Functional modeling meets meta-CASE tools for software evolution

1) A web service is a standard approach to making a reusable component available and accessible across the web2)activity-location matrix:Rows are system activities from event table、Columns are physical locations3)Alpha version – incomplete testing version4)Ambiguous requirements interpreted in different ways by developers and users.5)Ambiguous requirements interpreted in different ways by developers and users.6)Beta version – end-user testing version7)CASE’s fullname is Computer-Aided Software Engineering.8)Client/Server Architecture Advantage – deployment flexibility9)Client/Server Architecture Disadvantage – complexity10)Client/server divides programs into two types:Server、Client.11)Cohesion – qualitative measure of consistency of functions within a single class12)Controls are needed for all other design activities:User interface、System interface、Application architecture、Database and Network design.13)CORBA is an international standard for an Object Request Broker - middleware to manage communications between distributed objects.14)Coupling – qualitative measure of how closely classes in a design class diagram are linked15)CRUD – create, read, update, and delete16)Data dictionary is repository for definitions of data flows, data stores, and data elements17)Data flow diagrams (DFDs) are decomposed into additional diagrams to provide multiple levels of detail18)Deployment environment definition bridges analysis and design:Hardware、System software、Networking19)Design class diagram specifically defines software classes20)Design is process of describing, organizing, and structuring system components at architectural design level and detailed design level21)Design process activities include Architectural design、Abstract specification、Interface design、Component design、Data structure design、Algorithm design 22)Domain model class diagram shows conceptual classes in users’ work environment23)Early increments act as a prototype to help elicit requirements for later increments.24)Engineers should be aware of local laws governing the use of intellectual property such as patents, copyright, etc.25)Examples of process perspectives are Workflow perspective、Data-flow perspective and Role/action perspective26)First-cut design class diagram is based on domain model and system design principles27)Functional user requirements may be high-level statements of what the system should do but functional system requirements should describe the system services in detail.28)Generic activities in all software processes are Specification、Development、Validation and Evolution29)Generic process models are Waterfall、Iterative development andComponent-based software engineering30)Important part of testing is specifying test cases and test data31)In principle, requirements should be both complete and consistent.32)In principle, requirements should be both complete and consistent.33)Integration Testing tests the behavior of a group of modules or methods34)Internet – global collection of networks that use TCP/IP networking protocols35)Layered application architecture:Presentation layer、Application processing layer、Data management layer36)Non-functional classifications are Product requirements、Organisational requirements and External requirements.37)Object contains program logic and necessary attributes in a single unit38)Object-oriented design is process by which detailed object-oriented models are built39)Objects send each other messages and collaborate to support functions of main program40)Programmers carry out some program testing to discover faults in the program and remove these faults in the debugging process.41)Requirements engineering process include Feasibility study、Requirements elicitation and analysis、Requirements specification、Requirements validation42)Sequence diagrams show the sequence of events that take place during some user interaction with a system.43)Software engineers must behave in an honest and ethically responsible way if they are to be respected as professionals.44)Some Fundamental Design Principles：Encapsulation、Object reuse、Information hiding、Protection from variations、Indirection45)Sometimes DFD fragments need to be diagrammed in more detail46)Spiral model sectors include Objective setting、Risk assessment and reduction、Development and validation、Planning。

DEM-CFD modeling of solid-fluid flowsPaul Golz and John FavierDEM Solutions Ltd, 20 York Place, Edinburgh, EH1 3EP, UKCorresponding Author: jfavier@Effective modeling of solid-fluid flow requires methods for adequately characterizing the discrete nature of the solid phase and representing the interaction between solids and fluids. CFD multi-phase models such as the Euler-Lagrange Method and the Euler-Granular method address the problem within a continuum framework. In continuum models, contact between fluid, particles and boundary surfaces is not considered explicitly with respect to particle inertial and mechanical prop-erties. This can limit the ability of CFD multiphase models to adequately represent particle-particle and fluid-particle interactions and therefore can reduce the accuracy of the