集,北京,2005年,362-369[14] 波转子技术对涡轴发动机性能的影响,刘火星*,姜冬玲,邹正平,中国航空学会第十三届叶轮机学术讨论会论文集,p162-168,宜昌,2003年10月李晓东06301 博士生导师1.主要学术、社会兼职美国航空航天协会高级会员2.获奖情况1)2006 第八届中国航空学会青年科技奖;2)2002 获第八届霍英东基金会青年教师研究类三等奖;3)2002“特种功能蜂窝胶接结构制造技术”获国防科工委科技进步三等奖。

注塑模具工艺立体光照成型毕业论文中英文对照资料外文翻译文献

注塑模具工艺立体光照成型毕业论文中英文对照资料外文翻译文献

注塑模具工艺中英文对照资料外文翻译文献附录2Integrated simulation of the injection molding process withstereolithography moldsAbstract Functional parts are needed for design verification testing, field trials, customer evaluation, and production planning. By eliminating multiple steps, the creation of the injection mold directly by a rapid prototyping (RP) process holds the best promise of reducing the time and cost needed to mold low-volume quantities of parts. The potential of this integration of injection molding with RP has been demonstrated many times. What is missing is the fundamental understanding of how the modifications to the mold material and RP manufacturing process impact both the mold design and the injection molding process. In addition, numerical simulation techniques have now become helpful tools of mold designers and process engineers for traditional injection molding. But all current simulation packages for conventional injection molding are no longer applicable to this new type of injection molds, mainly because the property of the mold material changes greatly. In this paper, an integrated approach to accomplish a numerical simulation of injection molding into rapid-prototyped molds is established and a corresponding simulation system is developed. Comparisons with experimental results are employed for verification, which show that the present scheme is well suited to handle RP fabricated stereolithography (SL) molds.Keywords Injection molding Numerical simulation Rapid prototyping1 IntroductionIn injection molding, the polymer melt at high temperature is injected into the mold under high pressure [1]. Thus, the mold material needs to have thermal and mechanical properties capable of withstanding the temperatures and pressures of the molding cycle. The focus of many studies has been to create theinjection mold directly by a rapid prototyping (RP) process. By eliminating multiple steps, this method of tooling holds the best promise of reducing the time and cost needed to createlow-volume quantities of parts in a production material. The potential of integrating injection molding with RP technologies has been demonstrated many times. The properties of RP molds are very different from those of traditional metal molds. The key differences are the properties of thermal conductivity and elastic modulus (rigidity). For example, the polymers used in RP-fabricated stereolithography (SL) molds have a thermal conductivity that is less than one thousandth that of an aluminum tool. In using RP technologies to create molds, the entire mold design and injection-molding process parameters need to be modified and optimized from traditional methodologies due to the completely different tool material. However, there is still not a fundamental understanding of h ow the modifications to the mold tooling method and material impact both the mold design and the injection molding process parameters. One cannot obtain reasonable results by simply changing a few material properties in current models. Also, using traditional approaches when making actual parts may be generating sub-optimal results. So there is a dire need to study the interaction between the rapid tooling (RT) process and material and injection molding, so as to establish the mold design criteria and techniques for an RT-oriented injection molding process.In addition, computer simulation is an effective approach for predicting the quality of molded parts. Commercially available simulation packages of the traditional injection molding process have now become routine tools of the mold designer and process engineer [2]. Unfortunately, current simulation programs for conventional injection molding are no longer applicable to RP molds, because of the dramatically dissimilar tool material. For instance, in using the existing simulation software with aluminum and SL molds and comparing with experimental results, though the simulation values of part distortion are reasonable for the aluminum mold, results are unacceptable, with the error exceeding 50%. The distortion during injection molding is due to shrinkage and warpage of the plastic part, as well as the mold. For ordinarily molds, the main factor is the shrinkage and warpage of the plastic part, which is modeled accurately in current simulations. But for RP molds, the distortion of the mold has potentially more influence, which have been neglected in current models. For instance, [3] used a simple three-step simulation process to consider the mold distortion, which had too much deviation.In this paper, based on the above analysis, a new simulation system for RP molds is developed. The proposed system focuses on predicting part distortion, which is dominating defect in RP-molded parts. The developed simulation can be applied as an evaluation tool for RP mold design and process opti mization. Our simulation system is verified by an experimental example.Although many materials are available for use in RP technologies, we concentrate on usingstereolithography (SL), the original RP technology, to create polymer molds. The SL process uses photopolymer and laser energy to build a part layer by layer. Using SL takes advantage of both the commercial dominance of SL in the RP industry and the subsequent expertise base that has been developed for creating accurate, high-quality parts. Until recently, SL was primarily used to create physical models for visual inspection and form-fit studies with very limited func-tional applications. However, the newer generation stereolithographic photopolymers have improved dimensional, mechanical and thermal properties making it possible to use them for actual functional molds.2 Integrated simulation of the molding process2.1 MethodologyIn order to simulate the use of an SL mold in the injection molding process, an iterative method is proposed. Different software modules have been developed and used to accomplish this task. The main assumption is that temperature and load boundary conditions cause significant distortions in the SL mold. The simulation steps are as follows:1T he part geometry is modeled as a solid model, which is translated to a file readable by the flow analysis package.2Simulate the mold-filling process of the melt into a pho topolymer mold, which will output the resulting temperature and pressure profiles.3Structural analysis is then performed on the photopolymer mold model using the thermal and load boundary conditions obtained from the previous step, which calculates the distortion that the mold undergo during the injection process.4If the distortion of the mold converges, move to the next step. Otherwise, the distorted mold cavity is then modeled (changes in the dimensions of the cavity after distortion), and returns to the second step to simulate the melt injection into the distorted mold.5The shrinkage and warpage simulation of the injection molded part is then applied, which calculates the final distor tions of the molded part.In above simulation flow, there are three basic simulation mod ules.2. 2 Filling simulation of the melt2.2.1 Mathematical modelingIn order to simulate the use of an SL mold in the injection molding process, an iterativemethod is proposed. Different software modules have been developed and used to accomplish this task. The main assumption is that temperature and load boundary conditions cause significant distortions in the SL mold. The simulation steps are as follows:1. The part geometry is modeled as a solid model, which is translated to a file readable by the flow analysis package.2. Simulate the mold-filling process of the melt into a photopolymer mold, which will output the resulting temperature and pressure profiles.3. Structural analysis is then performed on the photopolymer mold model using the thermal and load boundary conditions obtained from the previous step, which calculates the distortion that the mold undergo during the injection process.4. If the distortion of the mold converges, move to the next step. Otherwise, the distorted mold cavity is then modeled (changes in the dimensions of the cavity after distortion), and returns to the second step to simulate the melt injection into the distorted mold.5. The shrinkage and warpage simulation of the injection molded part is then applied, which calculates the final distortions of the molded part.In above simulation flow, there are three basic simulation modules.2.2 Filling simulation of the melt2.2.1 Mathematical modelingComputer simulation techniques have had success in predicting filling behavior in extremely complicated geometries. However, most of the current numerical implementation is based on a hybrid finite-element/finite-difference solution with the middleplane model. The application process of simulation packages based on this model is illustrated in Fig. 2-1. However, unlike the surface/solid model in mold-design CAD systems, the so-called middle-plane (as shown in Fig. 2-1b) is an imaginary arbitrary planar geometry at the middle of the cavity in the gap-wise direction, which should bring about great inconvenience in applications. For example, surface models are commonly used in current RP systems (generally STL file format), so secondary modeling is unavoidable when using simulation packages because the models in the RP and simulation systems are different. Considering these defects, the surface model of the cavity is introduced as datum planes in the simulation, instead of the middle-plane.According to the previous investigations [4–6], fillinggoverning equations for the flow and temperature field can be written as:where x, y are the planar coordinates in the middle-plane, and z is the gap-wise coordinate; u, v,w are the velocity components in the x, y, z directions; u, v are the average whole-gap thicknesses; and η, ρ,CP (T), K(T) represent viscosity, density, specific heat and thermal conductivity of polymer melt, respectively.Fig.2-1 a–d. Schematic procedure of the simulation with middle-plane model. a The 3-D surface model b The middle-plane model c The meshed middle-plane model d The display of the simulation result In addition, boundary conditions in the gap-wise direction can be defined as:where TW is the constant wall temperature (shown in Fig. 2a).Combining Eqs. 1–4 with Eqs. 5–6, it follows that the distributions of the u, v, T, P at z coordinates should be symmetrical, with the mirror axis being z = 0, and consequently the u, v averaged in half-gap thickness is equal to that averaged in wholegap thickness. Based on this characteristic, we can divide the whole cavity into two equal parts in the gap-wise direction, as described by Part I and Part II in Fig. 2b. At the same time, triangular finite elements are generated in the surface(s) of the cavity (at z = 0 in Fig. 2b), instead of the middle-plane (at z = 0 in Fig. 2a). Accordingly, finite-difference increments in the gapwise direction are employed only in the inside of the surface(s) (wall to middle/center-line), which, in Fig. 2b, means from z = 0 to z = b. This is single-sided instead of two-sided with respect to the middle-plane (i.e. from the middle-line to two walls). In addition, the coordinate system is changed from Fig. 2a to Fig. 2b to alter the finite-element/finite-difference scheme, as shown in Fig. 2b. With the above adjustment, governing equations are still Eqs. 1–4. However, the original boundary conditions inthe gapwise direction are rewritten as:Meanwhile, additional boundary conditions must be employed at z = b in order to keep the flows at the juncture of the two parts at the same section coordinate [7]:where subscripts I, II represent the parameters of Part I and Part II, respectively, and Cm-I and Cm-II indicate the moving free melt-fronts of the surfaces of the divided two parts in the filling stage.It should be noted that, unlike conditions Eqs. 7 and 8, ensuring conditions Eqs. 9 and 10 are upheld in numerical implementations becomes more difficult due to the following reasons:1. The surfaces at the same section have been meshed respectively, which leads to a distinctive pattern of finite elements at the same section. Thus, an interpolation operation should be employed for u, v, T, P during the comparison between the two parts at the juncture.2. Because the two parts have respective flow fields with respect to the nodes at point A and point C (as shown in Fig. 2b) at the same section, it is possible to have either both filled or one filled (and one empty). These two cases should be handled separately, averaging the operation for the former, whereas assigning operation for the latter.3. It follows that a small difference between the melt-fronts is permissible. That allowance can be implemented by time allowance control or preferable location allowance control of the melt-front nodes.4. The boundaries of the flow field expand by each melt-front advancement, so it is necessary to check the condition Eq. 10 after each change in the melt-front.5. In view of above-mentioned analysis, the physical parameters at the nodes of the same section should be compared and adjusted, so the information describing finite elements of the same section should be prepared before simulation, that is, the matching operation among the elements should be preformed.Fig. 2a,b. Illustrative of boundary conditions in the gap-wise direction a of the middle-plane model b of thesurface model2.2.2 Numerical implementationPressure field. In modeling viscosity η, which is a function of shear rate, temperature and pressure of melt, the shear-thinning behavior can be well represented by a cross-type model such as:where n corresponds to the power-law index, and τ∗ characterizes the shear stress level of the transition region between the Newtonian and power-law asymptotic limits. In terms of an Arrhenius-type temperature sensitivity and exponential pressure dependence, η0(T, P) can be represented with reasonable accuracy as follows:Equations 11 and 12 constitute a five-constant (n, τ∗, B, Tb, β) representation for viscosity. The shear rate for viscosity calculation is obtained by:Based on the above, we can infer the following filling pressure equation from the governing Eqs. 1–4:where S is calculated by S = b0/(b−z)2η d z. Applying the Galerkin method, the pressure finite-element equation is deduced as:where l_ traverses all elements, including node N, and where I and j represent the local node number in element l_ corresponding to the node number N and N_ in the whole, respectively. The D(l_) ij is calculated as follows:where A(l_) represents triangular finite elements, and L(l_) i is the pressure trial function in finite elements.Temperature field. To determine the temperature profile across the gap, each triangular finite element at the surface is further divided into NZ layers for the finite-difference grid.The left item of the energy equation (Eq. 4) can be expressed as:where TN, j,t represents the temperature of the j layer of node N at time t.The heat conduction item is calculated by:where l traverses all elements, including node N, and i and j represent the local node number in element l corresponding to the node number N and N_ in the whole, respectively.The heat convection item is calculated by:For viscous heat, it follows that:Substituting Eqs. 17–20 into the energy equation (Eq. 4), the temperature equation becomes:2.3 Structural analysis of the moldThe purpose of structural analysis is to predict the deformation occurring in the photopolymer mold due to the thermal and mechanical loads of the filling process. This model is based on a three-dimensional thermoelastic boundary element method (BEM). The BEM is ideally suited for this application because only the deformation of the mold surfaces is of interest. Moreover, the BEM has an advantage over other techniques in that computing effort is not wasted on calculating deformation within the mold.The stresses resulting from the process loads are well within the elastic range of the mold material. Therefore, the mold deformation model is based on a thermoelastic formulation. The thermal and mechanical properties of the mold are assumed to be isotropic and temperature independent.Although the process is cyclic, time-averaged values of temperature and heat flux are used for calculating the mold deformation. Typically, transient temperature variations within a mold have been restricted to regions local to the cavity surface and the nozzle tip [8]. The transients decay sharply with distance from the cavity surface and generally little variation is observed beyond distances as small as 2.5 mm. This suggests that the contribution from the transients to the deformation at the mold block interface is small, and therefore it is reasonable to neglect the transient effects. The steady state temperature field satisfies Laplace’s equation 2T = 0 and the time-averaged boundary conditions. The boundary conditions on the mold surfaces are described in detail by Tang et al. [9]. As for the mechanical boundary conditions, the cavity surface is subjected to the melt pressure, the surfaces of the mold connected to the worktable are fixed in space, and other external surfaces are assumed to be stress free.The derivation of the thermoelastic boundary integral formulation is well known [10]. It is given by:where uk, pk and T are the displacement, traction and temperature,α, ν represent the thermal expansion coefficient and Poisson’s ratio of the material, and r = |y−x|. clk(x) is the surfacecoefficient which depends on the local geometry at x, the orientation of the coordinate frame and Poisson’s ratio for the domain [11]. The fundamental displacement ˜ulk at a point y in the xk direction, in a three-dimensional infinite isotropic elastic domain, results from a unit load concentrated at a point x acting in the xl direction and is of the form:where δlk is the Kronecker delta function and μ is the shear modulus of the mold material.The fundamental traction ˜plk , measured at the point y on a surface with unit normal n, is:Discretizing the surface of the mold into a total of N elements transforms Eq. 22 to:where Γn refers to the n th surface element on the domain.Substituting the appropriate linear shape functions into Eq. 25, the linear boundary element formulation for the mold deformation model is obtained. The equation is applied at each node on the discretized mold surface, thus giving a system of 3N linear equations, where N is the total number of nodes. Each node has eight associated quantities: three components of displacement, three components of traction, a temperature and a heat flux. The steady state thermal model supplies temperature and flux values as known quantities for each node, and of the remaining six quantities, three must be specified. Moreover, the displacement values specified at a certain number of nodes must eliminate the possibility of a rigid-body motion or rigid-body rotation to ensure a non-singular system of equations. The resulting system of equations is assembled into a integrated matrix, which is solved with an iterative solver.2.4 Shrinkage and warpage simulation of the molded partInternal stresses in injection-molded components are the principal cause of shrinkage and warpage. These residual stresses are mainly frozen-in thermal stresses due to inhomogeneous cooling, when surface layers stiffen sooner than the core region, as in free quenching. Based onthe assumption of the linear thermo-elastic and linear thermo-viscoelastic compressible behavior of the polymeric materials, shrinkage and warpage are obtained implicitly using displacement formulations, and the governing equations can be solved numerically using a finite element method.With the basic assumptions of injection molding [12], the components of stress and strain are given by:The deviatoric components of stress and strain, respectively, are given byUsing a similar approach developed by Lee and Rogers [13] for predicting the residual stresses in the tempering of glass, an integral form of the viscoelastic constitutive relationships is used, and the in-plane stresses can be related to the strains by the following equation:Where G1 is the relaxation shear modulus of the material. The dilatational stresses can be related to the strain as follows:Where K is the relaxation bulk modulus of the material, and the definition of α and Θ is:If α(t) = α0, applying Eq. 27 to Eq. 29 results in:Similarly, applying Eq. 31 to Eq. 28 and eliminating strain εxx(z, t) results in:Employing a Laplace transform to Eq. 32, the auxiliary modulus R(ξ) is given by:Using the above constitutive equation (Eq. 33) and simplified forms of the stresses and strains in the mold, the formulation of the residual stress of the injection molded part during the cooling stage is obtain by:Equation 34 can be solved through the application of trapezoidal quadrature. Due to the rapid initial change in the material time, a quasi-numerical procedure is employed for evaluating the integral item. The auxiliary modulus is evaluated numerically by the trapezoidal rule.For warpage analysis, nodal displacements and curvatures for shell elements are expressed as:where [k] is the element stiffness matrix, [Be] is the derivative operator matrix, {d} is the displacements, and {re} is the element load vector which can be evaluated by:The use of a full three-dimensional FEM analysis can achieve accurate warpage results, however, it is cumbersome when the shape of the part is very complicated. In this paper, a twodimensional FEM method, based on shell theory, was used because most injection-molded parts have a sheet-like geometry in which the thickness is much smaller than the other dimensions of the part. Therefore, the part can be regarded as an assembly of flat elements to predict warpage. Each three-node shell element is a combination of a constant strain triangular element (CST) and a discrete Kirchhoff triangular element (DKT), as shown in Fig. 3. Thus, the warpage can be separated into plane-stretching deformation of the CST and plate-bending deformation of the DKT, and correspondingly, the element stiffness matrix to describe warpage can also be divided into the stretching-stiffness matrix and bending-stiffness matrix.Fig. 3a–c. Deformation decomposition of shell element in the local coordinate system. a In-plane stretchingelement b Plate-bending element c Shell element3 Experimental validationTo assess the usefulness of the proposed model and developed program, verification is important. The distortions obtained from the simulation model are compared to the ones from SL injection molding experiments whose data is presented in the literature [8]. A common injection molded part with the dimensions of 36×36×6 mm is considered in the experiment, as shown in Fig. 4. The thickness dimensions of the thin walls and rib are both 1.5 mm; and polypropylene was used as the injection material. The injection machine was a production level ARGURY Hydronica 320-210-750 with the following process parameters: a melt temperature of 250 ◦C; an ambient temperature of 30 ◦C; an injection pressure of 13.79 MPa; an injection time of 3 s; and a cooling time of 48 s. The SL material used, Dupont SOMOSTM 6110 resin, has the ability to resist temperatures of up to 300 ◦C temperatures. As mentioned above, thermal conductivity of the mold is a major factor that differentiates between an SL and a traditional mold. Poor heat transfer in the mold would produce a non-uniform temperature distribution, thus causing warpage that distorts the completed parts. For an SL mold, a longer cycle time would be expected. The method of using a thin shell SL mold backed with a higher thermal conductivity metal (aluminum) was selected to increase thermal conductivity of the SL mold.Fig. 4. Experimental cavity modelFig. 5. A comparison of the distortion variation in the X direction for different thermal conductivity; where “Experimental”, “present”, “three-step”, and “conventional” mean the results of the experimental, the presented simulation, the three-step simulation process and the conventional injection molding simulation, respectively.Fig. 6. Comparison of the distortion variation in the Y direction for different thermal conductivitiesFig. 7. Comparison of the distortion variation in the Z direction for different thermal conductivitiesFig. 8. Comparison of the twist variation for different thermal conductivities For this part, distortion includes the displacements in three directions and the twist (the difference in angle between two initially parallel edges). The validation results are shown in Fig.5 to Fig. 8. These figures also include the distortion values predicted by conventional injection molding simulation and the three-step model reported in [3].4 ConclusionsIn this paper, an integrated model to accomplish the numerical simulation of injection molding into rapid-prototyped molds is established and a corresponding simulation system is developed. For verification, an experiment is also carried out with an RPfabricated SL mold.It is seen that a conventional simulation using current injection molding software breaks down for a photopolymer mold. It is assumed that this is due to the distortion in the mold caused by the temperature and load conditions of injection. The three-step approach also has much deviation. The developed model gives results closer to experimental.Improvement in thermal conductivity of the photopolymer significantly increases part quality. Since the effect of temperature seems to be more dominant than that of pressure (load), an improvement in the thermal conductivity of the photopolymer can improve the part quality significantly.Rapid Prototyping (RP) is a technology makes it possible to manufacture prototypes quickly and inexpensively, regardless of their comp lexity. Rapid Tooling (RT) is the next step in RP’s steady progress and much work is being done to obtain more accurate tools to define the parameters of the process. Existing simulation tools can not provide the researcher with a useful means of studying relative changes. An integrated model, such as the one presented in this paper, is necessary to obtain accurate predictions of the actual quality of final parts. In the future, we expect to see this work expanded to develop simulations program for injection into RP molds manufactured by other RT processes.References1. Wang KK (1980) System approach to injection molding process. Polym-Plast Technol Eng 14(1):75–93.2. Shelesh-Nezhad K, Siores E (1997) Intelligent system for plastic injection molding process design. J Mater Process Technol 63(1–3):458–462.3. Aluru R, Keefe M, Advani S (2001) Simulation of injection molding into rapid-prototyped molds. Rapid Prototyping J 7(1):42–51.4. Shen SF (1984) Simulation of polymeric flows in the injection molding process. Int J Numer Methods Fluids 4(2):171–184.5. Agassant JF, Alles H, Philipon S, Vincent M (1988) Experimental and theoretical study of the injection molding of thermoplastic materials. Polym Eng Sci 28(7):460–468.6. Chiang HH, Hieber CA, Wang KK (1991) A unified simulation of the filling and post-filling stages in injection molding. Part I: formulation. Polym Eng Sci 31(2):116–124.7. Zhou H, Li D (2001) A numerical simulation of the filling stage in injection molding based on a surface model. Adv Polym Technol 20(2):125–131.8. Himasekhar K, Lottey J, Wang KK (1992) CAE of mold cooling in injection molding using a three-dimensional numerical simulation. J EngInd Trans ASME 114(2):213–221.9. Tang LQ, Pochiraju K, Chassapis C, Manoochehri S (1998) Computeraided optimization approach for the design of injection mold cooling systems. J Mech Des, Trans ASME 120(2):165–174.10. Rizzo FJ, Shippy DJ (1977) An advanced boundary integral equation method for three-dimensional thermoelasticity. Int J Numer Methods Eng 11:1753–1768.11. Hartmann F (1980) Computing the C-matrix in non-smooth boundary points. In: New developments in boundary element methods, CML Publications, Southampton, pp 367–379.12. Chen X, Lama YC, Li DQ (2000) Analysis of thermal residual stress in plastic injection molding. J Mater Process Technol 101(1):275–280.13. Lee EH, Rogers TG (1960) Solution of viscoelastic stress analysis problems using measured creep or relaxation function. J Appl Mech 30(1):127–134.14. Li Y (1997) Studies in direct tooling using stereolithography. Dissertation, University of Delaware, Newark, DE..。

