Visual Dynamic Simulation and Optimization of Zhangjiuhe Diversion Project
基于Simulink的比例阀控液压缸的建模与仿真
基于Simulink的比例阀控液压缸的建模与仿真张兵;邓子龙【摘要】By improving the hydraulic system of the clamp experiment table to make it a closed-loop control system, and taking the system as the research object, the mathematical model and simulink simulation model are established. The dynamic performance in time and frequency domains is analyzed. Aiming at the improvement of the dynamic performance ofthe system, the damping ratioδh , the hydraulic cylinder frequency wh ,and the open loop gain Kc , of the close-loop control ing system, and their effect on the dy-namic performance of the system are analyzed by using MATLAB so as to provide reference for the design, calibration and optimiza-tion of the hydraulic system.%通过对夹具实验台液压系统进行改进，使其成为闭环控制系统并以此系统为研究对象，建立数学模型和Simulink仿真模型，分析了系统在时域和频域的动态性能。
从提高系统动态特性的角度出发，应用MATLAB分析了液压缸频率wh、阻尼比δh 和闭环控制系统开环增益Kc 对系统动态性能的影响，从而为液压系统的设计、校正、优化提供借鉴。
Autodesk Plant Solutions AutoCAD Plant 3D性能优化指南说明书
Autodesk Plant Solutions WhitepaperAutoCAD Plant 3D Performance – Maximizing SpeedIntroductionAutoCAD Plant 3D is built on AutoCAD, a general purpose cad engine as its platform. AutoCAD is used for many things and is not solely optimized for 3D graphics. AutoCAD Plant 3D default settings are not set up specifically to favor editor response speed over display quality.When looking to improve performance, the first thing to consider is using the Visual Styles Manager to reduce 3D display detail. You can also use 3dconfig to set options that affect graphics card hardware performance.This document presents options that go beyond tuning and identifies settings and strategies that can have an impact on editing behavior and model appearance.Performance increases can be made by turning off disconnect markers, breaking up large drawings using external references, and operating in 2dwireframe mode. AutoCAD Plant 3D specific performance settings are described in this document, as well as some AutoCAD performance settings.∙AutoCAD Plant 3D Settings∙AutoCAD Settings∙Large Drawing Management∙Sample SettingsAutoCAD Plant 3D SettingsYou can set system variables and use commands to improve display performance.Setting Value for maximum performanceplantconnectionmarker Off Hides disconnect markersplantinsulationdisplay Off Do not display insulation on pipingplantinsulationmode Partial Hides insulation on valves and fittingsplantlockfadectl Off Ignores locked piping setting for display plantpipesilhdisplay Off Turns off pipe silhouettesplantplaceholderdisplay Off Turns off markers (yellow !) for placeholder parts plantpropmismatchdisplay Off Hides property mismatch markersplantsavedetail Off Reduces drawing file sizeplantwelddisplay Off Hides connection markersplantsteelsetrep Line Displays structure using a line modelFor more information, see Maximize Plant 3D Display Performance in the User’s Guide, and System Variables in the Command Reference.You can close AutoCAD Plant 3D palettes that you are not using when working in the model. For example, closing the Data Manager can improve performance, especially when editing. You can also close Project Manager to improve performance. For best results close the palette, do not Auto-Hide.AutoCAD SettingsThere is no single best AutoCAD setting for performance, because some settings are designed to disable features that you need to select and edit. In general, the settings identified here are intended to maximize browsing speed.The most significant choice is between 2D and 3D view modes. 2D modes can display 3D objects that look similar to 3D wireframe, but the modes use a significantly different redraw database. The 3D redraw database is designed for quality and response, but not scalability. In practical terms, this means that in 3D view modes, performance deteriorates faster as the model in the display gets larger. Changing to 2D wireframe with silhouettes off can significantly improve performance.dispsilh=0, isolines=4 dispsilh=1, Isolines=0If you turn off dispsilh to improve performance in wireframe, you should set isolines to 4. Piping also uses plantpipesilhdisplay, which displays silhouettes for tube segments when dispsilh is off.For more information, see Control the Visual Style of the Plant 3D Model.Below are some of the AutoCAD system variables you can use to improve performance.AutoCAD Setting Value for maximum performancedispsilh Off Do not display silhouette edgesisolines 4 The number of contour lines per surface on objectspickfirst Off Objects are selected after the commandviewres 1 Sets the smoothness of curved objects in a 2D view savetime Zero (0) Turn off autosavevscurrent2Dwireframe Sets the fastest display modevtenable 0 Turns off smooth view transitions customerinvolvementprogram Off Turns off activity loggingIt is important to understand the impact of these settings and consider that they could cause undesired behavior. For example, turning off pickfirst speeds up browsing, but it disables the select connected parts shortcut menu. Dragging in 2dwireframe can be slower than 3dwireframe. The best performance configuration depends on how you are working with the model. Setting a low viewres speeds up dragging in 2D mode. Facetres is a similar variable that affects 3D views.You can use vtoptions to set a fast transition speed, lower the performance (fps) threshold, or disable view transitions.Other settings that are not specific to display can be used. For example, you can freeze unused layers to improve performance, instead of turning a layer off.You can also turn off features that you do not use. For example, if you do not need the properties window open, consider closing it until needed. Tracking, dynamic input, and even grips can be turned off when not used.Large Drawing ManagementBreaking up larger drawings into smaller ones using external references can significantly improve performance. For example, you can place equipment, piping, and structure into different drawings and break up large drawings by area.Sample SettingsYou can copy and paste the table below into the Command window, or create and run a LSP file. Values are also provided to restore default settings.Performance settings:(command "plantconnectionmarker" "0")(command "plantinsulationdisplay" "0")(command "plantinsulationmode" "p")(setvar "plantpipesilhdisplay" 0)(setvar "plantpropmismatchdisplay" 0)(command "plantplaceholderdisplay" "0")(setvar "plantwelddisplay" 0)(setvar "plantlockfadectl" 0)(setvar "plantsavedetail" 0)(command "plantsteelsetrep" "L")(setvar "dispsilh" 0)(setvar "isolines" 4)(setvar "pickfirst" 0)(command "viewres" "y" 1)(setvar "savetime" 0)(command "vscurrent" "2dwireframe")(setvar "vtenable" 0)(if (/= (getvar "cipmode") 0) (prompt "\nRun customerinvolvementprogram to disable.")) Default settings:(command "plantconnectionmarker" "1")(command "plantinsulationdisplay" "1")(command "plantinsulationmode" "f")(setvar "plantpipesilhdisplay" 1)(command "plantplaceholderdisplay" "1")(setvar "plantsavedetail" 1)(setvar "dispsilh" 1)(setvar "isolines" 0)(setvar "pickfirst" 1)(command "viewres" "y" 1000)(setvar "savetime" 10)(setvar "vtenable" 3)(command "vscurrent" "realistic")Autodesk, Inc.111 McInnis ParkwaySan Rafael, CA 94903USAAutodesk [and other products] are either registered trademarks or trademarks of Autodesk, Inc., in the USA and other countries. All other brand names, product names, or trademarks belong to their respective holders.© 2010 Autodesk, Inc. All rights reserved.。
On the Semantics of Interactive Visualizations