prediction of both the fluid and the particle dynamics. This limitation can be overcome by explicit calculation of the particle contact mechanics in a particle-scale reference frame using a Lagrangian approach such as the Discrete Element Method (DEM). Coupling of DEM and CFD provides a means of momentum and energy exchange between solids and fluid, which, in principle, removes the need for some of the semi-empirical approximations employed in CFD solid-fluid models.Coupled DEM-CFD simulation has potentially a very wide range of application and is at-tracting increasing interest from industry. DEM-CFD models reported in the literature have largely been applied to simulation of fluidised beds and more recently to pneumatic transport of particles. A feature of most of these models is the simple boundary surface geometry and use of mapped, usu-ally Cartesian, meshes. Many industrial processes involved complex geometry, often with moving parts, and complex flow dynamics. Simulation of such systems requires use of unstructured fluid meshes, and the ability to handle energy as well as momentum exchange, turbulent flow, and chemical reactions. This capability is now possible in a commercial environment using co-simulation of EDEM discrete element modeling software with FLUENT. EDEM is an advanced particle me-chanics simulation tool employed for modeling of industrial particulate handling and processing op-erations. It uses a surface mesh to represent boundary surfaces which enables a one-to-one cou-pling with the boundary surface elements of the CFD fluid volume mesh. EDEM-FLUENT co-simulation is being used to investigate systems such as particle agglomeration and clumping in flu-idised beds, dense phase conveying, filtration, solid-liquid mixing, pipe erosion, spray coating and many others. Here we show some applications and assess the opportunities and limitations for ap-plication of DEM-CFD simulation of industrial solid-fluid system.I. INTRODUCTIONThere is considerable industrial interest in simulation of particle-fluid systems such as pneumatic conveying, fluidized beds, spray drying, filtration and slurry transport among many applications. The most commonly employed methodologies are those implemented within a Computational Fluid Dy-namics (CFD) framework. An Euler-Lagrange approach is employed for dilute systems (particle solid fraction <10%) where the particles are considered as point masses which can exchange mo-mentum with the fluid. Individual particles are tracked within the fluid, and their trajectories are com-puted using a simple force balance equation. Particle shape and mechanical properties and inter-particle collisions are ignored and simple rebound models are used for particle-surface collisions. At higher particle solid fraction particle collisions become significant and the solid fraction is then treated as another interpenetrating continuum phase. This approach, called the Euler-Granular method, treats the granular phase statistically to estimate the stress tensor and void fraction. Through addition of conservation equations mass, energy and momentum exchange between the fluid and solid phases can be simulated. The CFD multi-phase approximations of particle-fluid flows ignore individual particle inertial and mechanical properties and local particle-particle interactions. This limits its ability to capture the evolution of non-uniform particle concentrations and properly ac-count for particle-boundary surface interactions.The discrete element method (DEM) is a Lagrangian solution for granular flows which ex-plicitly calculates individual particle dynamics. It is employed for modeling granular behavior in a wide range of applications including conveyor transport, hopper discharge, soil-tool interactions, particle attrition, mixing and many more. On its own DEM does not account for fluid-particle interac-tions other than by addition of an arbitrary viscous drag force. Consideration of the fluid flow field requires coupling of the particle dynamics with fluid dynamics calculations. The simplest coupling is a one-way coupling where the drag force on the particles is calculated based on local fluid condi-tions in a converged fluid flow field. This type of coupling is suitable if the momentum transfer from fluid to particles is small relative to the local fluid momentum. It has the advantage computational speed, and is applicable to strong flows with low particle loading. Full coupling between the phases takes in to account momentum exchange, and mass and energy exchange if necessary. The ap-proach commonly employed is to couple the DEM with the Navier-Stokes equations using momen-tum source terms. This type of coupling has previously been limited to simple geometries discre-tised using Cartesian meshes. The approach has been successfully used to model fluidized and spouting beds (e.g. Kuipers et al, 1991, Tsuji et al, 1993).Here we present a methodology for integrating DEM with CFD using two commercially-available software programs to provide a practical and flexible solution for simulating a wide range of common particle-fluid flow processes. The coupling is between EDEM (DEM Solutions Ltd) and