非对称双轨火箭橇动力学建模与仿真研究

非对称双轨火箭橇动力学建模与仿真研究

第20卷第11期装备环境工程2023年11月EQUIPMENT ENVIRONMENTAL ENGINEERING·53·非对称双轨火箭橇动力学建模与仿真研究周雪鹏1,代小强1,杨珍2,陈阳1(1.西南技术工程研究所,重庆400039;2.中国兵器工业试验测试研究院,西安 714200)摘要:目的以高速非对称双轨火箭橇系统为研究对象,建立包含橇-轨相互作用的相关动力学模型,开展全轨范围内的动态特性数值模拟研究。

方法在明确火箭橇的组成和动力学过程基础上,对非对称双轨火箭橇进行受力分析,推导和建立非对称火箭橇-轨道耦合动力学模型,并对火箭橇进行自由模态分析。

分析获得火箭橇气动力时程曲线、考虑火箭发动机质量损失的附加质量时程曲线,同时重构了轨道不平顺模型作为轨道激励。

在此基础上,采用动力学软件分析火箭橇系统的动态特性。

结果及结论产品橇模态高于第二级推力橇,第二级推力橇模态高于火箭橇整体模态。

火箭橇加速度随运行速度的增加而增大,竖向加速度大于横向加速度,火箭橇高速运行过程时的危险部位位于侧边翼上,有折断的风险。

火箭橇竖向滑靴之间存在相位差,火箭橇竖向做俯仰运动,横向为往复摆动,竖向动力响应约为横向的1~2倍。

关键词:非对称;火箭橇;橇轨耦合;动力学建模;数值模拟;动态特性中图分类号:TJ013 文献标识码:A 文章编号:1672-9242(2023)11-0053-10DOI:10.7643/ issn.1672-9242.2023.11.008Dynamic Modeling and Numerical Simulation ofAsymmetric Double Orbit Rocket SledZHOU Xue-peng1, DAI Xiao-qiang1, YANG Zhen2, CHEN Yang1(1. Southwest Institute of Technology and Engineering, Chongqing 400039, China; 2. Norinco Group Test andMeasuring Academy, China North Industries Group Corporation Limited, Xi'an 714200, China)ABSTRACT: The work aims to take the high-speed asymmetric double orbit rocket sled system as the research object to estab-lish a relevant dynamic model including the sledge-rail interaction and carry out numerical simulation on the dynamic character-istics within the whole orbit. Firstly, the composition and dynamic process of the rocket sled were clarified, and the force on the asymmetric double orbit rocket sled was analyzed to deduce and establish the coupling dynamic model of rocket sled and orbit.Secondly, the free mode of the rocket sled was analyzed, showing that the product sled mode was higher than the second stage thrust sled, and the second stage thrust sled mode was higher than the overall mode of the rocket sled. Finally, the aerodynamic time history curve of the rocket sled and the additional mass time history curve considering the mass loss of the rocket engine were obtained and theorbit irregularity model was reconstructedas the orbit excitation. On this basis, the dynamics software was used to analyze the dynamic characteristics of the rocket sled system. The acceleration of rocket sled increased with the increase of running speed and the vertical acceleration was greater than the lateral acceleration. The dangerous part of the rocket sled at high speed was on the side wing, where there was a risk of breaking. There was a phase difference between the vertical slippers of the rocket sled. The rocket sled was pitching vertically and swinging back and forth laterally, and the vertical dynamic re-sponse was about 1~2 times that of the lateral one.收稿日期:2023-03-06;修订日期:2023-07-17Received:2023-03-06;Revised:2023-07-17引文格式:周雪鹏, 代小强, 杨珍, 等. 非对称双轨火箭橇动力学建模与仿真研究[J]. 装备环境工程, 2023, 20(11): 053-062.ZHOU Xue-peng, DAI Xiao-qiang, YANG Zhen, et al. Dynamic Modeling and Numerical Simulation ofAsymmetric Double Orbit Rocket Sled[J]. Equipment Environmental Engineering, 2023, 20(11): 053-062.·54·装备环境工程 2023年11月KEY WORDS: asymmetric; rocket sled; sled-orbit coupling; dynamic modeling; numerical simulation; dynamic characteristics火箭橇试验技术能够将飞行状态进行精确模拟,已广泛应用于飞机、导弹、空中飞行器等,可以解决武器装备在研制过程中有关高速度、高加速度可能带来的许多技术问题。

海底隧道涌水量影响因素的数值模拟研究

海底隧道涌水量影响因素的数值模拟研究

收稿日期:2009-05-20基金项目:国家自然科学基金重点资助项目(50539080);国家重点基础发展规划(973计划)资助项目(2007CB209407);国家自然科学基金设备专项资助项目(50727904)作者简介:冯现大(1985-),男,山东潍坊人,硕士研究生,主要从事隧道、煤矿等突水方面的研究.E -mai l:feng.xianda@gmai 文章编号:1672-3961(2009)04-0021-04海底隧道涌水量影响因素的数值模拟研究冯现大,李树忱,徐帮树(山东大学岩土与结构工程研究中心,山东济南250061)摘要:针对某海底隧道,从数值模拟分析的角度,研究涌水量计算的影响因素.建立大量的海底隧道计算模型,采用不同模型尺寸和边界条件,计算其涌水量从而寻找规律.结果表明:边界范围至少为隧道尺寸的7倍时,才能保证涌水量计算结果的准确性;固定孔隙水压力边界引起涌水量被高估,自由孔隙水压力边界与此相反;侧压力系数对海底隧道涌水量的影响基本可以忽略.关键词:涌水量;海底隧道;数值模拟中图分类号:TU4511 文献标志码:ANumerical simulation study on influence factors ofthe seepage volume of submarine tunnelsFE NG Xian -da,LI Shu -chen,XU Bang -shu(Geotechnical and Structural Engineering Research Cen ter,Shandong University,Jinan 250061,China)Abstract :The influence factors of the seepage volume in submarine tunnels were studied by means of nu merical si mulation.A large number of models with di fferent model scales and boundary conditions were built for the numerical si mulation.The computed results showed that when the distance from the boundary to the tunnel profile was at least 7times the tunnel diameter,an accurate seepage volume could obtained,and the fixed boundary makes the seepage volu me be overestimated,while the free pore pressure boundary was underes timated.Key w ords :seepage volu me;submarine tunnel;numerical si mulation0 引言自1936年第一条海底隧道)))日本关门隧道始建以来,世界各地已有近百条海底隧道相继建造.迄今有海底隧道的国家主要包括日本、英国、法国、美国、挪威、澳大利亚、丹麦、冰岛、中国等[1-4].在特殊的环境地质条件下,建造海底隧道无疑是最安全经济、路线最短的方案,但海底隧道的设计与施工还有很多技术问题亟待解决[5].其中,涌水量的预测计算就是海底隧道防排水设计和施工中一个亟待解决的实际问题,迄今为止尚无成熟的理论和公认的准确计算方法.陆上隧道的涌水量预测研究相关文献较多,而海底隧道涌水量研究较少[6-7].隧道涌水量的预测常用解析法、经验类比法和数值模拟法,解析方法和经验类比法仅可以粗略估算涌水量,因为隧道涌水的复杂性和多变性,实际应用中应以数值模拟结果为准[8],所以数值模拟方法就显得尤为重要.为获得较为准确的涌水量计算值,本文从数值模拟分析的角度,研究海底隧道涌水量的影响因素,分别采用不同模型尺寸、不同边界条件和不同的侧压力系数进行第39卷 第4期Vol.39 No.4山 东 大 学 学 报 (工 学 版)JOUR NA L OF SHAN DO NG UNIVERSITY (EN GIN EERING SCIENCE)2009年8月Aug.2009了大量的数值模拟计算,得出了各个因素对海底隧道涌水量的影响规律.1 FLAC 3D 流固耦合基本原理FLAC3D 模拟多孔介质(如土体)中流体流动时,流体的模拟独立于结构计算.其主要通过孔隙水压力的消散引起岩体中位移的变化.流体在孔隙介质中的流动依据Darcy 定律,流固耦合过程满足Biot 方程[9].(1)运动方程流体的运动用Darcy 定律来描述.对于均质、各向同性固体和流体密度是常数的情况,这个方程具有如下形式:q i =-k [p -Q f x j g j ],i ,(1)式中,q i 是渗透流量,p 是孔隙压力,k 是介质的渗透系数(或机动系数),Q f 是流体密度,g j (j =1,2,3)是重力的三个分量.(2)平衡方程平衡方程的形式为R ij ,j +Q g i =Qd v id t,(2)式中,Q =(1-n )Q s +ns Q w 是体积密度,Q s 和Q w 是固体和流体的密度.(3)本构方程由于流体的流动导致孔隙介质中孔隙压力(p ),饱和度(s ),体积应变(e )和温度(T )的改变.则孔隙流体方程为1M 5p 5t +n s 5s 5t =1s 5F 5t -A 5e 5t +B 5T5t,(3)式中,M 是Biot 模量[N/m 2],n 是孔隙率,A 是Biot 系数,B 是热传导系数[1/e ],用此来考虑流体和颗粒的热膨胀.(4)相容方程应变率和速度梯度之间的关系为N ij =12[u i ,j+v i ,j],(4)式中,v i 是介质中某点的速度.2 工程概况某海底隧道拟采用钻爆法施工,设计为双线四车道.单隧道净轮廓如图1所示.隧址上部以粘性土为主,夹少量砾砂,下部为粘土质碎石.基岩为流纹质熔结凝灰岩,灰紫色、紫灰色,凝灰结构,块状构造,局部具流纹状构造.弱风化层岩芯呈碎石状,少数短块状,裂隙发育,锤击易碎.微风化层岩芯较完整,呈柱状、少数碎块状,弱风化流纹质熔结凝灰岩和微风化流纹质熔结凝灰岩承载力较高;层厚较大,揭露厚度大于8105m;分布稳定,工程地质条件较好.图1 隧道内轮廓Fig.1 The profile of the tunnel隧址处地下水主要以松散岩类孔隙承压水、基岩裂隙水为主.松散岩类孔隙承压水分布范围小,且不连续.未发现断层破碎带的存在,基岩较完整,节理不发育,透水性弱.该处有松散岩类孔隙承压水赋存条件.弱风化层基岩顶面作为透水边界,假定受到海平面静水压力的作用,孔隙水初给充足.3 数值计算方案311 计算模型选取某个典型的断面简化为平面分析.计算时取水平面内垂直隧道轴线方向为x 轴,铅直方向为y 轴.x 方向最大范围为?160m,y 方向下边界最大范围为隧道底板线向下100m,上表面为弱风化层顶面.弹塑性有限差网格如图2,运用FLAC 3D 程序计算涌水量.计算中,考虑软土层渗透性较大水头位置取海平面到软土层底面高度.根据渗流时间的不同进行7次计算,渗流时间依次为1h 、2h 、4h 、8h 、16h 、24h 、48h.图2 初始数值模型图Fig.2 The ini tial numerical model22山 东 大 学 学 报 (工 学 版)第39卷312 物理力学参数根据地质报告、岩石室内力学实验、岩石力学参数手册和相关工程经验,本次主要岩层物理力学参数选取如表1.表1 主要岩层物理力学参数及渗透系数Table 1 Mechanical parameters and permeable coefficientof rock mass地层名称密度Q /(g #cm -3)弹模E /GPa 泊松比L 凝聚力c /KPa 摩擦角</(b )渗透系数k /(c m #s -1)¹2156416201261127305@10-7º215991230122215532185@10-8注:¹表示弱风化流纹质熔结凝灰岩;º表示为微风化流纹质熔结凝灰岩.313 计算方案本文模拟计算边界条件、模型尺寸以及侧压力系数对涌水量的影响.在所有模型中,单元大小相同,以便消除离散化本身的影响.(1)模型尺寸对涌水量的影响为了描述方便,本文用L 表示左右边界距隧道洞壁的距离,用H 表示下边界距隧道底板的距离,如图3所示.采用两种方案研究模型尺寸对涌水量的影响:¹固定H 为100m,当L 分别等于20m 、30m 、40m 、50m 、60m 、70m 、80m 、90m 、100m 、110m 、120m 、130m 、140m 时,共建立了13个模型计算海底隧道涌水量;º固定L 为100m,当H 分别等于20m 、30m 、40m 、50m 、60m 、70m 、80m 、90m 、100m 时,共建立了9个模型计算海底隧道涌水量.图3 模型尺寸示意图Fi g.3 The size of numerical model(2)边界条件对涌水量的影响取L =160m,H =100m,分别计算完全透水和完全不透水两种边界条件下海底隧道的渗流量.(3)侧压力系数对涌水量的影响取L =160m,H =100m,分别计算侧压力系数为016和019时海底隧道的渗流量.4 计算结果411 隧道模型尺寸对涌水量的影响(1)边界完全透水时,图4和图5分别为涌水量随L 和H 的变化曲线.当模型边界完全透水时,随着L (H )的增大,涌水量逐渐减小.用D 表示隧道洞径,文中D U 10m.当L (H )<3D 时,涌水量明显偏大,当L (H )>5D 后,涌水量的变化趋于平稳,当L (H )U 7D 时涌水量稳定不再减小.图4 边界完全透水时L 对渗流量的影响曲线Fig.4 The effect curve of L on the seepage volume with fully per -vi ous boundaries图5 边界完全透水时H 对渗流量的影响曲线Fig.5 The effect curve of L on the seepage volume with fully per -vi ous boundaries(2)边界完全不透水时,图6和图7分别为涌水量随L 和H 的变化曲线.由图6和图7可见,当模型边界完全不透水时,随着L (H )的增大,涌水量逐渐增大.当L (H )<3D 时,涌水量明显偏小,当L (H )>5D 后,涌水量的变化趋于平稳,当L (H )U 7D 时涌水量稳定不再增大.图6 边界完全不透水时L 对渗流量的影响曲线Fig.6 The effect curve of L on the seepage volume with completelyimperviable boundaries412 边界条件对涌水量的影响取L =160m,H =100m,分别计算模型边界完全透水和完全不透水两种边界条件下海底隧道的涌水量,如表2所示.第4期冯现大,等:海底隧道涌水量影响因素的数值模拟研究23图7边界完全不透水时H对渗流量的影响曲线Fig.7The effect curve of H on the seepage volume withcompletely imperviable boundaries表2不同边界条件下海底隧道涌水量Table2The seepage volu me under different boundary conditions(m3#d-1)边界条件Q1h Q2h Q4h Q8h Q16h Q24h 完全透水210801182611675115831152911512完全不透水211011181711608114551132411232注:Q n h(n=1,2,3,)表示渗流时间为n h时隧道涌水量.从表2可见,若模型尺寸相同,两种边界条件下海底隧道涌水量均随着渗流时间逐渐减小.模型边界完全透水时,其渗流量要大于边界完全不透水时的渗流量.413侧压力系数对涌水量的影响取L=160m,H=100m,分别计算侧压力系数为016和019时海底隧道的涌水量,结果表明侧压力系数对海底隧道的涌水量影响很小,可以忽略.5结语通过数值模拟分析,本文研究了不同尺寸、不同边界条件、不同侧压力系数对海底隧道涌水量的影响规律,可以得出以下结论:(1)完全透水边界引起渗流量被高估,完全不透水边界与此相反;(2)两种类型的边界条件包含了问题的解,所以可以进行两次模拟,通过取平均值得到问题真解的合理估计.(3)当L/D>7且H/D>7时,才能保证涌水量计算结果的准确性.(4)侧压力系数对海底隧道的涌水量影响很小.参考文献:[1]ODGARD A,B RIDGE D G,ROS TAM S.Design of the store-belt railway tunnel[J].Tunnelling and Undergroud Space Technology,1994,19(3):293-307.[2]EISE NSTEIN Z rge undersea tunnels and the progress oftunneling technology[J].Tunnelling and Undergroud Space Technology,1994,9(3):283-292.[3]KITAMURA A.Tachnical development for the Sei kan tunnel[J].Tunnelling and Undergroud Space T echnology,1986,1 (3/4):341-349.[4]杨家岭,邱祥波,陈卫忠,等.海峡海底隧道及其最小岩石覆盖厚度问题[J].岩石力学与工程学报,2003,22 (增1):2132-2137.YANG Jialing,QIU Xiangbo,C HE N Weizhong,et al.Subsea tunnel through channel and its mini mu m rock cover[J].Ch-i nese Journal of Rock Mechanics and Engineering,2003,22 (S1):2132-2137.[5]曾超,邱祥波,李术才,等.海底隧道的数值模拟研究[J].岩石力学与工程学报,2003,22(增1):2264-2267.ZE NG Chao,QIU Xiangbo,LI Shucai,et al.Numerical simu-lation on subsea tunnel[J].Chinese Journal of Rock Mechan-ics and Engineering,2003,22(S1):2264-2267.[6]邓英尔,谢和平.低渗透微尺度孔隙气体渗流规律[J].力学与实践,2005,27(2):33-35.DENG Ying.er,XIE Heping.Gas flows in micro-scale pore of low permeability porous media[J].Mechanics in Engineering, 2005,27(2):33-35.[7]王建秀,朱合华,叶为民.隧道涌水量的预测及工程应用[J].岩石力学与工程学报,2004,24(7):1150-1153.WANG Jian x iu,ZHU Hehua,YE Weimin.Forward and inve-rse analyses of water flow into tunnels[J].Chi nese Journal of Rock Mechanics and Engineering,2004,24(7):1150-1153.[8]徐帮树,李树忱,李术才,等.海底隧道涌水量与覆岩厚度关系研究[J].力学与实践,2007,29(1):34-37.XU Bangshu,LI Shuchen,LI Shucai,et al.The relation be-tween seepage volume and rock cover thickness in subsea tunnel [J].Mechanics in Engineering,2007,29(1):34-37.[9]李术才,徐帮树,李树忱,等.舟山海底隧道最小岩石覆盖厚度研究报告[R].济南:山东大学,2007.LI Shucai,XU Bangshu,LI Shuchen,et al.The study report for the minimum rock cover of Zhoushan subsea tunnel[R].J-i nan:Shandong Universi ty,2007.(编辑:孙培芹)24山东大学学报(工学版)第39卷。