On the Semantics of Interactive VisualizationsMei C. Chuah, Steven F. RothSchool of Computer ScienceCarnegie Mellon UniversityPittsburgh, PA, 15 213, USATel: 1-412-268-2145E-mail: {mei+, roth}@AbstractInteractive techniques are powerful tools for manipulating visualizations to analyze, communicate and acquire information. This is especially true for large data sets or complex 3D visualizations. Although many new types of interaction have been introduced recently, very little work has been done on understanding what their components are, how they are related and how they can be combined. This paper begins to address these issues with a framework for classifying interactive visualizations. Our goal is a framework that will enable us to develop toolkits for assembling visualization interfaces both interactively and automatically.Keywords: Information visualization, interactive techniques, user interfaces, automatic presentation systems, graphics.1. IntroductionIn the last few years many interactive techniques and metaphors have been introduced for different visualization types. These techniques allow users to deform space[6], deform objects[1], view objects at different levels of abstraction[3] and a wide variety of other functions. Although interactive techniques facilitate the use of visualizations, they have been implemented and combined as point solutions to focused problems. No unifying framework exists that describes the structure of individual techniques and how they can be combined. As a result it is difficult to extract the critical functionality contributed by different techniques in a system, making it difficult to apply its design features in other systems. We propose a framework for decomposing visualization system user interfaces into primitive interactive components, describe a functional classification of the different primitives, and present rules for composing and structuring these primitives. The goal of the framework is to enable us to compare systems and reuse previous design elements, as well as to guide our design of a toolkit for constructing visualization interfaces through the composition of interaction primitives. This infrastructure is the first step towards developing an automatic presentation system that can generate interactive visualizations.To begin our characterization of interactive visualization techniques, we expand on previous work done on user interfaces[2,5,7]. Foley et al. [2] described three levels of design for interfaces: lexical design, syntactic design and semantic design. Lexical design refers to how input and output primitives are derived from basic hardware functions. Input primitives include all physical devices while output primitives could take the form of visual, audio or sensory elements. In this paper we are only concerned with visual output. Syntactic design consists of a set of rules by which primitive input or output units can be composed or joined to form ordered sequences of inputs and outputs. For example, the mouse movements, clicks and releases required to specify a bounding box together define a focus area in an interface but do not define its meaning (i.e. its function). Semantic design defines the meaning of a sequence of actions as a task. For example, mouse clicks, bounding boxes and sliders can all be used to define a selection of objects. In this case the meaning is the selection task,while the actions used to achieve the task could take multiple syntactic forms. Similarly, an action can have several meanings. For example a bounding box can be used for selection, aggregation, deletion, copy, or other functions.Figure 1 shows the three levels of design described above. The gray circles indicate the components added to Foley et al.’s framework in order to deal with interactive information visualizations. This paper focuses only on the semantic level. There is much previous work on the lexical and syntactic levels [2,5,7] and we can incorporate it into our framework.The semantic primitive in our framework is the basic visualization interaction (BVI). Understanding a BVI requires knowing its inputs (whether provided by a user through an input method or fixed by the interface designer). It also requires understanding its effects on the graphical, data, and control states of the visualization user interface environment. Our framework also characterizes the composition of BVIs into application interface functions that we normally see in visualization systems such as aggregation, filtering, sort, and coordinated displays. Most visualization applications consist of combinations of multiple BVIs for one or more visualizations. For example, in the Table Lens[8], users perform multiple tasks on a table visualization. Users manipulate a lens to focus attention and sort table rows with gestures. These provide magnify and sort tasks. The magnify task can be decomposed into a set-creation BVI to define the graphical rows to be magnified and two set-graphical-value BVIs toincrease the size of the focus rows and decrease the context rows.Other systems provide interface operations for multiple coordinated visualizations. For example, in the SAGE system[9], painting, dynamic query and aggregation operations can affect multiple active visualizations simultaneously. Each of these operations are compositions of multiple BVIs, which we illustrate in the next sections.User I nput Metho dFigure 1: Expanded interface architecturefor visualization interaction In this paper:〈 We propose a set of basic visualization interactionprimitives and describe their inputs and effects on the state of a visualization system. This specifies the flows into and out of sequences of BVIs. We will give some examples of the relation between BVIs and the physical and virtual devices that can be linked to them to provide inputs.〈 We propose a preliminary classification of BVIs as astarting point for understanding the kind of framework we believe is needed to understand their expressiveness and to use them to compare systems functionally. It also identifies the types of building blocks we might provide in an interaction toolkit. Once we extend this framework to include effectiveness and user task information we can determine which techniques or combination of techniques will best serve a given set of user tasks.〈 We describe issues involved in composing BVIs toform composite visualization position is a critical function in a toolkit and in automated design because it opens up the design spaceof possible interactive behaviors. By combining BVI’s,we can develop complex behaviors and effects.2. Real-estate exampleIn this section we present a simple interface for examining a data base of house sales. Figure 2 shows the locations of houses on a map and a dynamic query slider that controls their visibility based on asking-price .Selection occurs by clicking on houses or enclosing them with a bounding box. Selection can be performed in conjunction with filtering (dynamic query slider) to choose houses based on both their asking-price and their locations.For example, a user could make visible those houses costing more than $100K and select only those in the southern region using the bounding box (turning them red):Figure 2: Map display of house locationsFigure 3a: Bar chart showing days on themarket for subset of housesAgencyFigure 3b: Houses in Figure 3a aggregatedby Real Estate agency.After selecting the houses of interest, a user may want to know how quickly they were sold by creating a newgraphic displaying this information. The user drags the selected elements to an empty display with addresses and days-on-the-market on the axes, which causes the elements to be visualized as bars (Figure 3a). In order to compare the speed with which agencies sold these houses, the user then aggregates the houses by agency, replacing the individual houses with three agency averages (Figure 3b). Finally, an agency aggregate bar can be painted green to change the color of corresponding houses on the map (Figure 1). This would show regions where the painted agency sold its houses.The