FLUENT (Fluent Inc.). EDEM is a Discrete Element modeling software tool which provides a plat-form for integrating particle, fluid and machine dynamics. It has a fully functional graphical user in-terface which enables fast set-up, solution and analysis of a DEM model including non-spherical particles and complex geometries based on CAD models. It has been extensively experimentally validated for applications within the agriculture, pharmaceutical, mining and process industries. EDEM’s applications programming interface enables implementation of any desired contact condi-tions (e.g. cohesion or electrostatics) and coupling with external programs such as CFD or finite element analysis. FLUENT is the world’s leading CFD software program. It contains functionality allowing external programs to adjust and modify its single and multi-phase algorithms as well as providing a framework for coupled software development.II. THEORY: COUPLING METHODHere we present two methods of coupling: Lagrangian and Eulerian. The Lagrangian coupling al-lows only for momentum exchange between the fluid and the solid phases. As well as momentum exchange, the Eulerian coupling also accounts for the volume fraction of the particles. Lagrangian coupling is considerably less compute intensive than the Eulerian coupling but is only valid wherethe local solid volume fraction, i.e. the solid fraction within a fluid control volume, remains below 10%.The Lagrangian coupling scheme is shown in Fig.1. The CFD is performed as a singlephase, transient calculation which is iterated to convergence for a time step. A drag force is then calculated on the DEM particles using the fluid velocity in the mesh cell within which each particle is located. The DEM then takes control of the simulation and performs one or more iterations. After the DEM finishes iterating, control is passed back to the CFD. A momentum sink is added to each of the mesh cells to represent the effect of energy transfer to the DEM particles.Figure 1: Schematic of Lagrangian Coupling SchemeThe magnitude of the momentum sink, P , of the CFD mesh cell can be calculated fromwhere F is the force on a particle in a particular iteration from the fluid. The sum is over the number of DEM iterations carried out between each CFD iteration. This momentum transfer is typically un-der-relaxed on the CFD side to provide greater stability of the CFD solution.where χ is the under-relaxation factor.The Eulerian coupling model is based on FLUENT’s existing Euler-Granular model. The solver is run with two interpenetrating phases and additional sets of continuity equations must be introduced to account for the interaction of these phases. The individual phase continuity equations must also be modified to account for the volume fraction, α, of the individual phases. For this form of coupling,energy is not transferred between the phases meaning that only mass and momentum need to be conserved. Consider the mass continuity equation for the fluid phase:where the subscript F refers to the fluid and P to the particulate phase. v F is the velocity of the fluid phase. Since time is discretised, the change in the particulate mass in any grid cell is given by:where τ is the CFD time-step. A similar equation exists for the conservation of momentum:P =VFparticles /DEM iterations /P new =|P calculated +1-|^h P old2t 2(a F t F )+d $(a F t F v F )=2t2(m P )2t2m P (t )-m p (t )-m p (t -x )The subscript F refers to the fluid phase, τ F is the stress tensor and p is the pressure shared by all phases. P p is the momentum gained by the solid phase defined previously. The continuity equation for the solid phase is not solved by the CFD solver, nor are any phasic interactions considered ex-cept those specified here.In order to obtain a realistic momentum transfer for particles it is necessary to calculate the volume of the CFD mesh cell occupied by the solid fraction, i.e. m p (t) above. Regular sample points are taken within the bounding box of a particle and retained if the point lies within the bounding surface of the particle.Figure 2: Sample points within a particleEach point is checked to determine which CFD mesh cell it lies within. The mass within a particular mesh cell is therefore given by:where n c is the number of sample points contained within the mesh cell of particle p and N the total number of sample points of the particle. ρP is the particle’s density and V p the volume.III.THEORY: DRAG FORCEThe fluid drag force on each particle is calculated used a modified spherical, free-stream drag model. For non-spherical particles, the drag force is calculated based on a bounding sphere. All fluid parameters are taken from the CFD mesh element which contains the centre of the DEM parti-cle. The drag coefficient, c D , depends on the Reynolds number, Re:where ρ is the fluid density, η the fluid viscosity, L the diameter of the particle’s bounding sphere,and v the relative velocity between the fluid and the particle. α is the free volume of the CFD mesh cell.Free stream drag (F d ) for a sphere is calculated according to:A is the projected area of the particle. This drag equation can be modifiedwhere Vis the particle