基于元胞自动机微观模拟的随机车流与桥梁耦合振动数值研究

基于元胞自动机微观模拟的随机车流与桥梁耦合振动数值研究

文章编号:1000-4750(2021)02-0187-11基于元胞自动机微观模拟的随机车流与桥梁耦合振动数值研究周军勇,苏建旭,齐 飒(广州大学土木工程学院,广东,广州 510006)摘 要:将经典车桥耦合振动理论与最新提出的多轴单元胞自动机(MSCA)微观车流荷载模拟方法进行融合,形成了一种精细化的随机车流与桥梁耦合振动数值分析方法。

介绍了该研究所采用的车桥耦合振动理论及模型;提出了MSCA 实现车桥动力分析的思路和方法,并进行了程序开发;通过具有实测时程动态挠度的工程算例,验证MSCA 实现车桥耦合动力分析的准确性;将MSCA 用于随机车流激励下某斜拉桥的动力效应分析中,论证基于MSCA 的随机车流与桥梁耦合振动分析程序的可靠性。

研究结果表明:工程算例很好地证明了该文所提方法和模型在进行车桥耦合分析的准确性,最大误差仅为11.6%;斜拉桥在随机车流作用下的静力与动力时程挠度分析显示,两者具有很好的一致性,随着路面粗糙度等级提升两者差异更加显著,说明了该模型和方法在开展随机车流与桥梁耦合振动分析的可靠性。

该研究进一步拓展了MSCA 在随机车流激励下分析桥梁各类动态响应的能力,为该方法程序在实桥监测与评估的应用提供了基础。

关键词:桥梁工程;车桥耦合;随机车流模拟;多轴单元胞自动机;数值分析中图分类号:U441+.2 文献标志码:A doi: 10.6052/j.issn.1000-4750.2020.04.0239NUMERICAL INVESTIGATION ON RANDOM TRAFFIC-BRIDGE COUPLED VIBRATION USING CELLULAR AUTOMATON-BASED MICROSCOPIC SIMULATIONZHOU Jun-yong , SU Jian-xu , QI Sa(College of Civil Engineering, Guangzhou University, Guangdong, Guangzhou 510006, China)Abstract: A numerical delicacy method for random traffic-bridge coupled vibration analysis is proposed.Incorporating the classical vehicle-bridge interaction theory, it is a newly established multi-axle single-cell cellular automaton (MSCA)-based microscopic traffic load simulation approach. The utilized equations and models in the classical vehicle-bridge interaction theory are introduced. The concepts and routes of the realization of MSCA for vehicle-bridge coupled dynamic analysis are proposed, and the relevant code program is developed.An engineering example with measured time-history dynamic deflections is utilized to verify the accuracy of the vehicle-bridge interaction analysis by MSCA. MSCA is used to analyze the dynamic load effects of a cable-stayed bridge under the excitation of random traffic loads, to demonstrate the reliability of the proposed approach. The results indicate that MSCA has good accuracy in vehicle-bridge coupling analysis. The maximum error in the engineering example is 11.6%. The static and dynamic time-history deflections of the cable-stayed bridge under random traffic loads show that they have good consistency, and the difference between them becomes more significant along with the increase in the pavement roughness grade. These prove the reliability of the proposed model and method in the random traffic-bridge coupled vibration analysis. This study forwards MSCA's ability to收稿日期:2020-04-19;修改日期:2020-07-29基金项目:国家自然科学基金项目(51808148);广东省自然科学基金项目(2019A1515010701);广州市科技计划项目(201904010188)通讯作者:周军勇(1990−),男,江西人,讲师,博士,主要从事桥梁工程研究(E-mail: ***************.cn ).作者简介:苏建旭(1994−),男,广东人,硕士生,主要从事桥梁工程研究(E-mail: ****************);齐 飒 (1994−),女,河南人,硕士生,主要从事桥梁工程研究(E-mail: ***************).第 38 卷第 2 期Vol.38 No.2工 程 力 学2021年2 月Feb.2021ENGINEERING MECHANICS187analyze various types of dynamic load effects of bridges under the excitation of random traffic flow, which provides more applications of MSCA in monitoring and evaluation of real bridges.Key words: bridge engineering; vehicle-bridge interaction; random traffic simulation; multi-axle single-cell cellular automaton; numerical investigation车桥耦合振动特性是桥梁在移动车辆荷载作用下结构响应行为的重要表征,不仅可以揭示桥梁结构参数、力学行为和损伤特性[1],还能反演移动车辆荷载特性[2],是桥梁工程领域一直以来的研究热点[3 − 4]。

Smoothed Particle Hydrodynamics (SPH)

Smoothed Particle Hydrodynamics (SPH)

is
discretized
by
f x f x 'W ( x x ', h)dx '

f x f x j W ( x x j , h)
Smoothed Particle Hydrodynamics
7
Summary:

The fluid is presented by a set of particles following the fluid motion. Each particle carries mass ,velocity and other properties. SPH formulation is directly based on the macroscopic governing equation of fluid flow .
1, x x ' 0, x x '
lim W x x, h x x
h 0

W x x, hdx 1
when x x kh
W x x, h 0
Smoothed Particle Hydrodynamics
6

Then the computational domain representing it with a set of particles.
Smoothed Particle Hydrodynamics
5

SPH is a integral representation.
f x f x ' x x ' dx '

f x f x 'W ( x x ', h)dx '

后置蜗壳斜流叶轮内部射流-尾迹数值研究

后置蜗壳斜流叶轮内部射流-尾迹数值研究

收稿日期:2005-09-09; 修订日期:2005-12-09基金项目:西北工业大学研究生创新种子基金资助项目(Z200536)作者简介:楚武利(1962-),男,陕西兰田人,西北工业大学教授.文章编号:1001-2060(2006)03-0255-05后置蜗壳斜流叶轮内部射流-尾迹数值研究楚武利,杨 泳,吴艳辉,张 夏(西北工业大学动力与能源学院,陕西西安710072)摘 要:采用商业软件Numeca的Fine/Turbo模块,对包含斜流叶轮、蜗壳一体的斜流风机进行整机计算,并在与已有试验数据进行了较好吻合的基础上,对其内部流场进行详细的数值分析,证实斜流叶轮内部也存在离心式叶轮中古典的射流-尾迹结构。

研究结果表明:由于蜗壳高度非对称性,使各叶轮内部射流-尾迹结构也完全不同。

进一步研究表明,造成这一现象的根本原因在于非对称蜗壳的存在改变了叶轮顶部的叶顶泄漏流动。

关键词:斜流叶轮;蜗壳;尾迹/射流;叶顶泄漏流中图分类号:TH43 文献标识码:A1 前 言众所周知,离心式叶轮和混流式叶轮出口流场常呈现出“射流-尾迹”的流动结构。

这种复杂的非均匀流动结构对叶轮机械的效率和噪声特性有着重要的影响。

早在1960年,R obert等人在实验离心水泵时根据叶轮出口速度提出射流-尾迹结构[1]; Eckardt对一高速径向离心叶轮进行了详细的实验研究和内部流场分析[2~3],指出在离心叶轮曲率及哥氏力的共同作用下,吸力边附面层不断增厚,将主流不断推向压力边,形成“射流-尾迹”结构;K rain 对其自行设计高速后弯离心叶轮进行了详细的试验研究和数值研究[4~6],指出在不同转速下不同工况点下其不同的“射流-尾迹”结构,并得出这种“射流-尾迹”结构在出口附近由于叶顶泄漏流的作用会逐渐减弱;M.D.Hathaway对一低速大尺度离心叶轮进行了详尽的试验和数值研究[8~9],指出在这种低速大尺度叶轮中也存在与高速叶轮类似的“射流-尾迹”结构[2];K.Hillewaert采用对斜流叶轮进行非定常无粘计算耦合蜗壳定常计算的方法对叶轮-蜗壳整体进行计算[10],得出叶轮与蜗壳的相互作用导致蜗壳流场高度不对称性;杨策等人针对K rain叶轮叶进行了一些数值研究[11]。

开放式厨房与闭式厨房燃气泄漏模拟对比研究

开放式厨房与闭式厨房燃气泄漏模拟对比研究

第36卷第3期山东建筑大学学报Vol.36 No.32021 年 6 月JOURNAL OF SHANDONG JIANZHU UNIVERSITYJun. 2021D0I :10.12077/sdjz.2021.03.004开放式厨房与闭式厨房燃气泄漏模拟对比研究张增刚*,商铭恒,陈云丽(山东建筑大学热能工程学院,山东济南250101)摘要:开放式厨房中的燃气泄漏后,研究室内不同时间段的危险程度和燃气扩散规律,可以为相关建筑规范的制定及开放式厨房的推广提供理论依据。

文章基于仿真模拟软件,数值模拟了采用开放式厨房和闭式厨房的同一房屋的燃气泄漏,得到了室内燃气泄漏扩散的一般规律。

结果表明:发生燃气泄漏后,120 min 时闭式厨房内就会达到爆炸下限;开放式厨房之外区域燃气体积分数较高,扩散的区域更大;240 min 内,开放式厨房比闭 式厨房安全,但如果长时间泄漏,开放式厨房发生爆炸的可能性将会更大。

关键词:开放式厨房;闭式厨房;室内燃气泄漏;体积分数分布规律中图分类号:TU996 文献标识码:A 文章编号:1673-7644( 2021) 03-0025-07Comparative study of gas leakage simulation betweenopen kitchen and closed kitchenZHANG Zenggang * , SHANG Mingheng , CHEN Yunli( School of Thermal Engineering, Shandong Jianzhu University, Jinan 250101, China )Abstract : After gas leakage occurs in open kitchens , research on the degree of danger and gasdiffusion laws in the room at different time periods will help provide a theoretical basis for theformulation of regulations and the promotion of modern kitchens. Based on the Fluent simulationsoftware , the paper performs numerical simulation of gas leakage in the same house with open kitchenand closed kitchen respectively, and obtains the general law of indoor gas leakage and diffusion. The results show that when a gas leak occurs, the lower explosive limit will be reached at 120 minutes inthe closed kitchen. The gas concentration inside and outside the open kitchen is higher, and the diffusion area is larger. Within 240 minutes, the open kitchen is safer than the closed kitchen. But ifit leaks for a long time, the possibility of explosion in the open kitchen will be greater.Key words : open kitchen ; closed kitchen ; indoor gas leakage ; concentration distribution law0引言开放式厨房来自西方国家,将厨房和餐厅、起居室合而为一[1]。

水流对浮体作用的SPH方法模拟

水流对浮体作用的SPH方法模拟

水流对浮体作用的SPH方法模拟肖潇;蒋昌波;程永舟【摘要】Flow-induced floating body motions, that movement is very complicatied, can not be simulated exactly at present. SPH as a Lagrangian method without component grid, uses kernel fuction approximate to particle discretely. And it can solve some problems with strong deformation of free surface. In this paper, SPH method is applied to simulate the process of the floating body motion which results from the collapse of a water column and the movement of damaged floating body in sloshing water.Simulation results show that the SPH method can effectively study the flow indued motion of floating body.%水流与浮体相互作用时,运动情况十分复杂,目前很难准确有效地模拟.而SPH作为一种纯拉格朗日方法,无需构建网格,用核函数近似粒子进行离散,能较好地解决一些自由面大变形问题.文章利用SPH法对溃坝时引起的高速水流冲击浮体以及水体晃动时破损浮体的运动过程进行模拟.模拟结果表明,SPH法能有效地进行水流对浮体作用的研究.【期刊名称】《船舶力学》【年(卷),期】2011(015)008【总页数】6页(P861-866)【关键词】浮体;光滑粒子流体动力学;移动最小二乘法;核函数;数值模拟【作者】肖潇;蒋昌波;程永舟【作者单位】长沙理工大学水利工程学院,长沙410004;湖南省水沙科学与水灾害防治重点实验室,长沙 410004;长沙理工大学水利工程学院,长沙410004;湖南省水沙科学与水灾害防治重点实验室,长沙 410004;长沙理工大学水利工程学院,长沙410004;湖南省水沙科学与水灾害防治重点实验室,长沙 410004【正文语种】中文【中图分类】TV13;U661.1光滑粒子流体动力学(Smoothed Particle Hydrodynamics)法[1]是模拟流体流动的一种无网格拉格朗日粒子法,最初被用于解三维开放空间天体物理学问题。

冰区航行船舶碎冰阻力预报数值模拟方法_郭春雨

冰区航行船舶碎冰阻力预报数值模拟方法_郭春雨
计算中,ALE 算法先执行一个或几个 Lagrange 时步计算,此时单元网格随材料流动而产生变形,然 后执行 ALE 时步计算:
1) 保持变形后的物体边界条件,对内部单元进 行 重 分 网 格,网 格 的 拓 扑 关 系 保 持 不 变,称 为 smooth step;
2) 将变形网格中的单元变量( 密度、能量、应力 张量等) 和节点速度矢量输运到重分后的新网格 中,称为 advection step。
近年来,随着全球气候变暖、资源能源紧缺,蕴 藏着巨大潜在价值的北极地区的科考开发工作备受 相关国家重视。由此,破冰船、极地科考船、冰区运 输船等冰区航行船舶的基础性能研究工作也成为当 前的研究热点。目前冰与结构物相互作用的研究领 域内的成果主要集中在冰力学性能[1-2]和冰-固定锥 形结构[3]相互作用的研究领域,船-冰作用的研究进 展有限。而在船-冰作用的研究中,以破冰船和平整 冰碰撞作用[4]的情况为主,对冰区航行船舶在碎冰 区域内的阻力性能预报研究进行的较少,使用的研 究方法通常为理论分析方法或孤立的数值模拟方 法,由于条件限制,船模试验往往很少进行。目前,
A numerical simulation method for resistance prediction of ship in pack ice
GUO Chunyu,LI Xiayan,WANG Shuai,ZHAO Dagang
( College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China)
碎冰区域内的冰区航行船舶阻力性能研究尚处于起 步阶段,研究成果有限,大量问题需要解决。因此, 开展冰区航行船舶在碎冰区域内的阻力性能研究工 作具有重要的价值和意义。