example illustrates basic operations commonly performed in data analysis: filtering, selection, aggregation, coordinated highlighting, and creating graphics. We also want to emphasize that while each of the functions achieve different effects, they also share similar component basic interactions. For example, several operations involved creating or changing elements of a set. These operations include filtering with a slider, selecting with a bounding box, dragging a group of objects into a new display, aggregating, and coordinated painting across displays. Each also involved changing the graphical properties of the elements. For example, filtering with the query slider changed visibility, painting changed color, aggregation deleted graphical objects of elements and added aggregate objects.Our goal, then, is to define a set of basic visualization interactions like create-set and set-graphical-value that underlie these application tasks. The next section describes BVIs, their inputs and outputs (effects).3. The basic visualization interaction (BVI)A BVI is fully described only when we specify the BVI inputs, outputs and operation. The number and type of inputs required depend on the BVI. Inputs can be set by a user via an interface (a user input method), or they can also be predefined by the designer (a default specification). A user input method consists of combinations of device inputs and basic and composite interaction tasks (refer to Figure 1).BVI inputs pass through a BVI operation which consists of two phases: condition check and action. The condition check determines whether all the required inputs and values are sufficient for the operation to occur. The proceeding action will then change the graphical, data or control state of the visualization interface. An action may also generate outputs that serve as inputs for other BVIs, thereby producing sequences that can potentially achieve complex tasks.3.1 Basic visualization interaction inputsThe inputs provide ways in which users or designers affect BVI behavior. The dynamic query slider in our real-estate example, has as inputs: a data attribute, an initial visible set (control object), two values and a focus set which includes all elements in the visualization. Changing these inputs will cause an express-membership BVI to calculate a new definition of the members of the initial visible set (i.e. the intention defined by the slider). There are five classes of input arguments. We describe each and briefly outline how they can be derived from both physical and virtual input devices based on the framework described in [7] as well as interaction tasks described in [2]. In the right of Figure 4, we show the input classes BVIs require. Attribute: There are three attribute types: graphic (e.g., color, shape, and x-position), data (e.g., house address, house price, and date-sold), and state (e.g., number of graphical elements). Attributes are chosen by users using the selection basic interaction task, defined in [2], as the task of choosing an element from a choice set. This can be achieved through naming or menus. Each of these techniques can be achieved through different input device classes. For example, naming can be achieved with a 1-dimensional discrete position device (e.g., keys) and menus require a device(s) that is capable of expressing a (2,3)-dimensional continuous position and a 1-dimensional discrete position (e.g., a mouse). More details on device expressiveness can be found in [7].Control Object: Control objects or reference objects may be a data object, a graphic object, a virtual device object (e.g., handle, slider) or a set object. Control objects are used to provide a point of reference for the BVI. For example, adding new members into a set requires a control object that defines the reference set into which we add members. In our dynamic query slider example, the visible set is the control object. Defining control objects is also done through the selection basic interaction task. In order to do this we could select the names of the objects, similar to the way in which attributes were selected. Alternatively, we could select the graphical representations of the reference objects. The latter can be achieved by the pointing technique. Pointing requires a (2,3)-dimensional continuous position device (e.g., mouse moves).Value: Based on the framework specified in [7], there can be two different classes of values: linear and rotary. This distinction is important for choosing input devices based on their effectiveness. For example, a dial input device is more effective for specifying rotary values than a slider. Linear inputs consist of position, movement, force, and delta-force. Rotary inputs consist of rotation, delta rotation, torque, and delta torque. Which type of value is needed depends on both the BVI and the feedback method. Values are defined through the quantify basic interaction task which specifies a value between some minimum and maximum. In the dynamic query slider example, two linear values are provided by the slider input device method.Formula: A formula defines relationships among multiple variables. In our framework it is used to specify an effect on specific variables based on the values of other variables. Some example formulas used in our framework include the object encoding formula which specifies which graphical representation to use for data objects based on their characteristics. Distortion formulas are another example.They specify how the positional parameters of an object or an area should be altered based on a cost of value function or according to a reference object. Formulas are usually predefined. However, they may also be specified by users. Focus Set: The focus set argument defines the set of entities on which a BVI operates. The focus can either be defined through user input methods, assigned to a predefined set, or output by another BVI. User input methods can either define single objects with a single (2,3)-dimension positional input device (e.g., mouse move), or an object group and area with multiple (2,3)-dimension positional values (e.g., bounding box). The dynamic query slider in our real-estate example has a predefined focus set containing all the objects on the map (Figure 1).A focus set may consist of two different entity types: objects or space. An object usually refers to the graphical representation of a particular data point (e.g. point, line, node, link, axis). A data object may be represented multiple times in several visualizations. An operation may affect the graphical representations of data objects or it may affect the data objects themselves. The other entity type is space. Space selection may be of type area or volume. Areas are defined for both two-dimensional and three-dimensional visualizations. Spaces and objects can be operated on singly, as groups or universally (i.e. on the entire visualization).It is important to distinguish between space and object type entities because the same operation, when applied to an object or a space, may result in very different effects. For example, magnification of an object will only cause the objects to grow while the surrounding areas remain unchanged. The advantage of this method is that the absolute position of objects remain constant. Area magnification causes not only the objects within the area to expand, but the space between the objects to expand as well. Unlike the previous case, object positions are no longer static. As the area is magnified, objects move farther apart. Hollands reported in [4] that this effect may be disorienting to the user. However, the advantage of area magnification is that no matter how large objects get they will not overlap, because the surrounding space expands proportionately. This property does not hold for object