volume and2t 2(a F t F v F )+d $(a F t F v F v F )=-a F d p +d $x F +a F t F g -2t2P p m p (t )=N t p n c V p particles/Re =hat L v c D =Re 24240.441.0+0.15Re 0.687]g Z [\]]]]/Re Re #0.50.51Re #1000Re >1000F d =0.5c D t A v vF d =1-ab V v vIn addition to the drag force, the particles natural buoyancy must also be taken into account.Typically for Lagrangian coupling the modified drag law would be used to account for particle load-ing effects, whereas in the Eulerian model the effect of particle loading is already taken into account through the coupling of volume fraction. The approach to modeling fluid drag in a coupled DEM-CFD model taken here follows in part the treatment developed by Tsuji and co-workers at Osaka University and Kuipers and co-workers at University of Twente.IV. APPLICATIONSHere we illustrate the application of EDEM-FLUENT co-simulation with some simple models. A sim-ple fluidised bed is shown in Fig 2. The model comprises 1000 spherical particles being fluidized using a central jet of air.Figure 3Simple fluidised bed. Left: DEM results, and, right, a snapshot of solid volume frac-tion at same time-step taken from the CFD data. For this simulation a full Euleriancoupling is necessary.Another good test of the coupled solution is entrainment and deposition. The entrainment of sand particles by a jet of water passing over a packed bed of sand was modeled. Using an Eulerian method, the EDEM-FLUENT co-simulation correctly predicts both particle pick-up and transport,and also the circulation of the fluid as a result of the particles. Figure 4 (left) shows a vortex forming directly above the bed as a result of the presence of the growing wall of particles to the right of the bed. On the same figure (right) the circulation at the exit of the flow pipe is able to penetrate the packed bed only slightly.b =eL 2150(1-e )2h +L 1.75(1-e )t v 43c D t e -1.65(1-e )v Z [\]]]]z <0.8z $0.8F B =-t gVFigure 4: Entrainment of sand particles by a jet of water.Another area of application for coupled DEM-CFD simulation is filtration and separation. In this ex-ample large particles are prevented from traveling down a pipe by a filter (Figure 5 left). As the Eule-rian coupling excludes fluid volume from mesh cells, the particles can block certain holes in the filter resulting in high pressure build up in the pipe (Figure 5 right).Figure 5Blocking of a filter. Left: the lower port is blocked by the larger particles resulting in preferential flow through the upper holes. Right: the larger particles (green) cannotpass through the hole to the far side. The blockage of the holes results in an in-creasing pressure build up on the filter.DEM particles can be used to mimic bubbles. Figure 6 shows the aeration of viscous fluid. Due to the low loading and speeds, amalgamation of bubbles is unlikely however by modifying the contact law within EDEM, soft impacts can be studied. In this model the optimum dispersion speed was studied to allow for maximum aeration time.Figure 6Aeration of viscous liquid. This problem is ideal for the Lagrangian coupling as the local loading of particles remains low. Streamlines of the fluid are shown and theseshow a slow down near the bubble source. This is due to momentum transfer fromthe fluid to the bubbles which is seen to occur over a short distance.The Eulerian model in most CFD packages assumes that the particle dynamics are similar to ideal gas behavior. This approximation can not account for the nature of the particle interaction forces. In the example shown in Fig 7, cohesive forces between particles and adhesion between particles and pipe walls are introduced which result in clumping and significantly non-ideal behavior. Figure 7 (right) shows chains of particles forming. This microscopic behavior cannot be captured by tradi-tional two phase CFD techniques.Figure 7Flow around a pipe bend of cohesive particles in a fluid. Particles hold-up on the wall (left: blue is slow, red is fast) and start to clump (right - highlighted). The clumpsgrow and then detach and are carried further down the pipe by the fluid.V. CONCLUSIONSHere we present a coupled DEM-CFD solution based on co-simulation of EDEM with FLUENT. This coupling provides a means of modeling particle-fluid flows in complex geometries and can accountfor particle shape and mechanical properties as well as inter-particle and particle-wall interactions. The coupling can be implemented in two ways: Lagrangian for solutions when the solid fraction is low, or Eulerian for solutions when the solid fraction is high. Since the coupling works directly with existing CFD functionality is can be applied to a range of simulation conditions including moving meshes and periodic boundary conditions. EDEM can also be coupled with other CFD models such as volume-of-fluid as well electromagnetic models.Industrial applications of this coupling include filtration, slurry flow, aeration, deposition and entrainment. This methodology is also expected to be useful in applications such as ball mills, cy-clones, drying, spray coating, jet-milling and particle inhalation devices. In order to further increase the range of industrial applications, ongoing development is incorporating rotational forces (e.g. Saffman, Magnus forces), irregularly shaped particles, heat transfer, turbulent scattering, incorpora-tion of a free surface and mass transfer between the solid and fluid phase. This will enable simula-tion of such processes as crystallisation and chemical reactions.VI. NOTESThe examples described in this paper were produced using co-simulation of EDEM 1.1 (DEM Solu-tions Ltd) and FLUENT 6.2.16 (Fluent Inc.) using an EDEM-FLUENT coupling module available from DEM Solutions Ltd. For further information visit .VII. REFERENCESKuipers, J.A.M, Prins, W. and van Swaaij, W.P.M., Chem Eng Sci (1991) 46, 2881-2894Tsuji, Y., Kawaguchi, T. and Tanaka, T., Powder Technology (1993) 77, 79-83。