机械毕业设计英文外文翻译493五轴数控铣床翻译

机械毕业设计英文外文翻译493五轴数控铣床翻译

机械毕业设计英⽂外⽂翻译493五轴数控铣床翻译【附】英⽂原⽂翻译⽂献:Five-axis milling machine tool kinematic chain design and analysis作者:E.L.J. Bohez⽂献出处:International Journal of Machine Tools & Manufacture 42 (2002) 505–520 翻译页数:Five-axis milling machine tool kinematic chain design and analysis 1. IntroductionThe main design specifications of a machine tool can be deduced from the following principles:● The kinematics should provide sufficient flexibility inorientation and position of tool and part.● Orientation and positioning with the highest poss iblespeed.● Orientation and positioning with the highest possibleaccuracy.● Fast change of tool and workpiece.● Save for the environment.● Highest possible material removal rate.The number of axes of a machine tool normally refers to the number of degrees of freedom or the number of independent controllable motions on the machine slides.The ISO axes nomenclature recommends the use of a right-handed coordinate system, with the tool axis corresponding to the Z-axis.A three-axis milling machine has three linear slides X, Y and Z which can be positioned everywhere within the travel limit of each slide. The tool axis direction stays fixed during machining. This limits the flexibility of the tool orientation relative to the workpiece and results in a number of different set ups. To increase the flexibility in possible tool workpiece orientations, without need of re-setup, more degrees of freedom must be added. For a conventional three linear axes machine this can be achieved by providing rotational slides. Fig. 1 gives an example of a five-axis milling machine.2. Kinematic chain diagramTo analyze the machine it is very useful to make a kinematic diagram of the machine. From this kinematic (chain) diagram two groups of axes can immediately be distinguished: the workpiece carrying axes and the tool carrying axes. Fig. 2 gives the kinematic diagram of the five-axis machine in Fig. 1. As can be seen the workpiece is carried by four axes and the toolonly by one axis.The five-axis machine is similar to two cooperating robots, one robot carrying the workpiece and one robot carrying the tool.Five degrees of freedom are the minimum required to obtain maximum flexibility in tool workpiece orientation,this means that the tool and workpiece can be oriented relative to each other under any angle. The minimum required number of axes can also be understood from a rigid body kinematics point of view. To orient two rigid bodies in space relative to each other 6 degrees of freedom are needed for each body (tool and workpiece) or 12 degrees. However any common translation and rotation which does not change the relative orientation is permitted reducing the number of degrees by 6. The distance between the bodies is prescribed by the toolpath and allows elimination of an additional degree of freedom, resulting in a minimum requirement of 5 degrees.3.Literature reviewOne of the earliest (1970) and still very useful introductions to five-axis milling was given by Baughman [1]clearly stating the applications. The APT language was then the only tool to program five-axis contouring applications.The problems in postprocessing were also clearly stated by Sim [2] in those earlier days of numerical control and most issues are still valid. Boyd in Ref.[3] was also one of the early introductions. Bez iers’ book[4] is also still a very useful introduction. Held [5] gives a very brief but enlightening definition of multi-axis machining in his book on pocket milling. A recent paper applicable to the problem of five-axis machine workspace computation is the multiple sweeping using the Denawit-Hartenberg representation method developed by Abdel-Malek and Othman [6].Many types and design concepts of machine tools which can be applied to five-axis machines are discussed in Ref. [7] but not specifically for the five-axis machine.he number of setups and the optimal orientation of the part on the machine table is discussed in Ref.[8]. A review about the state of the art and new requirements for tool path generation is given by B.K. Choi et al. [9].Graphic simulation of the interaction of the tool and workpiece is also a very active area of research and a good introduction can be found in Ref. [10].4. Classification of five-axis machines’ kinematic structureStarting from Rotary (R) and Translatory (T) axes four main groups can be distinguished: (i) three T axes and two R axes; (ii) two T axes and three R axes; (iii) one T axis and four R axes and (iv) five R axes. Nearly all existing five-axis machine tools are in group (i). Also a number of welding robots, filament winding machines and laser machining centers fall in this group. Only limited instances of five-axis machine tools in group (ii)exist for the machining of ship propellers. Groups (iii)and (iv) are used in the design of robots usually with more degrees of freedom added.The five axes can be distributed between the workpiece or tool in several combinations. A first classification can be made based on the number of workpiece and tool carrying axes and the sequence of each axis in the kinematic chain.Another classification can be based on where the rotary axes are located, on the workpiece side or tool side. The five degrees of freedom in a Cartesian coordinates based machine are: three translatory movements X,Y,Z (in general represented as TTT) and two rotational movements AB, AC or BC (in general represented as RR).Combinations of three rotary axes (RRR)and two linear axes (TT) are rare. If an axis is bearing the workpiece it is the habit of noting it with an additional accent. The five-axis machine in Fig. 1 can be characterized by XYABZ. The XYAB axes carry the workpiece and the Z-axis carries the tool. Fig. 3 shows a machine of the type XYZAB , the three linear axes carry the tool and the two rotary axes carry the workpiece.5. Workspace of a five-axis machineBefore defining the workspace of the five-axis machine tool, it is appropriate to define the workspace of the tool and the workspace of the workpiece. The workspace of the tool is the space obtained by sweeping the tool reference point (e.g. tool tip) along the path of the tool carrying axes. The workspace of the workpiece carrying axes is defined in the same way (the center of the machine table can be chosen as reference point).These workspaces can be determined by computing the swept volume [6].Based on the above-definitions some quantitative parameters can be defined which are useful for comparison, selection and design of different types of machines.6.Selection criteria of a five-axis machineIt is not the objective to make a complete study on how to select or design a five-axis machine for a certain application. Only the main criteria which can be used to justify the selection of a five-axis machine are discussed.6.1. Applications of five-axis machine toolsThe applications can be classified in positioning and contouring. Figs. 12 and 13 explain the difference between five-axispositioning and five-axis contouring.6.1.1. Five-axis positioningFig. 12 shows a part with a lot of holes and flat planes under different angles, to make this part with a three axis milling machine it is not possible to process the part in one set up. If a five-axis machine is used the tool can process. More details on countouring can be found in Ref. [13]. Applications of five-axis contouring are: (i) production of blades, such as compressor and turbine blades; (ii) injectors of fuel pumps; (iii) profiles of tires; (iv) medical prosthesis such as artificial heart valves; (v) molds made of complex surfaces.6.1.2. Five-axis contouringFig. 13 shows an example of five-axis contouring, tomachine the complex shape of the surface we need to control the orientation of the tool relative to the part during cutting. The tool workpiece orientation changes in each step. The CNC controller needs to control all the five-axes simultaneously during the material removal process. More details on countouring can be found in Ref. [13]. Applications of five-axis contouring are: (i) production of blades, such as compressor and turbine blades; (ii) injectors of fuel pumps; (iii) profiles of tires; (iv) medical prosthesis such as artificial heart valves; (v)molds made of complex surfaces.6.2. Axes configuration selectionThe size and weight of the part is very important as a first criterion to design or select a configuration. Very heavy workpieces require short workpiece kinematic chains. Also there is a preference for horizontal machine tables which makes it more convenient to fix and handle the workpiece. Putting a heavy workpiece on a single rotary axis kinematic chain will increase the orientation flexibility very much. It can be observed from Fig. 4that providing a single horizontal rotary axis to carry the workpiece will make the machine more flexible. In most cases the tool carrying kinematic chains will be kept as short as possible because the toolspindle drive must also be carried.6.3.five-axes machining of jewelryA typical workpiece could be a flower shaped part as in Fig. 14. This application is clearly contouring. The part will be relatively small compared to the tool assembly. Also small diameter tools will require a high speed spindle. A horizontalrotary table would be a very good option as the operator will have a good view of the part (with range 360°). All axes as workpiece carrying axes would be a good choice because the toolspindlecould be fixed and made very rigid. There are 20 ways in which the axes can be combined in the workpiece kinematic chain (Section 4.2.1). Here only two kinematic chains will be considered. Case one will be a T T T R R kinematic chain shown in Fig. 15. Case two will be a R R T T T kinematic chain shown in Fig.16.For model I a machine with a range of X=300mmY=250 mm, Z=200 mm, C=n 360° and A=360°, and a machine tool table of 100 mm diameter will be considered. For this kinematic chain the tool workspace is a single point. The set of tool reference points which can be selected is also small. With the above machine travel ranges the workpiece workspace will be the space swept by the center of the machine table. If the centerline of the two rotary axes intersect in the reference point, a prismatic workpiece workspace will be obtained with as size XYZ or 300×250×200 mm3. If the centerlines of the two rotary axes do not intersect in the workpiece reference point then the workpiece workspace will be larger.It will be a prismatic shape with rounded edges. The radius of this rounded edge is the excentricity of the bworkpiece reference point relative to each centerline. Model II in Fig. 15 has the rotary axes at the beginning of the kinematic chain (R R T T T ). Here also two different values of the rotary axes excentricity will be considered. The same range of the axes as in model I is considered. The parameters defined in Section 5 are computed for each model and excentricity and summarized in Table 1. It can be seen that with the rotary axes at the end of the kinematic chain (model I), a much smaller machine tool workspace is obtained. There are two main reasons for this. The swept volume of the tool and workpiece WSTOOL WSWORK is much smaller for model I. The second reason is due to the fact that a large part of the machine tool workspace cannot be used in the case of model I, because of interference with the linear axes. The workspace utilization factor however is larger for the model I with no excentricity because the union of the tool workspace and workpiece workspace is relatively smaller compared with model I with excentricity e=50 mm. The orientation space index is the same for both cases if the table diameter is kept the same. Model II can handle much larger workpieces for the same range of linear axes as in model I. The rotary axes are here in the beginning of the kinematic chain, resulting in a much larger machine tool workspace then for model I. Also there is much less interference of the machine tool workspace with the slides. The other 18 possible kinematicchain selections will give index values somewhat in between the above cases.6.4. rotary table selectionTwo machines with the same kinematic diagram (T T R R T) and the same range of travel in the linear axes will be compared (Fig. 17). There are two options for the rotary axes: two-axis table with vertical table (model I), two-axis table with horizontal table (model II). Tables 2 and 3 give the comparison of the important features. It can be observed that reducing the range of the rotary axes increases the machine tool workspace. So model I will be more suited for smaller workpieces with operations which require a large orientation range, typically contouring applications. Model II will be suited for larger workpieces with less variation in tool orientation or will require two setups. This extra setup requirement could be of less importance then the larger size. The horizontal table can use pallets which transform the internal setup to external setup. The larger angle range in the B-axes 105 to +105, Fig. 17. Model I and model II T T R R T machines. compared to 45 to +20, makes model I more suited for complex sculptured surfaces, also because the much higher angular speed range of the vertical angular table. The option with the highest spindle speed should be selected and it will permit the use of smaller cutter diameters resulting in less undercut and smaller cutting forces. The high spindle speed will make the cutting of copper electrodes for die sinking EDM machines easier. The vertical table is also better for the chip removal. The large range of angular orientation, however, reduces the maximum size of the workpiece to about 300 mm and 100 kg. Model II with the same linear axes range as model I, but much smaller range in the rotation, can easily handle a workpiece of double size and weight. Model II will be good for positioning applications. Model I cannot be provided with automaticworkpiece exchange, making it less suitable for mass production. Model II has automatic workpiece exchange and is suitable for mass production of position applications. Model I could, however, be selected for positioning applications for parts such as hydraulic valve housings which are small and would require a large angular range.7.New machine concepts based on the Stewart platformConventional machine tool structures are based on Carthesian coordinates. Many surface contouring applications can be machined in optimal conditions only with five-axis machines. This five-axis machine structure requires two additional rotary axes. To make accurate machines, with the required stiffness, able to carry large workpieces, very heavy and large machines are required. As can be seen from the kinematic chain diagram of the classical five-axis machine design the first axis in the chain carries all the subsequent axes. So the dynamic responce will be limited by the combined inertia. A mechanism which can move the workpiece without having to carry the other axes would be the ideal. A new design concept is the use of a‘HEXAPOD’. Stewart [16] described the hexapod principle in 1965. It was first constructed by Gough and Whitehall [20] in 1954 and served as tire tester. Many possible uses were proposed but it was only applied to flight simulator platforms. Thereason was the complexity of the control of the six actuators. Recently with the amazing increase of speed and reduction in cost of computing, the Stewart platform is used by two American Companies in the design of new machine tools. The first machine is the VARIAX machine from the company Giddings and Lewis, USA. The second machine is the HEXAPOD from the Ingersoll company, USA. The systematic design of Hexapods and other similar systems is discussed in Ref. [17]. The problem of defining and determining the workspace of virtual axis machine tools is discussed in Ref. [18]. It can be observed from the design of the machine that once the position of the tool carrying plane is determined uniquely by the CL date (point + vector), it is still possible to rotate the tool carrying platform around the tool axis. This results in a large number of possible length combinations of the telescopic actuators for the same CL data.8.ConclusionTheoretically there are large number of ways in which a five-axis machine can be built. Nearly all classical Cartesian five-axis machines belong to the group with three linear and two rotational axes or three rotational axes and two linear axes. This group can be subdivided in six subgroups each with 720 instances.If only the instances with three linear axes are considered there are still 360instances in each group. The instances are differentiated based on the order of the axes in both tool and workpiece carrying kinematic chain.If only the location of the rotary axes in the tool and workpiece kinematic chain is considered for grouping five-axis machines withthree linear axes and two rotational axes, three groups can be distinguished. In the first group the two rotary axes are implemented in the workpiece kinematic chain. In the second group the two rotary axes are implemented in the tool kinematic chain.In the third group there is one rotary axis in each kinematic chain. Each group still has twenty possible instances.To determine the best instance for a specific application area is a complex issue. To facilitate this some indexes for comparison have been defined such as the machine tool workspace, workspace utilization factor, orientation space index, orientation angle index and machine tool space efficiency. An algorithm to compute the machine tool workspace and the diameter of the largest spherical dome which can be machined on the machine was outlined.The use of these indexes for two examples was discussed in detail. The first example considers the design of a five-axis machine for jewelry machining. The second example illustrates the selection of the rotary axes options in the case of a machine with the same range in linear axes.翻译题名:Five-axis milling machine tool kinematic chain design and analysis期刊与作者:E.L.J. Bohez出版社:International Journal of Machine Tools & Manufacture 42 (2002) 505–520●英⽂译⽂摘要:现如今五轴数控加⼯中⼼已经⾮常普及。

2004_72_Numerical_Simulation_of_Coupled_Heat_and_Mass_Transfer_in_Hygroscopic_Porous_Materials_Consi

2004_72_Numerical_Simulation_of_Coupled_Heat_and_Mass_Transfer_in_Hygroscopic_Porous_Materials_Consi