magnification. Such distinctions have significant implications for the effectiveness of a technique, and on the tasks it can support.3.2 Basic visualization interaction outputsBVI outputs may affect the graphical, data or control state of the system. Different BVIs have different output/effect choices. For example the set-graphical-value BVI may change any attribute of the graphical objects in the visualization. Its effect choices however are limited to graphical objects. The derive-attributes BVI on the other hand can only affect data objects. For a particular BVI, different output methods may require different inputs. For example, suppose we have a set-graphical-value BVI. If the output method is orientation of the focus set objects, then a rotation input value might be needed. If a positional change is desired, then a linear input value might be more appropriate. Furthermore, appropriateness of output is strongly dependent on user task. For example, displacing position brings out occluded objects , but displacing color does not.Graphical State: The graphical state refers to all objects that are currently visualized. This includes graphical objects as well as axes, labels, and keys. Changes in the graphical state can be made to objects (e.g. changing from marks to bars) or to attributes (e.g. color, transparency, visibility). There are two types of graphical attributes: spatial (e.g. size, shape, orientation and position) and surface properties (e.g. color, transparency, blinking, texture.In the real-estate example, coordinated painting involves coloring interesting objects green. This is specified by the designer through a default specification, which could be changed to blue if desired. Similarly, a designer may change the highlight attribute from color to visibility, so only interesting objects are visible. In the same way, a dynamic query slider can control size or color instead of visibility. Data State: The data state contains information about all of the data elements (e.g. house-1, house-2) that are currently in the system. This includes their attributes (e.g. asking-price, days-on-market) and attribute characterizations (e.g. cardinality, data-type). Changes in the data state occur as new data is read in, or as users delete and change existing data elements.Control State: The control state contains internal information generated during operation of the system. It includes four information types: virtual objects, global properties, interaction state and history. Virtual objects are abstractions created by the system during the course of interaction (e.g. set abstractions). Global properties describe general system state variables (e.g. visualizations currently open, current directory, access permissions). Interaction state refers to BVIs used in a system. This includes information about interaction constraints and associations between BVIs and visualizations. Finally history information includes traces of user activity and previous user errors.Information in the three states are needed to describe the function of BVIs. BVIs often query the system state before invoking their operations. For example, an interactive technique may use visibility for filtering when the total number of objects in the visualization is large and highlighting when the number of elements is small. Coordinated painting in our real estate example also queries the system state. When a user clicks on the agency aggregate bars, the control state is queried in order to retrieve all of the house names associated with the selectedaggregates. These house names are then used to constructthe highlighted set on the map.Note that changes to the control and data states do not provide any feedback to the user because their effects are internal. Changes to the graphical state may provide user feedback, simply because their outputs are very noticeable. The dynamic query slider in our example, changes object visibility, which gives sufficient user feedback. In other interactions, (e.g. the scaling operation in SDM [1]), large changes to the scale may sometimes only cause small changes to the selected objects, so feedback mechanisms must be included about the occurrence and effects of an action.4. BVI operation classificationThere are three BVI classes: graphical operations, which change the appearance of visualizations, data operations which manipulate data encoded in visualizations, and set operations which create and manipulate object sets. Data objects are only mapped to graphical objects, not spaces, so data operations will not refer to space entities.Bas i cVi s u a l i z a t i o n Tas ksEncodeDa taShi f tScal eGr aphi calo per a tionsSeto pe ra tionsAddDel et eDe ri vedat t r i b ut esDat ao pe ra tionsCreat eMappingT rans f or mMappi ngCons t an tGr aphi calT rans f or mCont i nuousNo n-c ont i n uousMa nip ul at eOb ject sCopyDele teValue+ AttFormula + AttFormulaNoneSetAtt+ Value(s) ORAtt+ Object(s) ORObject + Att+ FormulaFormula + ObjectSet + Att +FormulaNoneObjectValue+ AttFormulaOt herObjectOth e rSetGraph i calValueCr ea t e s e tDele t e s e tSumm ari z e setEnumerat eEx pr es smember s hi pObject(s)Ob jec tAt t r i b ut eValue+ AttFigure 4: Basic Visualization Interaction classification hierarchyEach of these classes affect different output states in the system. Graphical operations affect the graphical representations of data objects (i.e. the graphical state). Set operations affect the control state and data operations affect the data state. Changing the control and data state could however, cause secondary effects to the graphical state. For example, deleting a data object will cause the internal database to change. In addition, it also causes all of the graphical representations of the deleted data object to be removed, which changes the graphical state.Figure 4 shows a tree of the three BVI classes. The leaves of the tree indicate the different types of BVI. The italicized text towards the right of the tree show the input types needed by each of the BVIs. For example, a shift BVI requires an attribute (which could be an attribute in the graphical, data or control state) and a value. In the interest of space, only a partial tree is shown and details about input types (e.g., position, force, rotation and torque) are not included. In addition, the other component in both the set and data subtree indicate that there are unlisted operations within those classes. It is not our intention here to capture all of the possible techniques but rather to describe a basic set of operations that capture most of the interactive behaviors that exist today.Graphical Operations: Graphical operations can be divided into encode-data, set-graphical-value, and manipulate-objects. Encode-data refers to operations that change or transform mappings between data and their graphical representations. The change-mapping operation is achievedby altering graphical object or graphical attribute mappings. An example of the former is switching from points to bars to represent attributes of houses. The drag-and-drop operation in Visage [10] is an instance of this class of interaction. An example of the latter is changing the encoding of house-price from the size of a point to saturation.Mappings between graphical attributes and data can also be transformed, either by shifting the encoding range or rescaling the encoding range. Scale operations are usually used to magnify differences among values of a particular attribute (e.g. scale height), and shift operations are used to separate out sets of objects (e.g. shifting x-position). Examples of shift and scale operations can be found in the SDM system[1].As shown in Figure 4, operations that change graphical representations can also be of type set-graphical-value. These operations alter the visual representations of