geomagic studioGeomagic Studio: Revolutionary Software for 3D Design and ModelingIntroductionIn the ever-evolving world of technology, 3D design and modeling have become crucial techniques for various industries such as manufacturing, architecture, healthcare, entertainment, and more. To meet the increasing demand for precise and efficient 3D design solutions, Geomagic Studio has emerged as a revolutionary software that offers powerful tools and features for creating, optimizing, and analyzing 3D models. This comprehensive document aims to provide an in-depth exploration of Geomagic Studio, highlighting its key features, applications, advantages, and future prospects.I. Overview of Geomagic StudioGeomagic Studio is a professional-grade software developed by Geomagic, a leader in 3D software solutions. 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This feature is particularly beneficial in industries such as automotive, aerospace, and manufacturing, where the creation of accurate digital replicas can expedite the design and production processes.5. Inspection and Quality ControlGeomagic Studio offers powerful inspection and quality control tools that allow users to compare 3D scan data withthe original CAD designs. This feature is indispensable for identifying any discrepancies or defects that may have occurred during manufacturing or assembly. The software facilitates precise measurements, surface analysis, and deviation mapping, ensuring the final product meets the desired specifications.III. ApplicationsGeomagic Studio finds extensive applications in numerous industries, revolutionizing the way 3D design and modeling are carried out. Some notable areas of application include:1. Manufacturing and Engineering: Geomagic Studio enables manufacturers to create accurate digital replicas of physical objects, facilitating product design, prototyping, and quality control. Its reverse engineering capabilities allow for easy reproduction of complex parts and components.2. Architecture and Construction: Architects and designers can utilize Geomagic Studio to convert 3D scanned data of existing structures into digital models. This enables them to visualize and simulate renovations, remodelings, and architectural changes.3. Healthcare and Prosthetics: Geomagic Studio plays a crucial role in the field of healthcare by allowing for the creation of customized 3D models for surgical planning and prosthesis design. The software's precise measurement tools ensure the perfect fit and functionality of prosthetic devices.4. Entertainment and Animation: Geomagic Studio's powerful 3D modeling capabilities are widely used in the entertainment industry. From creating lifelike characters and animations to designing complex objects and environments, the software provides artists and animators with immense flexibility and control.IV. Advantages of Geomagic Studio1. Ease of Use: Geomagic Studio boasts an intuitive user interface that makes it accessible to both professionals and beginners. The software's simple and interactive workflow ensures a smooth learning curve.2. Time and Cost Efficiency: By automating complex processes such as point cloud processing, mesh creation, and surface reconstruction, Geomagic Studio significantly reducesthe time and effort required for 3D design and model generation. This leads to enhanced productivity and cost savings.3. Precise and Accurate Results: Geomagic Studio's advanced algorithms and tools ensure the creation of highly accurate and detailed 3D models. The software's inspection and quality control features enable professionals to validate and verify the integrity of their designs.4. Seamless Integration: Geomagic Studio seamlessly integrates with other software applications like Computer-Aided Design (CAD) systems, Computer-Aided Manufacturing (CAM) systems, and 3D printing software, ensuring a streamlined and efficient design workflow.V. Future Prospects and ConclusionAs technology continues to evolve, Geomagic Studio is expected to evolve with it, pushing the boundaries of 3D design and modeling further. Its potential for integration with emerging technologies like virtual reality and augmented reality holds immense promise for the future. With its innovative features, ease of use, and widespread applications,Geomagic Studio has undoubtedly become a game-changer in the field of 3D design and modeling.。

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