NUMERICAL SIMULATION OF COUPLED HEAT AND MASS TRANSFER IN HYGROSCOPIC POROUS MATERIALS CONSIDERING THE INFLUENCE OF ATMOSPHERIC PRESSURE Li FengzhiDepartment of Engineering Mechanics,Dalian University of Technology,Dalian,China and Institute of Textiles and Clothing,The Polytechnic University of Hong Kong,Hung Hom,Hong KongLi YiInstitute of Textiles and Clothing,The Polytechnic University of Hong Kong,Hung Hom,Hong KongLiu YingxiDepartment of Engineering Mechanics,Dalian University of Technology,Dalian,ChinaLuo ZhongxuanDepartment of Applied Mathematics,Dalian University of Technology,Dalian,ChinaA model of simultaneous transport in hygroscopic porous materials was developed.Water in fabrics is considered to be present in three forms:liquid water in the void space between fibers,bound water in the fibers,and vapor.It is assumed that the heat and mass transport mechanisms include movement of liquid water due to the capillarity and atmospheric pressure gradient,diffusion of vapor within interfibers due to the partial pressure gradient of vapor and total gas pressure gradient,diffusion of vapor into fiber,evaporation,and condensation of water.The moisture diffusion process into hygroscopic porous materials such as wool fabrics was simulated.At normal atmospheric pressure,the results were compared with experimental data on the temperature and water content changes reported in the literature.The distribution of temperature,moisture concentration,liquid water saturation,and atmospheric pressure in the void space between fibers at different boundary conditions are numerically computed and compared.The conclusion is that atmospheric pressure gradient has significant impact on heat and mass transport processes in hygro-scopic porousmaterials.Received 13March 2003;accepted 16July 2003.We would like to thank The Hong Kong Polytechnic University for funding this research throughprojects T612and A188in the ASD in Fashion,Design and Technology Innovation.Address correspondence to Li Yi,Institute of Textiles and Clothing,The Polytechnic University ofHong Kong,Hung Hom,Hong Kong.E-mail:tcliyi@.hkNumerical Heat Transfer,Part B ,45:249–262,2004Copyright #Taylor &Francis Inc.ISSN:1040-7790print/1521-0626online DOI:10.1080/104077904902688142491.INTRODUCTIONThe clothing system plays a very important role in determining the human body core temperature and other human thermal responses because it determines how much of the heat generated in the human body can be exchanged with the environment.With the development of new technology,it is becoming important to know how the human body will behave thermally under different environmental conditions with various clothing systems.In order to obtain a comfortable micro-climate for human body,it is necessary to understand the thermal and moisture transport behavior of the clothing.The first clothing model that describes the mechanism of transient diffusion of heat and moisture transfer into an assembly of hygroscopic textile materials was introduced and analyzed by Henry [1].He developed a set of two differential coupled governing equations for the mass and heat transfer in a small flat piece of clothingNOMENCLATUREc v volumetric heat capacity of the fabric,J =m 3Kc vf volumetric heat capacity of fiber,J =m 3KC fwater vapor concentration in the fibers of the fabric,kg =m 3div divergenceDdiffusivity of vapor,m 2=sD f ðw c ;t Þdiffusion coefficient of water vapor in the fibers grad gradienth c convection mass transfer coefficient,m =sh l $g mass transfer coefficient,m =sh t convection heat transfer coefficient,J =m 2Kh vap latent heat of evaporation =condensation,J =kgk mix thermal conductivity of the fabric,W =m KK intrinsic permeability,m 2K rg relative permeability of water vapor K rw relative permeability of liquid water L thickness of fabricm a mass flux of dry air under gas pressure gradient driving m v mass flux of vapor under gas pressure gradient drivingm D v diffusion mass flux of water vapor m w diffusion mass flux of liquid water M w mole mass of water vapor,kg =mol p a dry air partial pressure,kg =m s 2p c capillary pressure,kg =m s 2p g pressure of gas phase,kg =m s 2p v water vapor partial pressure,kg =m s 2rradius,mS liquid water volumetric saturation (liquid volume =pore volume)S v specific area of the fabric,l =m T temperature of the fabric,K T 1environmental temperature,KWevaporation or condensation flux of water in interfiber void space of fabric,kg =m 3sw c water content of the fibers in the fabric,kg =kg G boundarye porosity of fabricl heat of sorption or desorption water or vapor by fibers,J =kgm g dynamic viscosity of gas,kg =m s m w dynamic viscosity of water,kg =m s r a density of dry air,kg =m 3r w density of liquid water,kg =m 3r vwater vapor concentration in the air filling the interfiber void space,kg =m 3r vs saturated water vapor concentration,kg =m 3r v ?environmental water vapor concentration,kg =m 3Superscripts n previous time n þ1present time Subscripts 0initial value 1left boundary N right boundary ?environment250L.FENGZHI ET AL.SIMULATION OF COUPLED HEAT AND MASS TRANSFER251 material.The analysis of Henry was based on a simplified analytical solution. Downes and Mackay[2]and Watt[3]found experimentally that the sorption of water vapor by wool is a two-stage process.In order to describe the complicated process of the two-stage adsorption behavior in textile materials,Nordon and David [4]presented a model in terms of experimentally adjustable parameters appropriate for thefirst and second stages of moisture sorption.However,their model did not take into account the physical mechanisms of the process.For this reason,Li and Holcombe[5]introduced a new two-stage absorption model to better describe the coupled heat and moisture transport in fabrics.Li and Luo[6]improved the sorption rate equation by assuming that the moisture sorption by a woolfiber can be generally described as a uniform-diffusion equation for both stages of sorption.Luo et al.[7]presented a dynamic model of heat and moisture transfer with sorption and condensation in porous clothing assemblies.Their model considers the effect of water content in the porousfibrous batting on the effective thermal conductivity as well as radiative heat transfer.However,the above-mentioned models ignore the effect of liquid water movement.Fan and Wen[8]reported a model,in which eva-poration and mobile condensates are considered.Li and Zhu[9]reported a new model that takes into account the condensation=evaporation and liquid diffusion by capillary action,which is a function offiber surface energy,contact angle,and fabric pore size distributions.Furthermore,they investigate the effects of the pore size distribution,fiber diameter[10],thickness and porosity[11]on the coupled heat and moisture transfer in porous textiles.Wang Zhong et al.[12]reported a model con-sidering the radiation and conduction heat transfer coupled with liquid water transfer,moisture sorption,and condensation in porous polymer materials.How-ever,in the above references[1–12],the models were based on the mass diffusion due only to the concentration gradient;the effect of the atmospheric pressure gradient on heat and moisture transfer in porous materials was ignored.For example,when a person runs,the atmospheric pressure between the inside and the outside of clothing is different,which may have significant impact on the thermal comfort and protec-tion of clothing.Although the effects of the atmospheric pressure gradient on mass transfer in porous media were considered by previous researchers[13,16],these models are established for nonhygroscopic materials.In order to investigate the influence of atmospheric pressure gradient on heat and mass transfer within hygroscopic textile materials,and to provide insights into the functional design of clothing for windy conditions and active sportswear outdoors,we report a new model by introducing new equations=terms and integrating them.2.MATHEMATICAL FORMULATIONA schematic diagram of the physical mechanisms within fabric is shown in Figure1.To obtain the governing equation,we have made the following assump-tions.First,the clothing is isotropic,and the gas phase is considered to be an ideal gas composed of dry air and water vapor,which are regarded as two miscible species. Second,local thermal equilibrium exists among all phases.This is reasonable,as the pore dimension in the textile material is small.Third,volume changes of thefibers due to changing moisture content are neglected.Fourth,diffusion within thefiber is considered to be so slow that the moisture content at thefiber surface is always insorptive equilibrium with that of the surrounding air.Fifth,water in fabrics is considered to be present in three forms:free liquid water in the void space of interfibers,bound water in the fibers,and water vapor.The mass transport mechanisms are movement of free liquid water due to the capillarity and atmo-spheric pressure gradient,diffusion of vapor in the space between fibers due to the partial pressure gradient of vapor and total gas pressure gradient,and diffusion of vapor in the fiber due to sorption =desorption.Based on the above assumptions,we can establish the following mathematical equations for the coupled heat and mass transfer in the clothing according to the conservation of masses and heat energy:q E ð1ÀS Þr v ½ ½ q t þq C f 1ÀE ðÞÂÃq t ÀE ð1ÀS ÞS v h l $g r vs ðT ÞÀr v ½ ¼div ðÀm D v Þþdiv ðÀm v Þq ½E S r wq t þE ð1ÀS ÞS v h l $g r vs ðT ÞÀr v ½ ¼div ðÀm w Þq E ð1ÀS Þr a ½ ½ q t ¼div ðm D v Þþdiv ðÀm a ÞC v q T q t Àl ð1ÀE Þq C f q t þh vap E ð1ÀS ÞS v h l $g r vs ðT ÞÀr v ½ ¼div ½k mix ðgrad T Þ :8>>>>>>>>>>>>>>>>><>>>>>>>>>>>>>>>>>:ð1ÞHere,E is porosity;S is saturation of free liquid water;S v is the specific area of thefabric;r v is water vapor concentration;r a is the concentration of dry air;r w is the density of liquid water;C f is the water vapor concentration in the fibers;h l $gisFigure 1.Schematic diagram of the physical model.252L.FENGZHI ET AL.the mass transfer coefficient;r vsðTÞis the concentration of saturated water vapor at T;h vap is the latent heat of evaporation=condensation;l is the sorption heat of thefiber;m v is the massflux of vapor under total atmospheric pressure gradient;m Dv isthe diffusion massflux of water vapor;m a is the massflux of dry air under total atmospheric pressure gradient;m w is the diffusion massflux of liquid water;c v is the effective volume heat capacity;and k mix is the effective thermal conductivity of the fabric.Thefirst equation in(1)represents the mass balance of water vapor.Thefirst term on the left side represents vapor storage within the void space of the interfiber; the second term represents water vapor accumulation rate within thefiber,i.e.,the sorption rate offibers;whereas the third term is evaporation or condensationflux of the water in the interfiber void space.The right side of the equation represents water-vapor diffusion.Thefirst term denotes the effect of the vapor diffusion under vapor partial pressure gradient,whereas the second term represents the effect of the vapor diffusion under total gas pressure gradient driving.Similarity,the second equation represents the mass balance of liquid water,and the third equation represents the mass balance of dry air.The fourth equation in(1)represents the energy balance. Thefirst term on the left side of the fourth equation represents energy storage, whereas the second and third terms represent sorption latent heat of water vapor by fibers and the latent heat of liquid water within interfibers,respectively.The right side represents the heat conduction.Sorption and desorption of moisture by thefibers obey the Fickian Law[6]:q C f q t ¼1rqq rrD fðw c;tÞq C fq r!ð2Þwhere D fðw c;tÞis the diffusion coefficient,which has different presentation at dif-ferent stages of moisture sorption;C f is the moisture concentration in thefiber; w c is the bound water content in thefibers.The boundary condition around thefiber is determined by assuming that the moisture concentration at thefiber surface is instantaneously in equilibrium with the surrounding air.Hence,the moisture concentration at thefiber surface is determined by the relative humidity of the surrounding air and temperature,i.e.,[6]C fðR fÞ¼fðRH;TÞð3Þwhere f is the moisture sorption isotherm of thefibers,which can be determined experimentally for differentfibers.The pore sizes infibrous materials are generally small,so that the diffusion of flow is governed by Darcy’s law[1].In the pores formed betweenfibers,we have the massflux of water vapor and dry air under total atmospheric pressure gradient, respectivelym v¼Àr v KK rgm ggradðp gÞÂÃð4Þm a¼Àr a KK rgm ggradðp gÞÂÃð5ÞSIMULATION OF COUPLED HEAT AND MASS TRANSFER253where p g is the atmospheric pressure,K is intrinsic permeability,K rg is relative permeability of water vapor,and m g is the gas-phase dynamic viscosity.For the free liquid water phase,Darcy’s law is expressed as[13]m w¼Àr w KK rwm wgradðp gÀp cÞð6Þwhere K rw is the relative permeability of liquid water,m w is the dynamic viscosity of the liquid water,and p c is the capillary pressure.For the water-vapor phase,diffusion massflux under partial pressure gradient is expressed as[13]m Dv ¼À1:952Â10À7eð1ÀSÞÂT0:8p agradðp vÞð7Þwhere p a is the partial pressure of dry air and p v is the partial pressure of water vapor.In order to generate a solution to the equations mentioned above,we need to specify an initial condition and boundary conditions on the fabric surfaces in terms of concerntration,saturation,temperature,and atmospheric pressure.Initially,a fabric is equilibrated to a given atmospheric pressure,temperature,saturation,and humidity;the temperature,atmosphere,saturation,and concentration are uniform throughout the clothing at known value.T¼T0r v¼r v0S¼S0p g¼p g0C f¼fðRH0;T0Þð8ÞThe boundary conditions arem DvÁ n j GþmÁ n j G1¼h cðr vÀr v1ÞS jG1¼S B;m wÁ n j g12¼qð9ÞK mix grad TÁ n j G¼h tðTÀT1Þp g j G¼p g Gwhere G denotes the boundary,h c is the mass transfer coefficient,h t is the convective heat transfer coefficient,and the subscript1denotes the environment.3.DISCRETIZATION OF THE CONTINUUM EQUATIONTo derive a numerical solution for Eqs.(1),(8),and(9)by using thefinite-volume method,we select a well-distributed control volume with D x,as shown in Figure2.For example,thefirst equation of(1)can be rewritten as follows:A qr vq tþBq Sq tþCþD¼qq xEqr vq xþqq xFq Tq xþqq xGq p gq xð10ÞwhereA¼eð1ÀSÞB¼Àer v C¼e f q C fq tD¼Àeð1ÀSÞh lg S v r vsðTÞÀr v½254L.FENGZHI ET AL.E ¼ð1:952e À7Þe ð1ÀS ÞT 0:8p a RTM wF ¼ð1:952e À7Þe ð1ÀS ÞT 0:8p a R r vM wG ¼r vKK rg m gIf the control volume P is located in the inner of zone,integration of Eq.(10)over the control volume givesZ Or A qr v q t þB q S q t þC þDdx ¼Z Orq q x E qr v q x þq q x F q T q x þq q x G q p gq x !dxð11ÞThenA n p r n þ1vp Àr n v D t D x þB n p S n þ1vp ÀS n vp D t D x þC n pD x þD np D x ¼E qr v q x eÀE qr v w þF q T e ÀF q T w þG q p g e ÀGq p gwð12ÞNow the right-hand side of Eq.(12)can be written asE qr v q x e ÀE qr v q x w þF q T q x e ÀF q T q x w þG q p g q x e ÀGq p g q xw¼E n e r n þ1v E Àr n þ1v pD xÀE n wr n þ1v p Àr n þ1v WD xþF n eT n þ1EÀT n þ1p D xÀF n wT n þ1p ÀT n þ1W D x þG n e p n þ1gE Àp n þ1gp D x ÀG n wp n þ1gpÀp n þ1gW D xð13ÞWith the new symbols,Eq.(12)becomesK w v r n þ1v W þK wt T n þ1W þK wg p n þ1gW ÀK p v r n þ1v P ÀK ps S n þ1P ÀK pt T n þ1P ÀK pg p n þ1gPþK e v r n þ1v EþK et T n þ1EþK eg p n þ1gE¼RRð14ÞFigure 2.Schematic map of control volume.SIMULATION OF COUPLED HEAT AND MASS TRANSFER 255where K wv¼m E n w,K wt¼m F n w,K wg¼m G n w,K pv¼m E n wþm E n eþA n p,K pt¼m F nw þm F n e,K ps¼B n p,K pg¼m G n wþm G n e,K cv¼m E n e,K et¼m F n e,K eg¼m G n e,RR¼ÀA np r nvpÀB n p S n pþC n p D tþD n p D t.If control volume P is located on the left boundary,we consider that the integration area is half of the inner volume andE qr vq xþFq Tq xþGq p gq xP ¼h cl r nþ1vpÀr vp1ð15ÞThen,we can obtainÀK pv r nþ1vP ÀK ps S nþ1PÀK pt T nþ1PÀK pg p nþ1gPþK ev r nþ1vEþK et T nþ1EþK eg p nþ1gE¼RRð16Þwhere m¼D t=D x2,Z¼D t=D x,K pv¼2m E n eþA n pþ2Z h cl,K pt¼2m F n e, K ps¼B n p,K pg¼2m G n e;K ev¼2m E n e,K et¼2m F n e,K eg¼2m G n e,RR¼ÀA n p r n vpÀB n p S n pþC n pD tþD npD tÀ2Z h el r vp1.Similarly,if control volume P is located on the right boundary:K w v r nþ1n W þK wt T nþ1WþK wg p nþ1gWÀK p v r nþ1n PÀK ps S nþ1PÀK pt T nþ1PÀK pg p nþ1gP¼RRð17Þwhere m¼D t=D x2,Z¼D t=D x,K w v¼2m E n w,K wt¼2m F n w,K wg¼2m G n w, K p v¼2m E n wþA n pþ2Z h cN,K pt¼2m F n w,K ps¼B n p,K pg¼2m G n w;RR¼ÀA n p r n n pÀB n p S npþC n p D tþD n p D tÀ2Z h cN r n p1N,where h cN is the mass transfer coefficient in theright boundary,and r n p1N is the concentration of right environment water vapor.Following the same procedure as for the water-vapor mass conservation,we can derive the discretization equations for liquid water,energy,and dry air.By specifying initial conditions we can calculate the coefficient of the discretization and its right term,the values of water-vapor concentration,liquid water saturation, temperature,and atmospheric pressure at next time,can be obtained.The dis-cretization errors of these schemes are O½ðD xÞ2þðD tÞ2 .And the full implicit scheme is stable without any restriction on D t=D x2.In order to obtain grid independence of the solution,we selected grids of different sizes and did computations.The difference in solution betweenfine and coarse grids with increase of size by2is less than2%.4.NUMERICAL SIMULATION AND DISCUSSIONFor computation,the values of the main parameters are listed as Table1.parison between Theoretical Predictions and Experimental MeasurementsAs reported previously by Li Yi et al.[5],a wool fabric sample measuring 3cm615cm62.96mm was suspended in a cell from an electronic balance,which can measure the weight change of fabric during the sorption process.In the cell the temperature was controlled at293.12Æ2K,and the atmospheric pressure was controlled at1atm.The relative humidity was produced by aflow-divider humidity 256L.FENGZHI ET AL.generator,which can change the relative humidity (RH)from 0%to 99%in 1%steps to an accuracy of Æ0.1%of total flow.Fabric was equlibriated in the cell at 0%RH for 90min,then the RH in the cell was rapidly changed from 0%to 99%at time zero,and this RH was maintained for 90min.The surface temperature of the specimen was measured by using a fine thermocouple wire attached to fabric surface.The mean water content of the fabric was calculated on the basis of the weight recorded by a balance.The detailed information on experimental measurements and uncertainty analysis for experimental data was reported previously [5].Table 1.Parameter values for computation ParameterSymbol Unit Value and referenceDensity of a fiber r f kg =m 31,300[14]Volumetric heat C vf kJ =m 3K 373.3þ4661W c þ4.221T [14]capacity of fabric Thermal conductivity of fabricK fab W =m 2K (38.49370.72w c þ0.113w c 270.002w c 3)61073[14]Head of sorption of water vapor by fibers l kJ =kg 1602.5exp(711.72w c )þ2522.0[14]Diffusion coefficientof water vapor in fiberD f ðw c ;t Þm 2=s(1.04þ62.8w c 71342.59w c 2)610714t <540s [14]1:6164f 1Àexp ½À18:16exp ðÀ28:0w c Þ g t !540s [14]Dynamic viscosity of gasm g kg =m s 1.8361075[15]Dynamic viscosity of liquid waterm w kg =m s 1.061073[15]Intrinsic permeability of fabricKm 21.5610713[16]Figure parison of the temperature between theoretical predictions and experimental measurements.SIMULATION OF COUPLED HEAT AND MASS TRANSFER257The new model described in Section2is applied to this experimental condition by specifying corresponding boundary conditions.The comparisons of temperature and bound water content between theoretical prediction and experimental mea-surements[10]are shown in Figures3and4,respectively.Figure3shows tem-perature changes at the surface of the fabric during the dynamic-moisture diffusion process.Since the temperature of surroundings was kept constant at293.15K,in the testing conditions,no external heatflow was provided to the fabric.Hence the temperature rise in the fabric was due purely to the heat released during the moisture-sorption process.The mean error between theoretical prediction and experimental measurements on temperature is0.13%.Figure4shows the mean moisture uptake of the fabric during humidity transient.The mean error on water content of fabric is4.6%.The diameter of thefiber significantly affects its sorption rate.The difference between measured and actual diameter of thefiber and porosity of fabric may be the main sources of paring the predicted temperature and mean moisture uptake with the experimental results,it is obvious that the models are able to predict the moisture sorption of thefibers with satisfactory accuracy.4.2.Influence of Atmospheric Pressure GradientIn order to investigate the effect of the pressure gradient on heat and mass transfer in porous materials,we carried out a series of computational experiments; two cases are selected to show the trends.In the computations,the fabric sample is assumed to be made of woolfiber with thickness L¼3.0mm,and the initial and boundary conditions were specified as follows.The initial conditions wereT0¼298:15K r v0¼0:01kg=m3S0¼0p g0¼1:0135e5PaAt the left (x ¼0)boundary,T 1¼298:15K r v 1¼0:02kg =m 3m w j G ¼0p g G ¼1:0135e 5PaAnd at the right (x ¼L)boundary,T 1¼298:15K ;r v 1¼0:02kg =m 3S ¼0:4p g G ¼2:0135e 5Pa in Case 11:0135e 5Pa in Case 2(The difference between case 1and case 2is the pressure boundary condition at the location x ¼L .Other conditions are the same.The corresponding numerical simu-lations and comparisons are shown in Figures 5–9.Figure 5a shows the atmospheric pressure variation in the fabric with time under the pressure boundary condition of case 1.We can see that the atmospheric pressure is uniform constant initially,then the atmospheric pressure redistributes with the new boundary pressures.The pressure at x ¼L reaches 2atm.Figure 5bFigure 5.Distribution of atmosphericpressure.Figure 6.Distribution of water-vapor concentration.SIMULATION OF COUPLED HEAT AND MASS TRANSFER 259represents the pressure distribution under the condition of case 2.The pressure is uniform because of the same initial and boundary conditions.Figure 6a shows the distribution of the water-vapor concentration in case 1:the water vapor concentration at x ¼0is higher than that at x ¼L .This is because of the effect of atmospheric pressure.The atmospheric pressure gradient,which is the main driving force of water-vapor movement,makes the water vapor diffuse from high pressure (location x ¼L )to low pressure (location x ¼0).Figure 6b shows dis-tribution of the water-vapor concentration in case 2.The water vapor diffuses due to the driving force of water vapor partial pressure gradient only,when the total pressure gradient does not exist.Because of the water-vapor diffusion,the water-vapor concentration in the fabric increases from 0.01to 0.02kg =m 3with time.Then,the concentration of the water vapor reaches equilibrium.Figure 7shows the distribution of water content in fiber.Since the hygro-scopicity of wool is high,water vapor is absorbed.The different water vapor con-centration affects the relative humidity of the water vapor,and then affects the amount of water absorbed by the fiber.From Figures 7a and 7b we can see that the distributions of water content in the fiber agree with the water-vapor concentration in Figures 6a and 6b ,respectively.Figure 7.Water content distribution offiber.Figure8shows the predicted temperature distribution in the wool fabric during the vapor diffusion process.From Figure8,we can see that the temperature rises in the middle layer of the fabric because of the heat released during moisture sorption. At the beginning,the temperature rises quickly,due to the large sorption rate of fiber.Then the temperature decreases gradually,with time reaching equilibrium with the environmental temperature because of much lower sorption rate and heat exchange with environment.At the beginning,the temperature in case1is lower than that in case2.The reason is that the atmosphere pressure gradient affects the dis-tribution of the water-vapor concentration.The different water-vapor concentration affects the amount of water absorbed by thefiber,which leads to different sorption heat.Most of the water content of thefibers in the fabric,except at the boundary x¼L in case1,is lower than that in case2(see Figure7),so the temperature in case1 is lower than that in case2.From Figures8a and8b we also can see the temperature at location x¼L is larger than that at location x¼0in thefirst stage.This is because there is much more liquid water at location x¼L than that at x¼0,and the conductivity of liquid water is greater than that of fabric.Figure9shows the distribution of liquid water saturation during the process of liquid water diffusion into the wool fabric by capillary action.We can see that the liquid water diffuses from side x¼L to x¼0.The reason is that the diffusion potential of the liquid water is liquid water pressure,which equals the difference between atmospheric pressure and capillary pressure.The capillary pressure in the region of high saturation of liquid water(x¼L)is lower than that in the region of low saturation of liquid water(x¼0).In case1,the atmospheric pressure at x¼L is higher than that at x¼0.And in case2,the atmospheric pressure at x¼0is equal to that at x¼L.So the total liquid water driving potential at location x¼L is higher than that at location x¼0in both case1and case2.From Figures9a and9b we can see that the difference in the distributions of liquid water saturation between case1 and case2is not large in this computational condition.5.CONCLUSIONIn this article,we report a new model of heat and moisture transfer inhygroscopic porous textile materials,considering the influence of the atmospheric。

Numerical Methods for Differentiations and Integrations

Numerical Methods for Differentiations and Integrations

Lecture 2: Numerical Methods for Differentiations and IntegrationsAs we have discussed in Lecture 1 that numerical simulation is a set of carefully planed numerical schemes to solve initial value problems numerically. Let us consider the following three types of differential equations.dy (t )dt=f (t ) (2.1) dy (t )dt=f (y ,t )(2.2) ∂y (x ,t )∂t =f (t ,y ,∂y ∂x ,∂2y∂x2,...,ydx ,...∫)(2.3)The numerical methods for time integration of these equations will be discussed in the next section (Section 4). Before we conduct the time integration, we need to determine thedifferentiations∂y ∂x ,∂2y∂x 2,... and integrations ydx ,...∫ on the left hand side of the equation(2.3) at each grid point.In this section, we are going to discuss the following four types of numerical methods, which are commonly used in spatial differentiations and integrations.1. Finite Differences (based on Taylor’s expansion)2. FFT (Fast Fourier Transform)3. Cubic Spline2.1. Finite DifferencesFor convenience, we shall use the following notation in the rest of this lecture notes.f ijk n =f (x =i Δx ,y =j Δy ,z =k Δz ,t =n Δt )=f (x i ,y j ,z k ,t n )Given a tabulate functioni :12...N x i :x 1x 2...x N f i :f 1f 2...f NUsing finite difference method, we can obtain derivatives of a tabulated function f . Table 2.1 lists examples of the first-order and second-order finite-difference expressions of[d f /dx ]x =x i , [d 2f /dx 2]x =x i , and [d 3f /dx 3]x =x i . Table 2.2 lists examples of the finite∫,difference expressions of y=f dxTable 2.1. The numerical differentiations based on finite difference methodExercise 2.1.(a) Show that the Central Difference shown in Table 2.1 is a second-order scheme(b) Show that the Forward Difference and the backward difference shown in Table 2.1 arefirst-order schemes., and(c) Determine the forth order central difference expressions of [d f/dx]x=x i, based on the Taylor expansions of the function f.[d2f/dx2]x=x iExercise e the first-order, the second-order, and the forth-order finite-differences expressions to determine the d f/dx, and d2f/dx2, an analytical function f with a fixed Δx. Determine the numerical errors in your results. Compare the numerical errors obtained from different finite differences expressions.Figure 2.1. A summary diagram of the central difference, forward difference, and the backward difference schemes.Table 2.2. The spatial integrations based on finite difference methodExercise 2.3.Use the first order, the second order, and the forth order integration expressions listed in Table 2.2 to determine y (x =π/4) with dy (x )/dx =cos(x ) and boundary conditiony (x =0)=0. Determine the numerical errors in your results. Compare the numericalerrors obtained from different integration expressions.2.2. FFT (Fast Fourier Transform)A function can be expanded by a complete set of sine and cosine functions. In the Fast Fourier Transform, the sine and cosine tables are calculated in advance to save the CPU time of the simulation.For a periodic function f , one can use FFT to determine its differentiations and integrations, i.e.,d fdx=FFT −1{ik [FFT (f )]} fdx ∫=FFT −1{1ik [FFT (f )]} for k >0.Exercise 2.4.Use an FFT subroutine to determine the first derivatives of a periodic analytical functionf . Determine the numerical errors in your results.Exercise 2.5.Use an FFT subroutine to determine the first derivatives of a non-periodic analytical function f . Determine the numerical errors in your results.2.3. Cubic SplineA tabulate function can be fitted by a set of piece-wise continuous functions, in which the first and the second derivatives of the fitting functions are continuous at each grid point. One need to solve a tri-diagonal matrix to determine the piece-wise continuous cubic spline functions. The inversion of the tri-diagonal matrix depends only on the position of grid points. Thus, for simulations with fixed grid points, one can evaluate the inversion of the tri-diagonal matrix in advance to save the CPU time of the simulation.For a non-periodic function f, it is good to use the cubic spline method to determine its differentiations and integrations at each grid point.Results of differentiations obtained from the cubic spline show the same order of accuracy as the results obtained from the forth order finite differences scheme.Exercise 2.6.Use a Cubic Spline subroutine to determine the first derivatives of an analytical functionf. Determine the numerical errors in your results.ReferencesHildebrand, F. B., Advanced Calculus for Applications, 2nd edition,Prentice-Hall, Inc., Englewood, Cliffs, New Jersey, 1976.Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes (in C or in FORTRAN and Pascal), Cambridg e University Press, Cambridge, 1988.System/360 Scientific Subroutine Package Version III, Programmer’s Manual,5th edition, IBM, New York, 1970.。

英语试题汇总(答案)

英语试题汇总(答案)