selected entities uniformly by simply setting the values to a constant or according to a formula (graphical- transform). Painting a set of objects red is an example of an operation that sets the value of the color attribute to a constant (red). The formulas available for transforming graphical values have been classified by Leung [6] into continuous and non-continuous functions. The difference between set-graphical-value operations and the encode-data operations is that for the latter, the altered graphical attributes must encode a data attribute, whereas in the former, this need not be the case. Since set-graphical-value operations are not related to the underlying data, they can be applied equally well to both object and space type entities.The third class of graphical operations are for manipulating graphical objects, including creating and deleting them. Unlike the encode-data and set-graphical-value operations, these operations do not change graphical attributes nor do they change the mappings between graphical objects and data. Instead, they operate on the graphical objects as a unit of manipulation.Set operations: Set operations refer to all those operations that act on or form sets. These operations include creating sets, deleting sets, summarizing sets, joining sets, intersecting sets and so forth. Sets provide a way for users to expand the underlying data with new classification information. For example, the aggregation task in our real-estate example caused the creation of multiple sets to represent the classification of houses by agency. When the user aggregated the houses based on agency a set was created for all the houses sold by each agency. An object was created for each set and then visualized.Sets are populated by enumerating members of the set or by expressing conditions for set membership. Enumerating set members is achieved by having the user explicitly pick members from the visualization, by having the designer define the sets apriori or by getting the objects from the system control state. For example, during the coordinated highlighting task in our real-estate example, the highlighted set on the map is constructed based on the enumeration of data-objects within the selected agency aggregates (these objects are obtained by querying the control state).Set membership is defined through a formula or a constraint, which may be dynamically altered. Elements that fulfill the set constraint automatically get added or removed. The dynamic query slider technique in our example consists of a set associated with a membership constraint. The slider controls this constraint and cause elements to be dynamically added to or removed from the set when the slider is moved.Sets have group characteristics. Members that join the set will automatically inherit those group characteristics. Upon leaving the group, members will lose those characteristics and revert back to their individual characteristics. For example in the real-estate filtering task, the slider value controls set membership. When elements enter the set, they become visible because they inherit the visibility property of the set. When they leave the set, they revert back to their individual visibility value which is off. In the same example, a set is created with a bounding box and is painted red. Dragging one element of the set, causes the position of the set to change. This causes the position of all elements within the set to change as well.Data operations:Data operations affect the data elements contained within the visualization. Data operations are especially useful in creating simulations or carrying out what-if analyses. This can be done by changing sets of data values and then observing what changes these cause to the other data values. Another useful data operation is deriving new attributes for the data objects. During analysis, users usually discover new facts about the data and it is useful to be able to augment the data with new findings.5. Composite visualization interactionTo support complex tasks, BVIs can be combined to form composites. There are three types of composition: independent composition, set composition and chained composition. In independent composition, the BVIs are made available for the same visualization but are not related except when there are conflicts in desired effects. Effects are applied orthogonally and operations can be executed in any order or in parallel. Each operation has its own user input methods. In the real-estate example, the map display has a selection interaction with a bounding box and a filtering interaction with a slider. These operations are independently composed into the same visualization. Each operation has separate and distinct input methods (bounding box and slider). Elements that are filtered and therefore not visible cannot be selected with a bounding box. The semantics of items that are selected, made invisible and then visible again are potentially confusing (e.g. when they are invisible, do they remain in the selected set?). Our characterization enables the designer to represent the alternatives.。
Multidisciplinary Design Optimization
Multidisciplinary Design Optimization Multidisciplinary Design Optimization (MDO) is a complex and challenging process that involves integrating various engineering disciplines to achieve the best possible design solution. This approach considers the interactions between different components and subsystems of a system, aiming to optimize the overall performance while meeting multiple conflicting objectives. MDO has gained significant attention in recent years due to its potential to improve the efficiency, reliability, and cost-effectiveness of engineering systems. However,it also presents several challenges and requires a multidimensional perspective to be effectively implemented. From an engineering perspective, MDO offers a systematic framework for addressing the inherent complexity of modern engineering systems. By considering the interactions between different disciplines such as structural, thermal, fluid dynamics, and control systems, MDO enables engineers to develop more integrated and optimized designs. This holistic approach can lead to significant improvements in performance, weight, cost, and other key metrics. For example, in the aerospace industry, MDO has been used to design more fuel-efficient aircraft by optimizing the aerodynamic shape, structural layout, and propulsion system in a coordinated manner. However, the implementation of MDO is not without its challenges. One of the primary obstacles is the need for effective collaboration and communication between experts from different disciplines. Each discipline may have its own specialized tools, models, and optimization algorithms, making it difficult to integrate them into a unified framework. Furthermore, the conflicting objectives and constraints of different disciplines can lead to trade-offs and compromises that are not easily resolved. This requires a careful balance between the competing requirements to achieve a satisfactory solution. Moreover, the computational cost of MDO can be substantial, especially when dealing with complex engineering systems and high-fidelity models. The optimization process often involves running numerous simulations and analyses, which can be time-consuming and resource-intensive. This necessitates the use of advanced computational tools and techniques, as well as efficient algorithms for solving large-scale optimization problems. Additionally, the uncertainty and variabilityin the input parameters and models can further complicate the optimization process,requiring robust and reliable methods for handling these uncertainties. From a business perspective, MDO has the potential to provide a competitive advantage by enabling the development of