1.()翻译如下短语和论文标题:1)…短语‟热解与着火的动力学行为kinetic behavior of pyrolysis and ignition2)…短语‟城市火灾的危害性预测与风险评估hazard prediction and risk assessment of urban fires3)…短语‟腔室建筑中的机械排烟系统smoke extracting system in the compartment buildings4)…短语‟野火三角中燃料、地形和天气之间的相互作用interactions among the fuel, terrain and weather in a wild fire triangle5)…短语‟坡度和风对火灾发展的影响influence of the slope and wind on the fire growth6)…标题‟细水雾对油池火影响机制的实验与数值研究experimental and simulating study of the suppression mechanism of water mist on pool fire7)…标题‟过去20年中国建筑火灾的统计分析statistical analysis of Chinese building fires in the past 20 years8)…标题‟高层建筑中的人员疏散与火灾安全设计:理论模拟pedestrian evacuation and fire safety design in high buildings: theoretical simulation9)…标题‟火诱导的温度场和速度场的机理分析mechanism analysis of the temperature field and velocity field induced by fire 10)…标题‟火灾信号特征在研制火灾探测器方面的应用application of the characteristics of fire signals in the development of fire detectors 11)…短语‟火灾安全科学与消防工程fire safety science and fire protection engineering12)…短语‟利用多信号报警的火灾探测系统Fire detection system using multi-signals alarm13)…短语‟有焰和无焰燃烧的动力学行为kinetics behavior of flame and flameless combustion14)…短语‟热量释放速率和火蔓延速度Heat release rate and fire spreading velocity15)…短语‟火诱导的温度场和速度场temperature field and velocity field induced by fire16)…标题‟火灾人员疏散的理论与实验研究Theoretical and experimental study of the human evacuation in fires17)…标题‟火灾抑制和扑救技术的发展:回顾与展望development of fire suppression and fighting technology: review and prospect18)…标题‟野火中垂直火旋风的理论分析与数值模拟Theoretical analysis and numerical simulation of the vertical fire whirl in the wild fires 19)…标题‟中国建筑火灾中轰燃和回燃的统计调查Statistical survey of the flashover and backdraft of building fires in China20)…标题‟野火三角中燃料、地形和天气三因子之间相互作用的机理分析Mechanism analysis of the interactions among the fuel, terrain and weather in a wild fire triangle21)燃料类型对自发着火动力学行为的影响:实验研究Influence of fuel type on the spontaneous ignition dynamical behavior: Experimental Study 22)大尺度池火诱导的速度场和温度场理论分析theoretical analysis of velocity field and temperature field induced by Large-scale pool fire23)坡度和风速对地表火蔓延中火焰高度和长度所起的作用:理论分析与测量The role of slope and wind velocity on the flame height and length in the ground fire : theoretical analysis and measurement24)野火中垂直火旋风的热释放速率和燃烧速率heat release rate and burning rate of the vertical fire whirl in the wild fires25)易燃和可燃材料着火温度的特征分析characteristics analysis of the ignition temperature of the flammable and Combustible marterial26)建筑腔室火灾中的轰燃与回燃动力学:非线性数值模拟Dynamic of flashover and backdraft in Building compartment fire:non-linear numerical simulation27)自动火灾探测与报警系统对人员疏散的作用的统计性分析statistical analysis of the role of automatic fire alarm system in personnel evacuation 28)关于炭化和非炭化材料热解化学反应动力学的综述Overview about the decomposition Chemical reaction dynamics of the charring and non-charring material29)火的燃烧与其诱导流动之间的相互作用机理mechanism analysis of the interaction between the fire combustion and induced flow30)分区模拟方法在建筑火灾烟气运动研究中的应用application of zone modeling in the study of the smoke movement of building fires1、The importance of flashover results from the fact that it is of great danger when a compartmentfire occurs. 翻译成中文,并且用表达因果关系的两种不同方式改写。

潜艇流场数值模拟及不确定度分析

潜艇流场数值模拟及不确定度分析

潜艇流场数值模拟及不确定度分析姚震球;杨春蕾;高慧【摘要】为了分析计算流体力学预测结果的可信度问题,采用雷诺平均N-S方程,结合SSTκ-ω湍流模型,对验证研究用的美国DARPA潜艇模型SUBOFF光体湍流场进行数值计算,预报了艇体压力系数,计算流体力学预报值与实验基准数据有较好地吻合,并对雷诺平均N-S方程法进行了验证与确认,不确定度为1.52%.本文相关计算网格数20万左右,单机运行时间大约在2 h后得到收敛值,充分显示了计算流体力学方法在潜艇初步设计中预测水动力性能的高效性、可用性与可信性.【期刊名称】《江苏科技大学学报(自然科学版)》【年(卷),期】2009(023)002【总页数】4页(P95-98)【关键词】计算流体力学;不确定度分析;潜艇模型【作者】姚震球;杨春蕾;高慧【作者单位】江苏科技大学,船舶与海洋工程学院,江苏,镇江,212003;江苏科技大学,船舶与海洋工程学院,江苏,镇江,212003;江苏科技大学,船舶与海洋工程学院,江苏,镇江,212003【正文语种】中文【中图分类】U661.1在潜艇初步设计阶段,设计人员要通过模型实验来评估水动力系数,但是在水池或风洞中进行模型实验,比较昂贵费时.随着计算机技术的迅猛发展,计算流体力学(Computational Fluid Dynamics,CFD)已成为研究潜艇水动力性能的有效方法,它能提供流场更为详细的数据.随着流场计算方法日趋多样和对潜艇流场的数值计算日趋完善[1-2],数值计算在潜艇流场模拟研究中将发挥越来越重要的作用.尽管CFD方法具有成本低、速度快、数据完备且可以模拟各种不同的工况等独特的优点,但CFD 方法的可信度或者其结果的可靠性和对实际问题的可用性,已经成为影响CFD技术进步的关键问题.数值不确定度是对计算结果的正确性或准确性的可疑程度,是合理表征结果或其误差分散程度的一个参数,因此,数值结果的不确定度分析已越来越重要,并且成为当今国际研究的热点.国际组织,如美国航空航天学会(American Institute of Aeronautics and Astronautics,AIAA)等,已做了大量的工作,并且也有了一些初步验证和考核的指标体系.美国机械工程师协会的流体工程刊物对所刊登的有关数值计算的论文提出了误差分析的要求,随后AIAA期刊等刊物都有类似要求[3].文献[4]进行了开创性工作,结合AIAA的CFD规程提出了船舶CFD验证和确认的更加全面、更加可操作的方法[5],这一方法被22届国际船模试验水池会议阻力委员会采纳作为临时规程.文献[6]将其用于潜艇模型SUBOFF流场模拟的验证.文献[7]也对潜艇数值计算进行了CFD不确定度初步分析.目前CFD不确定度分析方法的研究尚处于起步阶段,国际船模试验水池会议临时规程在CFD不确定度分析应用面有限,需要科研人员不断地改进.1 流场计算的数学模型1.1 控制方程不可压缩流体连续性方程与雷诺平均N-S方程(RANS)的张量形式为(1)(2)式中,为时均速度;u′为脉动速度为雷诺应力;ρ为密度;F为质量力;p为压力. 1.2 湍流模型SSTκ-ω模型在船舶CFD的应用中具有良好的稳定性和收敛性,能够精确预报压力梯度流动的对数层,并且对自由来流的湍流度也不敏感,其湍流动能κ,ω方程为(3)(4)式中,Γκ,Γω表示κ,ω的有效扩散率;Gκ表示由于平均速度梯度产生的湍流动能;Gω表示特殊湍流动能消耗率ω的产生;Yκ,Yω表示由于湍流κ,ω的消耗;Sκ,Sω为用户自定义项.1.3 边界条件潜艇光体计算域边界条件 (L为潜艇模型总长):1) 速度入口潜艇艏部向上游延伸1L,设定来流速度的大小和方向,Vin=V0;2) 压力出口潜艇艉部向下游延伸2L,设定相对于参考压力点的流体静压值;3) 壁面潜艇外表面,设定无滑移条件,u=v=w=0;4) 外场距潜艇表面1L,速度为未受扰动的主流区速度.1.4 数值计算方法采用有限体积法离散动量方程,对流项采用二阶迎风差分格式,扩散项采用中心差分格式,压力速度耦合采用SIMPLE算法,利用代数多重网格法加速收敛.2 不确定度分析评估方法CFD的不确定度分析方法用ITTC临时规程,相关参数的定义见文献[8],分析过程可分为验证和确认.评估数值不确定度的过程叫验证.验证就是计算数值模拟的数值不确定度UV的过程,当条件允许时,还要估计模拟的数值误差及此误差估计中的不确定度.迭代和参数的收敛性研究通常使用参数系列加细的多重解来估计数值误差和不确定度. 定义收敛因子为Rk=ε21/ε32(5)式中,ε21为“中”-“细”解之差,ε32为“粗”-“中”解之差.由收敛因子可以判断可能出现的收敛状况有3种:① 单调收敛0<Rk<l;② 波动收敛Rk<0;③ 发散Rk>l.对于单调收敛,使用Richardson外推法估计,对于仅能估算首项的三重解,可以提供误差和准确度阶数的一项估计值为(6)(7)式中,为误差;Pk为准确度阶数,对误差的修正因子为(8)式中,Pkest是当空间步长趋于0,渐近线范围Ck→1时首项准确度极限阶数的估计值,取2,统一参数加细比Ck<1时,不确定度的表达式可由误差估计式得到(9)对于波动收敛,不确定度可以简单估计为波动最大值SU和最小值SL限定的误差.由于波动收敛有可能被错误地看作单调收敛或发散,这时需要多于三重解.对于发散状况,误差和不确定度都不能估算.确认则是利用基准实验数据评估数值模拟的模型不确定度USM的过程,当条件允许时,还要估计模型误差δSM,比较误差E由实验数据D和模拟结果S之差给出.3 艇体压力的数值计算及不确定度分析3.1 潜艇压力计算采用Fluent软件,对SUBOFF光体流场进行数值计算.计算用轴对称模型,SUBOFF长度为L=4.3561m,模型直径为d=0.508 m,来流速度V=2.77m/s,雷诺数Re=1.2×107.为了验证RANS方程数值不确定度,采用GAMBIT软件,生成3种不同密度的网格,网格细化比例为种网格形式在轴向、周向、径向为135×55×65,95×40×55,68×30×40,沿壁面压力系数Cp=(p-p∞)式中,p为静压力,p∞为无穷远处压力,ρ为流体密度,U∞为无穷远处流体速度),3种网格迭代都为单调收敛,压力系数的网格收敛情况如图1所示,图中包括了实验数据,并对阻塞效应进行了修正,实验不确定度为1.5%驻点压力,实验数据来源于文献[9].图1 沿艇体表面压力系数的网格收敛解Fig.1 The grid convergence of the pressure coefficient profile along the full surface3.2 验证压力系数3种网格的计算解需要全部插值到和实验数据相同的点,由式(5)计算得RG=0.52,上述RANS解是单调收敛的,并得到验证.由式(6~9)可以分别计算相应的参数.解的精度为其中修正因子为其中PGest=2.网格不确定度为数值误差主要考虑网格尺寸误差δG,迭代误差忽略不计,因此,数值模拟的不确定度为计算结果见表1.表1 压力系数验证结果Table 1 Results of verification uncertainty for the pressure coefficientRGPGCGUG/%USN/%0.5181.8980.9310.2420.2423.3 确认比较误差为 E=D-S式中,D为实验数据,S为细网格模拟结果.确认不确定度为计算结果列于表2.表2 压力系数确认结果Table 2 Results of validation uncertainty for the pressure coefficient %EUVUDUSN1.5061.521.50.242由于E<UV,UV在水平上确认实现,不确定度为1.52%驻点压力.3.4 局部计算结构的误差和不确定度分析为了全面准确了解解的变化情况,需要对局部解的误差和不确定度进行分析.可以利用上文中计算整体误差和不确定度的方法,得出每一点的误差和不确定度的分布(E, UV)如图2.当E处在±UV之间时,表明解在不确定度UV的水平上得到确认.由图2可见,除了艏部有少量点外,细网格的模拟误差绝大部分介于确认不确定度区间中.而局部的压力系数无法得到确认是由于图中艇体的首端、中段与首尾端的交接处以及尾端附近存在涡流现象,流体在艇体尾部的速度趋近于零,接着脱离尾部逐渐形成尾流.图2 压力系数计算误差和不确定度的分布Fig.2 The distribution of error and validation uncertainty for the pressure coefficient4 结论本文对潜艇模型SUBOFF光体稳态流动进行数值计算,并且作了不确定度分析,通过验证和确认,结果是可信的.数值不确定度的值比实验不确定度的值小一个数量级,说明数值方法能满足要求,但是对网格依赖性很强,这也符合二阶离散格式依赖网格的特点.确认得以实现,说明湍流模式是可以满足精度要求的.本文也体现了CFD和EFD相结合,即通过实验和计算,修正模式方程、建立较精确的湍流模式方程的求解方法.参考文献[1] 胡健,黄胜,王培生.螺旋桨水动力性能的数值预报方法[J].江苏科技大学学报:自然科学版,2008,22(1):1-6.Hu Jian, Huang Sheng, Wang Peisheng. Numerical prediction about hydrodynamic performance of propeller[J]. Journal of Jiangsu University of Science and Technology:Natural Science Edition, 2008,22(1):1-6.(in Chinese)[2] 姚震球,高慧,杨春蕾.基于滑移网格的带螺旋桨艇体尾流场数值分析方法[J].江苏科技大学学报:自然科学版, 2008,22(2):15-20.Yao Zhenqiu, Gao Hui, Yang Chunlei. Numerical simulation of interaction between submarine and propeller based on approach of sliding mesh[J]. Journal of Jiangsu University of Science and Technology:Natural Science Edition, 2008,22(2):15-20.(in Chinese)[3] Roache P J,Ghia K,White F.Editorial policy statement on the control of numerical accuracy[J].Journal of Fluids Engineering,1986,108(1):2-3.[4] Coleman H W ,Stern F.Uncertainties in CFD codevalidation[J]. Journal of Fluids Engineering,1997,119(2):795-803.[5] Stern F,Wilson R,Coleman H ,et al.Verification and validation of CFD simulations[R].Iowa Institute of Hydraulic Research,IIHR Report No.407. Iowa City: The University of Iowa,1999.[6] Van S H,Kim J,Ryong P I,et al.Calculation of turbulent flows around a submarine for the prediction of hydrodynamicperformance[C]//The 8th International Conference on Numerical Ship Hydrodynamics. Busan, Korea:[s.n.], 2003:22-25.[7] 朱德祥,张志荣,吴乘胜,等.船舶CFD不确定度分析及ITTC临时规程的初步应用[J].水动力学研究与进展,2007,22(3):363-370.Zhu Dexiang, Zhang Zhirong,Wu Chengsheng,et al. Uncertainty analysis in ship CFD and the primary application of ITTCprocedures[J].Journal of Hydrodynamics,2007,22(3):363-370.(in Chinese)[8] ITTC QM Procedure 7.5-03-01-01[S]. 2002.[9] Huang T T,Liu H L,Groves N,et al.Measurements of flows over an axisymmetric body with various appendages in a wind tunnel:the DARPA SUBOFF experimental program[C]// Proceedings of 19th Symposium on Naval Hydrodynamics. Seoul Korea:[s.n.],1992.。

Mathematical simulation of multiphase steelmaking reactions

Mathematical simulation of multiphase steelmaking reactions

Mathematical simulation of multiphase steelmaking reactionsin the electric arc furnaceRodolfo D. Morales1), M. Macias1), Ruben G. Lule2) and FranciscoLopez2)1) Instituto Politécnico Nacional-ESIQIE, Dept. Metallurgy, Lindavista, Mexico D.F., CP 073382) Arcelor-Mittal Steel, Process Engineering Group, Lazaro Cardenas Michoacan. CP 60950Abstract: A mathematical simulator for the electric arc furnace has been developed. The main characteristic of thissimulator is the integration of all steelmaking reactions having place at the main interfaces in the electric arc furnace.These reaction-interfaces include metal-slag, gas-liquid, slag-carbon, carbon-gas and slag-gas and solid-gas involving, therefore, a complex multiphase system just as the steelmaking process actually is. Additionally, the present simulatorhas the capacity to model the kinetic behavior of the furnace as affected by intermittent operations such as feeding oflime, reduced iron pellets and oxygen lancing. The simulator is provided with a model to calculate the dynamic changesof physical properties of the slag and uses them to calculate slag foaming properties. The integration of slag foamingindex into the kinetic equations makes possible to describe the dynamic foaming index which changes according to thespecific operating conditions of the furnace. The capability of this simulator to predict the process dynamics of theelectric arc furnace (EAF) was tested in current heats in a furnace with a capacity of 220 tons demonstrating anexcellent agreement between the predicted and actual process kinetics. This simulator can be used on-line, for processcontrol and off-line for process design purposes.Keywords: EAF, mathematical simulator, molten slag, foaming, interfaces1. IntroductionA mathematical simulator for EAF, melting scrap, was developed by Matson and Ramirez [1]. Other simplifiedmodels were reported by Fruehan [2].Kinetic models of steelmaking processes like BOF and EAF were developed by thegroup of the authors some years ago [3,4]. In these last two cases bath chemistry and temperature evolution with time arepredicted reasonably well. Specifically, the EAF simulator was based on a set of differential equations involving massbalances and kinetic expressions. However, some empirical correlations obtained through data fitting of actual EAF operations were employed and integrated into the model. Just to give two examples, lime dissolution and pellets ofdirect reduced iron (DRI) were integrated in this simulator through correlated expressions. These expressions, becauseof their empirical nature, are far away from being descriptive of the phenomenology of steelmaking processes. Thecurrent trend of steelmaking research has provided new insights which can be used to avoid the empirical approachesand then is advisable to use this knowledge to improve former mathematical simulators. Therefore, the aim of thepresent work is to improve our mathematical simulator with updated phenomenological models available in the open literature. The final objective is to count on a simulator focused on the phenomenology of steelmaking processes, particularly, EAF steelmaking. The following lines describe, in a summarized way, the main features of this simulatorand its corresponding application to a current EAF in the company Arcelor Mittal Steel located in Lázaro CardenasMichoacán, Mexico, hereinafter called here AMLC.2. Plant DescriptionSteelmaking facilities at AMLC consist of four EAF´s with a capacity, each one, of 220 tons of steel. These EAF´s are powered by 120 MW transformers delivering electric energy to the three electrodes. All furnaces are equipped with copper injection supersonic lances to supply oxygen and carbon powder to decarburize the melt and to foam slag. Steel is tapped through eccentric bottom tapping (EBT) system. DRI pellets are continuously feed through a hole on the roof. Materials like coke, lime and general fluxes can be fed also through that hole using an automatic electronically controlled system of transporting belts and hopers. A heal of liquid steel is left, from the precedent heat, to start the next heat charging scrap and feeding DRI pellets continuously at a rate governed by its metallization. Steel is later refined in three ladle furnaces and degasified in a vacuum degassed (VD) unit. Ultra low carbon steels are also refined in these facilities through a RHOB unit.3. Mathematical SimulatorThis simulator is divided in various mathematical models, each one dealing with different reactions and interfaces in a structure that allows each model to run independently to study specific phenomena. When all models are assembled then the simulator is ready to make predictions of process dynamics including all reactions at all interfaces. In the next lines each one of these models is described in a summarized scheme.3.1 Static ModelTo run a simulation first data, generated by a static model involving mass and energy balances, must be performed. This static model consists of a linear programming set of equations to find optimum charge policies of raw materials, mainly looking for the cheapest charge for a given heat or minimum possible energy consumption. The set of linear equations are derived from mass balances for metal and slag phases and energy balance of all steelmaking reactions. The SIMPLEX algorithm [5]is used to solve the system of linear equations to find out optimum points. All data generated by the static model are later used as initial boundary conditions to solve a set of differential equations describing the process dynamics. Therefore, initial slag chemistry is derived from this model and a thermodynamic model, to be described in the next section, start to calculate activities for all components in slag.3.2 Thermodynamics for Multicomponent SlagsThe kinetic expressions of this simulator require the knowledge of thermodynamic activities of steelmaking slags. Consequently, a thermodynamic model was developed to calculate those activities at each time step during the numerical integration of the differential equations. For this purpose the modified quasi-chemical model developed by Pelton and coworkers [6-8] was selected.Basically, this model is based on the hypothesis that when a basic oxide mixes with a silicate slag, it promotes In order a decrease in the extent of de-polymerization due to the rupture of Si-O bonds according with the following reaction:This is equivalent to,Full de-polymerization occurs for a ratio MO/SiO2=2, which corresponds to X SiO2 =1/3 being this the same composition corresponding to maximum ordering for silicate slags and is attributed to the formation of orthosilicate ions. The quasi-chemical model assumes that the atoms mix substitutionally on a quasi-lattice with a constant coordination number (z=2), forming, for a binary silicate slag the following pair of bonds: M-M, Si-Si and M-Si, where the energy change is dependent upon the local environment of the pair-bonds involved. Equation (2) can also be represented as a reaction expressing the different types of bonds in the binary silicate system (MO-SiO2) as follows:[M-M]+[Si-Si]=2[M-Si] (3)[M-M] and [Si-Si] represent the two terms on the left side of Eq. [1], while [M-Si] represents the term on the right side of the same equation. A change in free energy in the previous reaction indicates a random distribution of M-Si bonds, while if the change in free energy is negative,then, the formation of pair-bonds M-Si is promoted, but if the change in free energy is positive, then, the formation of pair-bonds M-M and Si-Si is now predominant. Thus, in order to describe the thermodynamic properties of slags in the quasi-chemical model, the concentration of pair-bonds must be computed (X ij). This is in fact, the major difference with the structural model since in such a model the free energy change is based on the concentration of oxygen ions. The expression representing the free energy change for reaction (3) is the following,is the molar enthalpy change involved in Eq. [3], which is expressed in terms of pair-bond energies («M-Si), and is the non-positional entropy change. When is a negative, M-Si pairs predominate. The excess entropy (S E)is the sum of positional and non-positional entropies. Equation (4) is then derived and equalized to zero to find out the minimum for concentrations of each pair of bonds.3.3 Carbon-Gas and Carbon-Slag InterfacesFigure 1a is scheme of the reaction interfaces in the steelmaking-multiphase system used in the construction of this simulator and Figure 1b shows an enlarged scheme of carbon-gas and carbon-slag interfaces. This model simulates the injection of carbon particles in the slag to control its oxidation level and maintaining the metallic yield. It is based on the work of Morales et al.[9]. Consists on an energy balance, the energy of the particles in the gaseous phase ()of the energy of the particles in the gas phase is employed to overcome and interfacial resistance W, to displace a portion of the liquid slag in front of the interface due to the penetrations of the solid particles and to maintain their own energy within the liquid slag,. The energy balance is given by the following equation:This energy balance permits the calculation of fraction of solids that is able to pass through the gas-liquid interfaceand react with the liquid slag to reduce the iron oxide content through the reaction,However, the contact between carbon particles and the molten slag is not direct it takes place through the gas phase, this means that the solid particle remains in the core of a gas bubble and (FeO) reacts with CO to be reduced accordingto,CO 2 diffuses through the gas phase to react with the carbon particle, to rebuild the CO supply through theBoudouard´s reaction,Therefore reaction (6) is the overall reaction of two partial reactions (7) and (8).(a)(b) Fig. 1—(a ) Chemical reaction subsystems: ① metal-slag interface, ② carbon-slag interface, ③ oxygen-slag-interface, and ④ DRI melting. (b ) Closeup of the reaction mechanism for the reduction of iron oxide with carbon particles.Fig. 2—Relationship between the volume fraction of particles that cross the gas-liquid slag interface and the volume fraction of particles.The surface tension of steelmaking slags depends, evidently, on their chemistry being silica an active component that decreases this physical property. In other words, silicate molecules are adsorbed in the gas-liquid interface poisoningthe bubble available area for reaction avoiding the contact between the gas phase and slag at the gas-slag interface. To estimate the fraction of the available area of bubbles occupied by silicate molecules a Langmuir type equation for adsorption was used and using data of surface tension for slags [10]an expression linking this fraction with the slag chemistry was derived as follows:With the thermodynamic model, described above, the silica activity in the molten slag can be calculated and its magnitude is substituted in equation (9) together with the mass % FeO calculated with the kinetic model making possible to know the occupied fraction. The fraction of particles that cross over the slag-gas interface is plotted against the resistance to penetration given by the ration between the interface energy opposing penetration to the kinetic energy contained in the solid particles just before they penetrate this interface and is plotted in Figure 2. The fraction of solids unable to pass through the metal-slag interface is carried out by the gas toward the furnace exhausting system. The kinetic expression for iron oxide reduction with carbon particles is given by,Where is the mass of FeO reduced by carbon, t is time, M FeO and M C are the molecular weight and atomic weightof FeO and Carbon, Q s is the mass flow rate of carbon particles (calculated through a separated model as reprtedin reference (4)) is the final size of a particle after a computing time step and is dependent on time. One example of numerical results of this model is given in Figure 3. There is seen that for a fixed flow rate of carrier gas of carbon particles reduction rate increases with mass flow arte of solids and decreases with silica activity as it is expected given the poisoning effects of this oxide. During injection of carbon in the EAF the simulator is informed with a size distribution curve of these particles and using a Monte Carlo simulation approach; the simulator chooses the size to be simulated at some given computing time step in a random fashion.Fig. 3—Effect of silica activity on the rate of reduction of iron oxide for three different carbon particle sizes andtwo solid flow rates.Fig. 4—Effect of residence time and carbon particle size on the consumption of carbon particles.3.4 Solid-Liquid Interface, the Lime Dissolution ModelDissolution mechanisms of lime in steelmaking slags are influenced by the chemistry of slag and limited by the saturation level of dicalcium silicate. In this work the lime dissolution model of Dogan et al. [11]is used, then the dynamic changes of lime weight is given by the equation,Where W L is lime weight, r is lime particle radius, S is surface area of particle, k is mass transfer coefficient and n L is number of lime particles, speed dissolution in equation (11) is calculated through the next equation,Where (%CaO)eq is the solubility of CaO obtained from equilibrium diagram CaO-FeO-SiO2. The mass transfer coefficient was calculated through a correlation for the Sherwood number given by,In this equation d p is particle diameter and εCO is the stirring power generated by CO evolution from the steelmaking reaction and from the reduction reaction of iron oxide by carbon particles and is given by,Where Q CO is volume of CO evolved by the reactions and is calculated by the kinetic model, h is slag height, T s is slag temperature, W s is its weight and P a is ambient pressure.3.5 Metal-Slag InterfaceFruehan [2] and Sommerville [12] assume that reaction of decarburization at the metal-slag interface is carried out in two steps. In the first one carbon dissolved in molten metal reacts with CO2 to produce CO and then CO reacts with FeO:(15)The reaction of CO with FeO is the same as that in equation (7). The final expressions for the rate of consumption of FeO by the reaction reduction with carbon dissolved in the metal and the change rate of carbon dissolved in metal phase are given by the following expressions;Rate reactions and are calculated using the fraction coverage of this interface using equation (9) and the kinetic rate constants reported by Wei et al.[13].3.6 Gas-Liquid Interface, Decarburization and FeO Formation by OxygenChemical reactions for carbon and iron oxidations are represented as follows,If carbon concentration is high iron oxide is easily reduced as is accounted by Equation (16) and then the decarburization reaction is controlled by the supply rate of oxygen. If the carbon concentration in the metal is low then iron is oxidized and the decarburization reaction is controlled by mass transfer of carbon in the metallic phase. Therefore, decarburization kinetics was calculated using the expressions proposed by Matsuura et al. {14}, according to,Where R PC is the post-combustion ratio, k m is the mass transfer coefficient, A is the interfacial area for reaction, is melt density, W M is its weight and Q O2 is the flow rate of oxygen blown through the lancing system. From industrial experience the critical carbon concentration for a shift from reaction control by rate of oxygen supply, equation (21) to mass transfer of carbon in the metals is smaller than 0.2%, equation (20).3.7 Solid-Liquid Interface, Melting DRI Pellets in BathNo being able, at the moment of writing this work, to build a reliable model for melting DRI pellets the former expression for DRI feeding rate as a function of metallization already published 4) was employed according to,3.8 Dynamic Foaming IndexSlag foaming is one of the most important operation in modern EAF steelmaking to assist in decreasing consumption of electric energy. Here a dynamic foaming index [4], based on the laboratory foaming index as defined by Ghag [15] is used here in order to follow variations of this important parameter with process time as a function of bath (metal and slag) chemistries and specific furnace operating conditions. The expression of this index is;Where Σis Ghag´s index and f r(t) is a time depending ratio of time depending volumes of gas in slag-gas emulsion andslag according to,The evolution of volume rate of gas in the slag-gas emulsion is given by,All derivatives in equations (25) are calculated with the general kinetic model at each computing time step permitting the monitoring of the slag foaming time during the process time. Ghag´s laboratory data for foaming index corresponding to the CaO-SiO2-FeO system were fitted through a polynomial expression according to;3.9 Bath TemperatureTemperature of molten bath is computed based on a simplified relationship for the liquidus line from the Fe-C phase diagram adding a superheat of 100 K, according to,At each carbon concentration, calculated through the general kinetic model, the bath temperature is calculated with the simple expression given in equation (28) and this temperature is used to calculate all slag and metal thermodynamics as well as all properties of gas, slag and metal phases. Slag viscosity was calculated using the Urbain model [16] and other physical slag properties were consulted in reference [17].4.General Kinetic Model to Predict Dynamics of Steelmaking in EAFAll these models were assembled to build the general kinetic model which includes the kinetic expressions and mass balances for each oxide in slag and each component in molten metal. This model is summarized in Table I.5. Results and DiscussionFigure 4 shows, for a given amount of particles, the percentages of particles consumed or reacted with iron oxide as function of the residence time in the slag phase. The smaller the particles are the more are consumed. Figures 5 and 6 show two simulations of two industrial heats employing different qualities of DRI pellets for a low metallization (87%) and high metallization (95%9, respectively. In these simulations carbon particle size is 250 μm. In both cases, the pattern of carbon and oxygen injection is as follows; Oxygen is injected from minute 10 until the end of the heat with a specific consumption of 10 Nm3/ton of steel. Carbon particles are injected at minute 30 with a mass flow of 60 kg/minute and the flow rate of the carrier gas is 350 Nl/minute; this injection stops at minute 90. The dynamic foaming index (DFI) for both heats is also plotted in these figures too. The results with low DRI metallization indicate a much higher initial concentration of FeO as high as 43% compared with 20% for the case of High DRI metallization. Later,the FeO concentration increases with injection of oxygen. This trend stops as soon as carbon injection starts; then, the concentration of FeO decreases continuously until it reaches a final concentration of FeO, at minute 90, of 30% and 10% for low and high DRI metallization, respectively. The DFI observed in these plots indicates two peaks, The first peak corresponding to the start time of oxygen injection indicating that this operation rises the slag foaming by, CO generation, for some time, after the effect decreases. That is, the injection of oxygen, produces large amounts of CO which foams the slag phase and the injection of carbon contributes also to the formation of larger amounts of CO. The minimum DFI is achieved at the end of the heat when the carbon injection stops. Comparing the DFI for both cases, higher DFI magnitudes are observed for the heat with lower DRI metallization with higher FeO concentrations. Iron oxide decreases the foaming index but apparently its reaction with carbon produces large amounts of CO to counteract the negative effects of FeO on slag foaming.Fig. 5—Simulation of changes in slag chemistry and DFI during a hypothet- ical heat using a low metallization DRI. Fig. 6—Simulation of changes in slag chemistry and DFI during a hypothet- ical heat using a high metallization DRI.The fact of using or not Monte Carlo simulation for reduction of iron oxide with carbon particles plays an important effect in predicting chemical changes of industrial slags. Indeed, Figure 7 show chemistry evolution of an industrial slag determined through the chemical analysis of samples taken from furnace number 4 at AMLC, using a fixed particle size of 250 μm (no Monte Carlo Simulation), which are fed at mass flow rate of 58 kg/minute with a flow rate of the carrier gas of 350 l/minute and DRI metallization of 90%. As is observed, ´predictions of the mathematical simulations and actual slag chemistry is excellent, as is always the case, when carbon concentrations fall down below 0.2% iron is oxidized to form iron oxide. The predictive capacity of this simulator was tested through the simulation of 15 heats and the relation between predicted slag chemistry, for each oxide in slag, and actual slag chemistry determined by chemical analysis is plotted in Figure 8. The mutual relation between each type of data is remarkably good indicating the very good capability of this simulator to predict reliably the dynamic behavior of actual heats. However, assuming a normal distribution of sizes for carbon particles going from 20 to 1000 introduced into the Monte Carlo simulator the predicted slag chemistry dynamics during carbon injection follow more irregular curves as is seen in Figure 9. Comparing with results presented in Figure 9 with those presented in Figure 7 is clearly seen that Monte Carlo simulations do not predict smooth curves for chemistry changes with time. Instead, chemistry curves yield irregular curves, particularly those corresponding to iron oxide, due to the random choosing procedure of particle size at each computing time step. Therefore, these type of predictions can be claimed to be closer to real furnace conditions.Fig. 7—Theoretical model predictions and comparison with industrial data on the chemical slag composition of the slag as a function of time, without Monte Carlosimulation.Fig. 8—Comparison between model predictions and industrial data on the chemical composition of slag for the whole set of heats studied.Predictions of slag chemistry dynamics for a heat using DRI of very low metallization of 84-86% with a specific oxygen consumption rate of 3.7 m3/ton of steel, lime consumption of 54 kg/ton of steel, injection of carbon particles with a mass flow rate of 3.18 kg/ton of steel are presented in Figure 10. FeO rises up to 42% during the first 35 minutes, when oxygen and carbon are injected simultaneously. The prediction of slag composition for CaO and SiO2 are close to actual values, while in the case of FeO predictions during the 30 minutes of sampling are lower than the actual values. In spite of this the predicted final FeO concentration of FeO before tapping is closer to the actual concentration. The first and last analysis of FeO correspond to routine sampling, the first analysis is used by the furnace operator to establish the policy for oxygen injection.Fig. 9—Theoretical model predictions and comparison with industrial data on chemical composition of the slag as a function of time, using Monte Carlo method.Fig. 10—Simulation changes of slag chemistry for an industrial heat (I). The points indicate experimental measurements of chemical composition of slag.Predictions for another heat which used a DRI with a metallization of 91% was used, with a specific oxygen consumption rate of 50 kg/ton of liquid steel, a mass flow rate for injection of carbon equivalent to 30 kg/minute or 6.36 kg/ton of steel are presented in Figure 11. This heat starts and ends with lower concentrations of FeO compared with the previous heat because a higher DRI quality was melted. Nevertheless, it should be pointed out that the same results can be obtained through different process routes involving mainly procedure of carbon and oxygen injection and charging policies of lime and good DRI metallization does not ensure low final FeO contents and high metallic yields if theration between injections of oxygen and carbon are not properly balanced. DFI predictions are compared with the so called arc distortion. This parameter is a measure of the noise magnitude during the electrical arcing on the bath. As the arc distortion increases so does the magnitude of the noise, then, when slag foaming arises, both the distortion and the noise are smaller. Arc distortion is recorded from the panel control of the furnace and the corresponding data are stored in the process control computer. The complete different nature of the arc distortion makes difficult, if not impossible to achieve a full correlation between arc distortion between and DFI but their comparison must yield a qualitative agreement at least. To have the same trends between arc distortion and DFI, the fist is plotted as the reciprocal of arc distortion and compared directly with DFI in Figure 12. In this particular heat the DRI melted had a metallization of 91%, the mass flow of carbon was 65 kg/minute and the specific consumption of oxygen was 7.7 m3/steel ton. The theoretical behavior of DFI indicates a slight foaming condition at the beginning of the hate decreasing steadily later on. At some certain time, after 20 minutes after starting there is magnitude peak of DFI. This trend of DFI corresponds very well to the actual reefing of this heat since at the beginning there is some amount of oxidized slag reacting with carbon in the melt making possible a moderate slag foaming. However, as fasr as carbon is not injected carbon the reaction stops and the slag foaming decreases. At minute 20 carbon and oxygen injections are applied inducing an intensive slag foaming well predicted by the appearance of the DFI peak. Close to the end of the heat a large CO evolution is predicted by the simulator, however, slag foaming decreases since oxidized slags have large density and smaller surface tensions decreasing the foaming capacity. The reciprocal of the arc distortion does not follow the exact path of DFI but it can be said that a qualitative agreement of the trends of these variables are the same. Therefore, it can be said that the present simulator has the capability for predicting slag foaming as dependent of furnace operation policies.Fig. 11—Simulation of changes of slag chemistry for an industrial heat (II). The points indicate the experimental measurements of chemical compo- sition ofslag.Fig. 12—Evolution of the dynamic foaming index in EAF steelmaking for an industrial heat compared with both the evolution of CO and actual 1/arc distortion.Closing RemarksThe production of steel involves quite complex phenomena and understanding the mechanisms which lead to control them has been the driven force to improve EAF technology. There is a strong need to develop phenomenological models oriented to perform process control in order to achieve a more efficient use of global resources.References[1] S. Matson and W.F. Ramirez: Proc. 55th Electric Arc Furnace Conf., ISS; Warrendale PA, 1997, 675-685.[2] R.J. Fruehan: Proc. Elliot Symp. On Chemical Process Metallurgy, ISS Warrendale PA, 1991, 1-10.[3] P.G Garnica, R.D. Morales and N.U. Rodriguez: Proc. 77th Steelmaking Conf., ISS, Warrendale PA, 1994, 189-198.[4] R.D. Morales, A.N. Conejo and H.H. Rodriguez: Metall and Mater, Trans. B, (2002) 33B, 187-199.[5] G. Geiger: Direct Reduced Iron: Technology and Economics of Production and Use, R.L. Stephenson and R.P.Samailer Eds., TMS-AIME, Warrendale PA, 1980, 149-159.[6] A.D. Pelton and M. Blander: Peroc. 2nd Symp. on Metallurgical Slags and Fluxes, TMS-AIME, Warrendale PA,1984, 281-291.[7] A.D. Pelton and M. Blander: Metall. Trans. B, 1986, 17B, 805-815.[8] G. Eriksson and A.D. Pelton: Metall. Trans. B, 1993, 24B, 807-816.[9] R.D. Morales, H. Rodriguez, P. Garnica and A. Romero: ISIJ Int. 1997, 37, 1072-1080.[10] H.H. Rodriguez, A.N. Conejo and R.D. Morales: Steel Res., 2001, 72, 298-303.[11] N. Dogan, G.A. Brooks and M.A. Rhamdhani: ISIJ Int., 2009, 49, 1472-1482.[12] I.D. Somerville, P. Grievson and J. Taylor: Ironmaking and Steelmaing, 1980, 7, 25-31.[13] P. Wei, M. Sano, M. Hirasawa and K. Mori: ISIJ Int., 1991, 31, 358-365.[14] H.Matsuura,C.P. Manning,R.A. F. O. FORTES and R.J. Fruehan: ISIJ Int. 2008, 48, 1197-2105.[15] S.S. Ghag, P.C. Hayes and H.G. Lee: ISIJ Int., 1998, 38, 1201-1207.[16] K.C. Mills: Viscosities of Molten Slags, Verlag Stahleisen GmbH, Dusseldorf, 1995, 353.[17] B.J. Keene and K.C. Mills: Physicochemical Properties of Slags, National Physical Laboratory, London, 1977.。

考虑模糊推理及蒙特卡洛方法的毁伤评估研究

考虑模糊推理及蒙特卡洛方法的毁伤评估研究

考虑模糊推理及蒙特卡洛方法的毁伤评估研究作者:菅鲁京李运泽张加迅何宗波王玉莹来源:《航空兵器》2018年第06期DOI:10.19297/ki.41-1228/tj.2018.06.013摘要:随着定向能技术的发展,热毁伤效能评估已经成为研究的重点方向之一,但目标毁伤程度的量化研究缺乏较为通用的评估方法。

为此,在有限元分析的基础上,考虑采用失效概率模糊推理和蒙特卡洛法相结合的方法求解可靠性。

分析结果表明,该方法可以有效量化评估毁伤效应。

关键词:模糊推理;蒙特卡洛法;毁伤评估中图分类号:TJ760.3+1;TN249文献标识码:A文章编号:1673-5048(2018)06-0078-06[SQ0]0引言与传统的毁伤方式不同,热毁伤效应评估一直是定向能技术研究的一个难题。