innovative and high-performance products. By optimizing the design of engineering systems, companies can reduce development time, minimize costs, and improve the overall quality and reliability of their products. This can lead to increased customer satisfaction and market share, as well as enhanced profitability and sustainability. However, the initial investment in MDO capabilities and the training of personnel can be significant, requiring a long-term strategic commitment from the organization. Furthermore, theintegration of MDO into the product development process may require changes in the organizational structure and workflow. This can pose challenges in terms of resistance to change, cultural barriers, and the need for cross-functional collaboration. Effective leadership, communication, and change management are essential for successfully implementing MDO within an organization. Additionally, the intellectual property and data management issues associated with MDO, such as sharing proprietary information and protecting sensitive data, need to becarefully addressed to ensure confidentiality and security. From a societal perspective, MDO has the potential to contribute to sustainable development by promoting the efficient use of resources and the reduction of environmental impacts. By optimizing the design of engineering systems, MDO can help minimize energy consumption, emissions, and waste generation, contributing to a more sustainable and eco-friendly future. For example, in the automotive industry, MDO has been used to develop more fuel-efficient and low-emission vehicles, addressing the global challenges of climate change and air pollution. However, the adoption of MDO also raises ethical and social responsibility considerations. The potential misuse of MDO for military purposes, surveillance, or other controversial applications poses ethical dilemmas that need to be carefully considered. Additionally, the accessibility and affordability of MDO tools and technologies can raise equity and inclusivity concerns, as not all individuals and communities may have equal access to the benefits of MDO. It is essential to ensure that the deployment of MDO is aligned with ethical principles, social values, and regulatory frameworks to promote the common good and minimize potential risks andnegative impacts. In conclusion, Multidisciplinary Design Optimization offers significant opportunities for improving the efficiency, reliability, and sustainability of engineering systems. However, its implementation requires a multidimensional perspective that takes into account engineering, business, and societal considerations. By addressing the technical challenges, organizational barriers, and ethical implications, MDO can contribute to the development of innovative and high-performance products that benefit individuals, organizations, and the environment. Embracing a holistic and responsible approach to MDO can lead to a more prosperous and harmonious future for all stakeholders.。
某电驱动桥主减速器振动噪声特性仿真与试验研究
2023年第47卷第11期Journal of Mechanical Transmission某电驱动桥主减速器振动噪声特性仿真与试验研究崔玥1田韶鹏1邹琳2王新强2（1 武汉理工大学汽车工程学院，湖北武汉430070）（2 武汉理工大学机电工程学院，湖北武汉430070）摘要利用Adams软件建立了主减速器“二级斜齿轮副-转子-轴承”仿真模型，仿真结果显示，在0~3 000 r/min，噪声主要来源于系统的共振，且高速级齿轮副贡献量较大。
此外，进行了主减速器振动试验和实车噪声试验，结果表明，当输入转速为2 138.67 r/min时，主减速器的噪声值激增最为明显，与此相对应的齿轮副啮合频率为605.96 Hz，验证了仿真结果。
最后，研究了轴承支承刚度对共振转速分布的影响，并提出系统结构刚度的优化措施。
优化结果表明，在1 400~2 200 r/min转速带，振动幅值显著减小，共振转速向高转速偏移。
试验结果与仿真分析结果相对误差均小于5%，证明了Adams仿真结果的正确性和优化措施的有效性，为缩短电驱动桥主减速器开发周期、节省研发成本提供了参考。
关键词电驱动桥齿轮-转子-轴承系统共振特性路试试验Simulation and Experimental Study on Vibration and Noise Characteristics ofthe Main Reducer of an Electric Drive AxleCui Yue1Tian Shaopeng1Zou Lin2Wang Xinqiang2(1 School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China)(2 School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China)Abstract In this study, a simulation model of the two-stage helical gear pair-rotor-bearing of the main reducer is developed by Adams software. The simulation results show that the noise mainly comes from the reso⁃nance of the system and the contribution of the high-speed gear pair is large when the speed is in the range of 0~ 3 000 r/min. Furthermore, the vibration test of the main reducer and the real vehicle noise test are also con⁃ducted. The test results show that the noise of the main reducer increases obviously when the input speed is 2 138.67 r/min, and the corresponding meshing frequency of the gear pair is 605.96 Hz, verifying the simulation results. Finally, the effect of bearing stiffness on the resonance speed distribution is studied, and the optimiza⁃tion measures of the structural stiffness of the system are proposed. After optimization, the vibration amplitude decreases significantly when the speed is in the range of 1 400~2 200 r/min, and the resonance speed shifts to a higher speed. The relative errors between the test results and simulation analysis results are less than 5%, which verifies the Adams simulation results and the optimization measures, and provides some reference for shortening the development cycle of the electric drive axle main reducer and saving research and development costs.Key words Electric drive axle Gear-rotor-bearing system Resonance characteristics Road test0 引言相较于燃油汽车，由于没有发动机，电动汽车省略了变速箱、纵向传动轴等零部件，采用机械结构将电动机与主减速器直接相连。
SolidWorks运动仿真完全教程
15
运动副基础知识（6）
In Line点在直线上
In Plane点在面内
方向
指定 的面
连接点
连接点
X参考轴
约束2个移动自由度
约束构件1的连接点，只能沿着构件2连接点 标记的Z轴运动
© 2007 SolidWorks Corp. Confidential.
a Concentric joint.
– 一个正交同轴配合转化为同轴副
One Coincident and One Orthogonal Concentric mates in
SolidWorks becomes a Revolute joint.
– 一个重合和一个正交同轴配合转化为一个转动 副
One Point to Point coincident mate in SolidWorks
Pendulum restrained to pivot about mounting point
5
Constraint Mapping约束映射
▪ Mapping of SolidWorks assembly mates (constraints) to COSMOSMotion joints.
映射SolidWorks装配体配合（约束）为 COSMOSMotion的运动副
运动约束 PointPoint PointPointDist PointLine PointLineDist PointPlaneDist PointPlaneDist PointLineDist PointLine LineLine LineLineDist LineLineAng LineLineAng (0 deg.) LineLineAng (90 deg.)
智能网联环境下单交叉口车辆轨迹优化
第22卷第1期2024年03月交通运输工程与信息学报Journal of Transportation Engineering and InformationVol.22No.1Mar.2024文章编号：1672-4747（2024）01-0025-14智能网联环境下单交叉口车辆轨迹优化冯红艳1，康雷雷1，刘澜*1,2（1.西南交通大学，交通运输与物流学院，成都611756；2.综合交通运输智能化国家地方联合工程实验室，成都611756）摘要：为了提高信号灯前车辆的通行效率，改善交通流整体运行水平，本文从减少车辆延误和降低燃油消耗两个角度入手，在智能网联环境下，提出了一种车辆编组识别算法和针对编组头车的多目标线性轨迹优化模型(MOLP-pl)。
首先对智能驾驶员跟驰模型（IDM）进行改进，调整车辆状态，减少车辆随机到达状态下车辆速度和车头时距分布的差异，同时为后续MOLP-pl轨迹优化模型的运行提供先决条件。
在此基础上，以车辆编组为优化单元，通过车辆编组识别算法识别编组头车和跟随车辆，将编组头车的行驶轨迹作为优化对象并建立相应的数学模型。
为了提高车辆轨迹优化模型的求解效率和精度，对其进行线性化重构，采用线性求解器计算编组头车加速度，构建编组头车最佳时空轨迹，然后，利用IDM跟驰模型计算跟随车辆的行驶速度，从而使编组车辆最大效率的通过交叉口。
最后，利用SUMO构建的仿真实验表明：本研究提出的车辆轨迹优化算法可显著提高信号灯前车辆的通行效率，在三种不同的交通饱和度条件下，相对于无速度引导场景，车辆延误分别降低了8.56%、12.42%、64.79%，燃油消耗分别降低了17.21%、18.34%、12.64%；相对于逻辑控制场景，延误分别降低了-1.31%、2.63%、60.83%，燃油消耗分别降低了2.47%、7.91%、2.28%。