国内外许多研究人员从毁伤机理、试验分析、数值计算分析、可靠性分析等多个方面进行了研究[1]。

文献[2]研究了高功率辐照对材料造成的热、力、辐射破坏,模拟了材料毁伤发生概率和影响因素。

文献[3]采用试验和数值计算方法研究了高能辐照对铝和铜材料的毁伤效应。

文献[4]研究了热毁伤效应,以无限大均匀介质热扩散方程为基础提出了辐照条件下目标材料热毁伤评估方法。

文献[5-6]通过结合蒙特卡洛随机模拟和模糊分析方法得出结构可靠度评估,给出较准确的结构可靠性计算结果。

本文在有限元分析、模糊推理、蒙特卡洛法的基础上提出了基于可靠性计算的可靠性评估法,实现对部件毁伤的定量描述。

1毁伤机理定向能功率密度较小时,目标材料在吸收大量能量后会升温,还会在加热区外传热。

当功率密度进一步提升时,材料局部区域的温度会升高到熔化温度,如果继续以较高的速率沉积能量,这个局部区域材料就会发生熔融。

如果功率密度达到MW/cm2,吸收能量的材料就可能经历一系列过程达到气化,当强度超过气化阈值时,辐照将使目标材料持续气化,这个过程称作热烧蚀。

当强度足够高、气化很强烈时,将会发生材料蒸气高速喷出把部分凝聚态颗粒或液滴一起冲刷出去的现象,从而在材料上造成凹坑甚至穿孔。

多级火箭级间分离流动特性的数值模拟

多级火箭级间分离流动特性的数值模拟

多级火箭级间分离流动特性的数值模拟刘 君,徐春光,郭 正(国防科技大学航天与材料工程学院,湖南长沙410073) 摘 要:针对某型号研制提出一种级间分离设计方案,从三维薄层近似N-S方程出发,对多级火箭级间分离发动机喷流与超声速外流形成的复杂流动进行了数值模拟,比较了栅栏构架和排焰孔两种设计方案级间段流场,从流动形态分析三维排焰孔模型更为稳定。

研究给出不同分离距离下火箭的轴向力系数,发现在较近分离距离内,后体受到的作用力随距离不是单调减小,与分离喷流参数有密切关系。

计算结果可作为分离段设计的参考。

关键词:多级火箭;级间分离;射流;数值仿真;纳维尔-斯托克斯方程中图分类号:V430 文献标识码:A 文章编号:1001-4055(2002)04-0265-03Numerical simulation on the stage-separation flow fieldsof the multi-stage rocketLIU Jun,XU Chun-guang,GUO Zheng(Inst.of Aerospace and M aterial Engineerin g,National Univ.of Defence Technology,Changsha410073,China)A bstract: A design of stage-separation of multi-stage rocket is discussed.Detailed numerical investigation of the interaction be-tween an engine-jet and the supersonic external flow was preformed for several models.Numerical predication of the flow was obtained using an existing3-D TLA N-S computational technique.The flow structures for different models of the design were compared,the re-sults show the model with inducing holes is better.The drag on the aft bod y changed nonlinerly with different separation distance.The result can provide bas is for the design of the stage separation part.Key words: Multistage rocket;Stage separation;Jet flow;Nu merical simulation;Navier-Stokes equation1 引 言级间分离技术大体可分为冷分离和热分离两种[1]。

新型反应装甲结构对长杆弹小法线角侵彻的干扰分析和防护效能

新型反应装甲结构对长杆弹小法线角侵彻的干扰分析和防护效能

新型反应装甲结构对长杆弹小法线角侵彻的干扰分析和防护效能熊良平;黄道业;王凤英【摘要】A new explosive reactive armor (ERA) structure was designed. A series of dynamic analyses were carried out on the interferences of the new ERA structure with the motion velocity and attitudes of the long-rod projectiles, the corresponding dynamic equations were obtained for the long-rod projectiles in the mass conservation condition, and the protection effectiveness of the new ERA structure was experimentally validated. Investigated results show that the new ERA can disturb effectively the motion attitudes of the long-rod projectiles with small yaw angles and induce their kinetic energy decrease markedly.%针对防御小法线角入射的长杆弹的需要而设计出了一种新型反应装甲结构.通过分析新型反应装甲结构对长杆弹运动速度和姿态的干扰,得出在长杆弹质量守恒的情况下的动力学方程,并进行反应装甲抗长杆弹侵彻实验.结果表明,新型反应装甲能够有效的干扰小角度长杆弹的飞行姿态并造成动能损失,可提高对长杆弹的防护效果.【期刊名称】《爆炸与冲击》【年(卷),期】2013(033)001【总页数】5页(P108-112)【关键词】爆炸力学;新型反应装甲结构;侵彻实验;长杆弹【作者】熊良平;黄道业;王凤英【作者单位】中北大学机械工程与自动化学院,山西太原030051【正文语种】中文【中图分类】O385;TJ55爆炸反应装甲(explosive reactive armor,ERA)是抵御反坦克武器的主要装备,具有抗弹效益高、安全性好、质量轻、占用空间小等优点。

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Numerical simulation of interaction during the top blow in a steel-making converterHranislav Milosevic a ,Svetlana Stevovic b ,⇑,Dojcin Petkovic aa Serbian University Pristina,PMF Kosovska Mitrovica,SerbiabFaculty of Construction Management,University Union,Belgrade,Serbiaa r t i c l e i n f o Article history:Received 28February 2011Accepted 16May 2011Available online 14June 2011Keywords:Numerical modeling Interaction Turbulent flow Navier-Stokes Flow structurea b s t r a c tThe numerical modeling of interaction between oxygen high-pressure jets and a liquid–metal surface in a steel-making converter,where,under the force action of the jets,a cavern with a hydrodynamically unstable surface forms in the metal bulk,is studied.A simplified scheme of chemical reactions and mech-anisms of metal-drop dispersing from the interface between the phases is proposed.This scheme permits an adequate description of the hydrodynamic flow pattern in the cavern.The modeling of a two-phase turbulent flow in the cavern is considered within the framework of the continuum model based on the averaged Navier–Stokes equations.To close the equations,a modified k –e turbulence model is used,which takes into account the presence of the second phase.The flow structure in the cavern is studied.Practical recommendations for increasing the efficiency of the carbon-monoxide afterburning process in the cavern are given.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionThe top blow of a cast-iron bath in a steel-making converter is used to burnout the dissolved carbon,which is the essence of the cast-iron-to-steel conversion [1].The blow is conducted using a system of high-pressure supersonic and subsonic oxygen jets impinging on the cast-iron bath surface.Under the force action of these jets,a gas cavity (cavern)with a hydrodynamically unsta-ble interface between phases forms in the metal bulk [26].From this surface,an intense dispersing of finely dispersed metal drops into the cavern proceeds,which causes a manifold increase in the surface area where the interaction between the metal and oxy-gen takes place.The main gaseous products of this interaction are CO and CO 2[29],and,to increase the efficiency of the conversion process and to decrease the amount of harmful effluents,one should raise the afterburning degree of CO and CO 2in the working volume of the converter by improving the scheme of the top blow-ing [28].The studies in this direction are an urgent matter for all con-verter plants of Russia [18]and all other CIS countries [20]since the waste gases are not recovered there for their subsequent use as a fuel.On the whole,the problem of carbon monoxide afterburn-ing inside a converter is the object of numerous studies [23,25].However,up to the present time,it has found no satisfactory solution [16]because of the complexity and poor understandingof processes that proceed during the interaction of oxygen with cast iron [17].In this work,an attempt is undertaken to consider the gasdynamic aspect of the problem,with due regard for both the formation of a cavern and the structure of a two-phase flow in it,and with allowance for the occurrence of nonequilibrium chemical reactions.2.Physical model of the processA typical scheme of the top blow is shown in Fig.1.Through a special vertical tube (lance),commercially pure oxygen is fed,which flows out of a multi-nozzle head in the form of a system of sub-and supersonic jets of an adjustable strength of their action on the metal surface in the bath [2].In this way,conditions are pro-vided for a controlled steel melting with minimum losses of metal and for increasing the effectiveness of the off-gas afterburning in the converter without any substantial deterioration of its firebrick lining.According to data reported in (1),the refinement of the metal under the action of oxygen proceeds in two stages,the first one being metal oxidation with the predominant formation of ferric oxides (initial reaction zone)and the other being interaction of these oxides with chemical elements dissolved in the metal bath (secondary zone).The main gaseous reaction product in the sec-ondary zone is carbon monoxide which is released from the reac-tion bed and incomes into the gas cavity where its partial afterburning takes place as it interacts with the oxygen jets accord-ing to Milosevic [12].Although the conversion of the cast iron into the steel is described by a system of several tens of chemical reactions (1)an insight into the gasdynamic flow pattern in the0017-9310/$-see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.ijheatmasstransfer.2011.05.018⇑Corresponding author.E-mail addresses:mhrane@ (osevic),svetlanas123@ (S.Stevovic),dojcin.petkovic@ (D.Petkovic).cavern can be gained,as studies showed,by considering the fol-lowing simplified scheme of the process:–the liquid phase includes the melt with 4%of carbon (C)and 96%of metal (Fe);–at the interface between the phases,the only generalized reac-tion of carbon combustion takes place:2C þO 2¼2CO;ð1Þ–the afterburning of the carbon monoxide proceeds in the cavern through a gas-phase reactionCO þO 2¼CO 2;ð2Þ–at the gasdynamically unstable gas/metal interface,liquid–metal drops (shots)form,which enter the cavity.At the surface of the drops,reaction (1)proceeds as well;–formation of slag-metal emulsion on the metal surface is ignored.3.Mathematical modelSince,as already noted,the main objective of this work is a study of the gasdynamic aspect of the process of interest,we simplify the real scheme of the top blow shown in Fig.1and assume that the flow is axisymmetric and the lance ends in a hemisphere,from the surface of which two jet of commercially pure oxygen flow are ejected (Fig.2):a central (supersonic)jet and a side (subsonic)jet whose inclination can be varied.In this axisymmetric formulation,theside jet is a fan one,which well models the multi-nozzle design of real heads (2).Next,we assume that the jet efflux is steady,the shape of the interface is not known beforehand and is given by the condition of equality between the gas-phase pressure and the hydrodynamic pressure in the liquid metal at each point of the interface.The flow in the gas cavity of the cavern contains sub-and supersonic zones and shock waves,and is two-phase and turbulent.Additionally,the following assumptions are adopted:–the motion of the gas phase is ignored,and only the hydrostatic pressure is taken into account;–the gas phase includes five components,O 2,CO,CO 2,N 2and H 2O,with the only chemical reaction (2)proceeding in it;–the mass concentration of carbon in the metal is assumed con-stant since the characteristic duration of the gasdynamic pro-cesses proceeding in the cavern is by several orders of magnitude shorter than the converter melting duration.The components N 2and H 2O are assumed neutral.The presence of molecular nitrogen is caused by its presence in the air which can be sucked by the supersonic jet into the cavern from the ambient space.The presence of steam,as was noted in [3],is necessary for the occurrence of reaction (2),the complete scheme of its for-mation being,however,rather complex and concentration small (within 0.1%[1]).Therefore,in the present study it is assumed that H 2O incomes from the interface in the above-indicated concentration.To describe the flow of interest,a continual approach was used,within which the system of governing equation written in the vec-tor-tensor form isr ðy q ~UÞ¼yJ ;ð3Þr ðy q ~U ~U Þþr y q Àr ðy s Þ¼y q p C R ð~U p À~U ÞÀJ ~U p h i ;ð4Þr y q ~Uh Àk r T hi¼~U r yp þy U þq p C k ðT p ÀT ÞþC R ~U p ð~U p À~U Þn o h i;ð5Þr y q ~Uk Àðl trþl Þr k hi ¼Àyl t @u t @x k þ@u k @x t&þ23q k þl t @u i @x l d ik !@u t@x k þq e þe s þe JÀÁ'ð6Þr y q ½C i ~U ÀD i r C i ¼yJ i ði ¼1;...;5Þ;ð7Þk ¼c plPrþl tPr t;D ¼lScþl tSc t;p ¼q TR 0X 5i ¼1C i =M i ;ð8Þr y ðq p ~U p þh q 0p ~U 0p iÞ¼yJ p ;ð9Þr y ðq p ~U p ~U p þ~U p h q 0m ~U p iÞþr y q p h ~U 0p ~U 0p i ¼y q p C R ð~U À~U p Þ;ð10Þr y ðq p ~U p þh q 0m ~U p iÞh p þr y q p h h 0p ~U 0p i¼y ½q p C a ðT ÀT p ÞþJ p q ;ð11ÞJ ¼J 2ÀJ 1;J p ¼ÀJ ;J 3¼J 4¼J S ¼0;osevic et al./International Journal of Heat and Mass Transfer 54(2011)4275–4279where s and U are the viscous-stress tensor and the term allowing for dissipation owing to viscous forces in the averaged Navier–Stokes model(s also includes,through the dynamic viscosity l ef=l+l t the turbulent-stress tensor);l t is the turbulent dynamicviscosity;C R is the aerodynamic drag coefficient of a particle,C a the heat-transfer coefficient from a particle to the gas[15];the sub-scripts1–5and p refer to the gas components O2,CO,CO2,N2, H2O and the particles,respectively;J1is the massflow rate of O2, J2is the formation rate of CO2;M i are the molecular masses of the components;and k and e are the kinetic turbulent energy and its dissipation rate.The values of the coefficients C R and C a and those of correlations between thefluctuating parameters of the particles were deter-mined from the average parameters of the gas-carrier using the expressions reported in[4]and the terms e s and e J allowing for the additional dissipation k on the particles were borrowed from [5].In the stagnation region of the impinging jet,a modification of the k–e model proposed in[6]was used with correction of the turbulence model constant adopted for the free-flow region of the jet[7].In the calculation of the carbon-monoxide combustion rate,the gas-phase turbulence was taken into account so that the combus-tion rate was found from the expressionJCOÀmin f J ch;J tb g;J ch ¼1:3Á1014expðÀ30;000=RTÞC COffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiC O2C H2Oq;J tb ¼A qekmin C O2;C COs&';where J ch is the rate of the generalized chemical reaction of CO com-bustion taken from[8]and allowing for the effect of steam concen-tration on the combustion;J tb is the turbulent mixing rate given by the turbulent-vortex fragmentation model[9];s is the stoichiome-tric coefficient,and A is an empirical constant(A=2).The solution of system(3)–(11)was sought in the domain shown in Fig.2,which was covered by a rectangular irregular dif-ference grid.At the domain boundaries,taking into account the flow symmetry,the following boundary conditions were set:-at the exit sections of the nozzles(HK and FG)installed on the lance,the mass-flow rate of oxygen,the sloping angle of the jets to the symmetry axis,and the stagnation temperature equal to the ambient value were set;-at the interface AED between the phases,a constant pressure equal to the hydrostatic pressure in thefluid and the adhesion condition for the gas phase were set.The mass inflow rate of CO from the surface was determined from the carbon combustion reactionJCO¼ðM CO=M CÞq C O2q C K C expðÀE=RTÞ;where q C is the carbon mass concentration in the metal,K C–2Á105m/s and E=À158,000J/mole,and for CO the following boundary condition was set:q C COÀq D CO q C CO¼J CO;where D CO is the diffusion coefficient and n is a normal to the sur-face.For the other components of the mixture,the nonpenetration condition at the surface was used;–at the symmetry axis AA,the conditions offlow symmetry were set;–at the outlet boundary CD and at the boundary BC,‘‘soft’’bound-ary conditions and conditions imposed by the environment(air at the standard conditions),respectively,were used.An approach is proposed to describe the dispersion of the metal drops from the interface.This approach is based on the following representation of the process:the interface is assumed to be‘‘stable in the mean’’,and,from it,finely dispersed liquid–metal particles income into the gas cavity.The sizes of the particles and the inten-sity of their income are determined by the velocity characteristics of the gas stream moving along the tangent line to the interface[14], as well as by the physical properties of the two phases[21].To determine these parameters,we consider the interaction process between the phases as a result of theflow around a curvilinear impermeable surface covered by a liquid,evaporating metalfilm by the gasflow.Thefilm can be defragmented into drops owing to Taylor instability,and its behavior,as the experiments[10,11] show,turns out to be unstable in the case when the rate of the transverse mass blowing from thefilm surface becomes comparable with the mass-flow rate of the gas phase,the gas-phase velocity being directed along the a parameter that characterizes the onset offilm defragmentation,the results of[11]were used:tangent line to the‘‘averaged’’interface.As a parameter that characterizes the onset offilm defragmen-tation,the results of[11]were used:q00q1=2U00lr Ar1=3ÃRe1=6>1:75;ð12ÞwhereArür3v2q3j~g;~s jð1Àq00=qÞ1=2;Re¼u00dv:Here U00is the modulus of the velocity vector of the gas which flows over the surface outside the boundary layer,q00is the gas density,q is thefluid density,r is the surface-tension coefficient of the liquidfilm,v is the kinematics viscosity of thefluid,ð~g;~sÞis the projection of the free-fall acceleration vector on the tangent to the surface,and d is thefilm thickness.From the experimental dependences,we found the following relation between the drop size and the gas-stream dynamic pressure:U002q00D0r P10;ð13Þwhere D0is the diameter of a drop at the moment the drop leaves the surface.Therefore,from expressions12and13,it is possible to determine the position of the onset of thefluidfilm fragmenta-tion and the sizes of drops originating from this process.To determine the mass income rate of the metal particles,prac-tical recommendations of[1]were used,according to which the mass income rate of the drops G p is3–5times higher than the mass-flow rate of oxygen passing through the lance.By virtue of the above,the following parametric dependence was used in the calculations:G p¼a G O2;ð14Þwhere the parameter a was assumed to run through a set of its val-ues,and the magnitudes of all parameters of the particles at the moving boundary AE were determined from the above relations. At the boundaries KF and BC,the conditions of the absence of parti-cles were used,and at the boundary CD‘‘soft’’boundary conditions were set[24,27].The numerical solution of the problem under study was found using the ELA–FINT method–a combined Eulerian–Lagrangian method for calculating incompressibleflows with moving bound-aries[13],which was modified to cover the case of incompressible flows[22].The smoothing procedure based on b-splines was also used,in order to determine‘‘the average in the mean’’interface.osevic et al./International Journal of Heat and Mass Transfer54(2011)4275–427942774.Some calculation results and discussionThe numerical modeling of the top blow was carried out to reproduce its initial stage,for which the carbon concentration in the metal is maximum.The Mach numbers of the central and the side jet,M 0and M 1were assumed to equal 1.2and 0.5,respec-tively.The sloping angle of the side jet to the lance was 45°.Three values of the parameter a in (14)were tried:a =2,4and 6.Fig.3shows the structure of the flow pattern in the form of the velocity vectors and Mach number contours in the cavern forming as a result of the interaction between the oxygen jet and the liquid-metal surface for (solid curve).For the two other values of this parameter,it is only the shape of the cavern that substantially changes (see the dashed curves in Fig.3b which correspond to a =4and 6),while the flow structure in the cavern does not change noticeably.As follows from Fig.3,the deceleration of the super-sonic jet occurs near the metal surface without its penetration into the cavern.Owing to the high difference between the jet and metal temperatures,the temperature and density of the gas phase in the cavern is essentially nonuniform,which results in the occurrence near the interface of the region with high gas velocities,which near the exit section of the cavern can exceed the sound velocity [19,24].Fig.4a s hows the profiles of particle concentration in various cross-sections of the cavern.The interaction of particles with the gas-carrier is seen to increase the particle concentration near the interface.This inhomogeneity results in that the main fraction of CO is released near the cavern surface,the main consumption of oxygen incoming into this zone due to both convective and diffu-sional transfer in reaction (2)taking place right here.However,this transfer,as follows from Fig.4b does not guarantee a sufficiently complete CO 2combustion,and its considerable fraction is carried away out of the cavern.Practically zero CO concentration near the jet axis is related to that here,owing to the low temperature and the lack of steam not reaching the oxygen-jet axis,reaction (2)does not proceed.An increase in the steam concentration by one order of magnitude leads to a profound growth of the com-pleteness of the CO combustion (dashed curves in Fig.4b).There-fore,taking measures aimed at intensifying oxygen transfer to the interface and increasing the steam concentration in the caverncan be considered as practical recommendations for increasing the effectiveness of the CO after burning process.5.ConclusionFrom the results obtained,the following conclusions can be drawn:1.A numerical algorithm for modeling the top burn of a steel-making converter is devised.This algorithm takes into account the CO after burning process in its accurate formulation.2.The structure of the two-phase flow in the cavern with allow-ance for the occurrence of chemical reactions in it is studied.3.Based on the analysis of the flow structure and some peculiar-ities of the CO after burning process,several practical recom-mendations are given for enhancing the effectiveness of the process.AcknowledgmentThis work was supported by the Ministry of Science and Tech-nological Development of Serbia,Grant No.TR35030.References[1]V.I.Baptizmanskii,V.B.Okhotskii,Physicochemical Foundations of the Oxygen-Converter Process,Vyshcha 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