关键词：智能交通；车辆轨迹优化；交通效率与能耗；编组识别；SUMO中图分类号：U491文献标志码：A DOI：10.19961/ki.1672-4747.2023.08.004Trajectory optimization of vehicles at isolated intersectionin a connected and automated environmentFENG Hongyan1,KANG Leilei1,LIU Lan*1,2（1.School of Transportation and Logistics,Southwest Jiaotong University,Chengdu611756,China；2.National United Engineering Laboratory of Integrated and Intelligent Transportation,Chengdu611756,China）Abstract：To enhance traffic efficiency at signal lights from the perspective of reducing vehicle de-lay and minimizing fuel consumption,this study proposes a vehicle platoon identification algorithm and a Multi-objective Linear Programming Trajectory Optimization Model for Platoon-leading Vehi-cles(MOLP-pl).First,the Intelligent Driver Model(IDM)is improved to adjust the vehicle state,re-duce the differences in vehicle speed and headway distribution under random arrival conditions,and provide a prerequisite for the operation of the subsequent MOLP-pl trajectory optimization model.On this basis,the vehicle platoon identification algorithm is utilized to discern the leading and fol-lowing vehicles,with the trajectory of the former serving as the optimization objective for establish-ing a corresponding mathematical model.Then,the vehicle trajectory optimization model is linear-收稿日期：2023-08-16录用日期：2023-09-07网络首发：2023-09-18审稿日期：2023-08-16~2023-08-21；2023-08-31~2023-09-07基金项目：成都市重点研发支撑计划技术创新研发项目（2022-YF05-00302-SN）；国家自然科学基金项目(61873216)作者简介：冯红艳（1998—），女，硕士研究生，研究方向为智能交通，E-mail：***************通信作者：刘澜（1965—），男，教授，研究方向为智能交通系统，E-mail：*******************引文格式：冯红艳，康雷雷，刘澜.智能网联环境下单交叉口车辆轨迹优化[J].交通运输工程与信息学报，2024，22(1)：25-38.FENG Hong-yan,KANG Lei-lei,LIU Lan,Trajectory optimization of vehicles at isolated intersection in a connected and automated environment[J].Journal of Transportation Engineering and Information，2024，22(1)：25-38.26交通运输工程与信息学报第22卷ized and reconstructed to enhance efficiency and accuracy.Subsequently,a Liner Solver is employed to determine the acceleration of the leading vehicle,facilitating the construction of an optimal spatio-temporal trajectory.The IDM model is utilized to calculate the speed of the following vehicles.The simulation experiments conducted using SUMO demonstrate that:1)The vehicle trajectory optimiza-tion algorithm proposed in this study can significantly improve traffic efficiency at intersections.Un-der three different levels of traffic saturation,vehicle delay reduced by8.56%,12.42%,and64.79%, while fuel consumption decreased by17.21%,18.34%,and12.64%,respectively,compared to the sce-nario without vehicle speed guidance;2)The vehicle delay reduced by-1.31%,2.63%,and60.83% respectively,while the fuel consumption decreased by2.47%,7.91%,and2.28%in comparison to the logic-based control strategy.Key words：intelligent transportation;vehicle trajectory optimization;traffic efficiency and consump-tion;platoon identification;Simulation of Urban MObility0引言交叉口是城市交通网络中的关键节点[1]，经常会发生交通拥堵和车辆延误，其中驾驶人员不恰当的加减速行为会造成车辆时空轨迹的随机性和不确定性，从而降低通行效率。
plant simulation
Plant SimulationIntroductionPlant simulation is a powerful tool used in various industries to model, analyze, and optimize the performance of manufacturing systems. It helps in understanding and improving the efficiency of production processes by simulating the behavior of different components within a plant.Benefits of Plant SimulationSome of the key benefits of using plant simulation include:1.Visualization: Plant simulation provides a visual representation ofthe manufacturing system, allowing stakeholders to understand complexprocesses easily. It helps in identifying bottlenecks, optimizing layouts, andimproving overall productivity.2.Process Optimization: By simulating different scenarios, plantsimulation helps in identifying inefficiencies in the production process. Itallows users to test and evaluate alternative strategies, optimize schedules, and reduce cycle times.3.Cost Reduction: Plant simulation enables users to identifyopportunities for cost reduction by analyzing production line performance,minimizing material handling, and optimizing resource utilization. It helps in reducing inventory levels, improving throughput, and increasing overallprofitability.4.Reduced Downtime: Simulation enables users to analyze and predictthe impact of equipment failures, maintenance activities, or other disruptions on the production process. It helps in planning preventive maintenance,reducing unplanned downtime, and increasing overall equipment effectiveness.5.What-if Analysis: Plant simulation allows users to perform。
Optimization and Control of Dynamic Systems
Optimization and Control of DynamicSystemsOptimization and control of dynamic systems is a crucial field in engineering that focuses on finding the best possible solution for a given problem. This field encompasses various aspects such as modeling, analysis, design, and implementation of control algorithms to achieve desired system performance. In this response, I will explore the importance of optimization and control of dynamic systems from multiple perspectives. From an engineering perspective, optimization and control of dynamic systems play a vital role in improving the performance, efficiency, and reliability of complex systems. By utilizing mathematical models and control algorithms, engineers can design and implement control strategies that ensure the system operates within desired specifications. This is particularly important in industries such as aerospace, automotive, and manufacturing, where theoptimization of systems can lead to significant cost savings, improved safety, and enhanced productivity. Moreover, optimization and control techniques areessential in addressing real-world challenges. For instance, in the field of renewable energy, the integration of renewable sources into the power gridrequires advanced control strategies to ensure stability and reliability. Optimization techniques can be used to determine the optimal placement and sizing of renewable energy sources to maximize their contribution while minimizing costs. Similarly, in autonomous vehicles, control algorithms are crucial for safe and efficient navigation, taking into account various factors such as traffic conditions, weather, and pedestrian movement. From a societal perspective, optimization and control of dynamic systems have a direct impact on our daily lives. For example, in transportation systems, traffic control algorithms optimize traffic flow, reducing congestion and travel time. This not only improves the efficiency of transportation networks but also reduces fuel consumption and greenhouse gas emissions. Similarly, in healthcare, optimization techniques can be used to improve patient scheduling, resource allocation, and treatment planning, leading to better healthcare outcomes and reduced costs. Furthermore,optimization and control of dynamic systems have significant economic implications.By optimizing processes and systems, companies can reduce operational costs, improve product quality, and enhance customer satisfaction. For instance, in manufacturing, control algorithms can be used to optimize production processes, minimizing waste and maximizing throughput. This leads to increased profitability and competitiveness in the market. Optimization techniques are also widely used in financial markets, where algorithms are employed to optimize investment portfolios and trading strategies, maximizing returns while minimizing risks. From apersonal perspective, optimization and control of dynamic systems can have a profound impact on individuals' lives. For instance, in the context of smart homes, control algorithms can be used to optimize energy consumption, adjusting heating, cooling, and lighting systems based on occupancy and weather conditions. This not only reduces energy bills but also contributes to environmental sustainability. Additionally, optimization techniques can be applied to personal finance, helping individuals make informed decisions about saving, investing, and spending, ultimately improving their financial well-being. In conclusion, optimization and control of dynamic systems are of utmost importance from various perspectives. From an engineering standpoint, these techniques enable the design and implementation of control strategies that enhance system performance andreliability. Societally, optimization and control techniques have a direct impact on transportation, healthcare, and energy sectors, leading to improved efficiency, reduced costs, and enhanced quality of life. Economically, optimization andcontrol contribute to increased profitability, competitiveness, and financialwell-being. Personally, these techniques can improve energy efficiency, financial decision-making, and overall quality of life. Thus, optimization and control of dynamic systems are essential in addressing complex problems and driving progressin various domains.。
visual simulation
SIMIT V9.1 安装和使用说明说明书
SIMITNotes on Installation and UsageThese notes should be considered more up-to-date than the information in other documents.Read the notes carefully, because they contain information on installing and using SIMIT V9.1. The installation notes in chapter 4 contain important information that you will require in order to install SIMIT. Read these notes before installing the software.Software disclaimer for simulation productsSiemens offers simulation software to plan, simulate and optimize plants and machines. The simulation- and optimization-results are only non-binding suggestions for the user. Thequality of the simulation and optimizing results depend on the correctness and thecompleteness of the input data. Therefore, the input data and the results have to bevalidated by the user.Security informationSiemens provides automation and drive products with industrial security functions thatsupport the secure operation of plants or machines. They are an important component in a holistic industrial security concept. With this in mind, our products undergo continuousdevelopment. We therefore recommend that you keep yourself informed with respect to our product updates. Please find further information and newsletters on this subject at:.To ensure the secure operation of a plant or machine it is also necessary to take suitable preventive action (e.g. cell protection concept) and to integrate the automation and drive components into a state-of-the-art holistic industrial security concept for the entire plant or machine. Any third-party products that may be in use must also be taken into account.Please find further information at: /industrialsecuritySIMIT allows you to identify simulation models using a unique version number and also to restrict visibility of certain information by providing password protection for macros andcomponents. Please note that these procedures do not provide unimpeachable protection against skilled high effort attacks.ContentsNotes on Installation1Contents of the Consignment2Hardware Requirements3Software Requirements3.1Operating Environment3.2Memory Requirements3.3Compatibility with Other Software Products3.4Anti virus software3.5Online Documentation4Installation4.1Installation of SIMIT4.2Copy protection dongle4.3Upgrade from SIMIT4.4Uninstalling SIMIT5Specific features of the SIMIT Unit (“SIMBA”)6Specific features of the Virtual Controller (“SIMIT VC”)7Remote Control Interface8UnlockHWConfig.exe9Terms of License and Disclaimer of Liability for Open Source Software1Contents of the ConsignmentYou received one of the following products with this consignment:SIMIT V9.1The following items are included in this package:1 CD SIMIT V9.11 Dongle with an individual license number (Type: Standard, Professional or Ultimate)1 Product InformationSIMIT Upgrade V9.1The following items are included in this package:1 CD SIMIT V9.11 Product information including license informationContent of the SIMIT CDFile Start.exe:-SIMIT Installer (Frame Setup)Folder _Beispiele:-SIMIT-Sample Projects (german)-Sample implementation for the Shared Memory (SHM) coupling-Sample data for bulk data import (SMD)-Sample data for 3D-Models-Sample data and Document Type Definition for XML-ImportFolder _D ocs:-Manuals in pdf format in German and English. You can view the manuals at any time on the SIMIT-CD.Ordner _LegacyComponents:-Prior versions of standard component types. They can be helpful if they are used in existing SIMIT projects but are not embedded in the archive file.Ordner _LegacyTemplates:-Prior versions of standard templates. These templates still contain the variable “GATEWAY” that has been changed to “COUPLING” in SIMIT V8.0.Folder _Samples:-SIMIT Sample Projects (english)-Sample implementation for the Shared Memory (SHM) coupling-Sample data for bulk data import (SMD)-Sample data for 3D-Models-Sample data and Document Type Definition for XML-ImportFolder Support/Tools:-Tool to make HWConfig data available.Folder XMLTRANSFER_09.00.00.00_01.89.00.03::-Setup for installation of the XML transfer as an add-on for PCS 7.In order to work with SIMIT, you need a PC with the following minimum requirements for processor speed/performance (recommendations from Microsoft), RAM and graphics capability:GraphicsProcessor Expandedmemoryconfiguration2 GHz2GB *)DirectX 9-raphics device withWDDM 1.0- or later driver*) At least 4 GB expanded memory configuration is recommendedIn addition, you will need a CD drive and a free USB port.The performance of your graphics architecture as well as memory configuration may have considerable influence on the performance of SIMIT. In case you work with large SIMIT projects with e.g. several hundred diagrams you should use a PC with up-to-date performance.3.1Operating EnvironmentOperating SystemSIMIT is a 32-bit application that is released for the following operating systems:-MS Windows 7 SP1 (Professional, Ultimate, Enterprise, 32 and 64 bit versions)-MS Windows 10 Pro and Enterprise (32 and 64 bit versions)-MS Windows Server 2008 R2 (64 Bit)-MS Windows Server 2012 R2 (64 Bit)-MS Windows Server 2016You may use one of these operating systems as a virtual machine under the control of aVMware host (ESXi V5.5 or V6.0).SIMIT has not been tested for use in other environments; use at your own risk.Display of PDF filesTo read the supplied PDF files, you need a PDF reader that is compatible with PDF 1.7(ISO32000-1:2008 PDF).Security SettingsIn project directories as well as in the SIMIT workspace, all users need to receive writepermission in case of non-exclusive use by one user only. These rights have to be set up byan administrator.Note: The standard rights available in the operating system depend on the operating systemin use. Tools used for the creation of partitions will implement their own security guidelines.Hibernation modeShifting to hibernation mode is generally prevented by SIMIT.Modifying date and timePlease do not modify the date or time of your computer while SIMIT is running since thiscould cause unpredictable errors.3.2Memory RequirementsSIMIT requires approx. 350 Mbytes of memory on your hard disk. The exact value dependson your operating system and on the file system used on your personal computer.Additionally, on the drive your project data is located you need to make sure enough harddisk space is available. If during an operation (e.g. saving a SIMIT diagram or starting thesimulation) disk space is insufficient, this may lead to corruption of project data.We also recommend that you do not store the project data on the same drive as the Windowsswap file.3.3Compatibility with Other Software ProductsSIMIT V9.1 cannot be installed as long as SIMIT V8.x is installed on your computer. Asapplicable please uninstall SIMIT V8.x first.There are no further incompatibilities known to other software products. Simultaneous use ofSIMIT V5.x or SIMIT V7.x and SIMIT V9.x may fail, however.Since the Virtual Controller is integrated into SIMIT an existing installation of SIMIT VC V3.0must be uninstalled manually if applicable.SIMIT VC supports PCS 7 versions 7.0 to 9.0 according to the emulation of driver blocks. 3.4Anti virus softwareThe following antivirus software has been tested for compatibility with SIMIT V9.1-Trend Micro OfficeScan Client V11.0.6054Other versions or other anti virus software cannot be guaranteed by Siemens. Please do thetest for compatibility yourself if using them.3.5Online DocumentationAll SIMIT components contained in this delivery provide an online help which may be openedfrom the component taskcard as well as from the diagram.4Installation4.1Installation of SIMITSIMIT requires administrator rights for installation. Insert the SIMIT CD in the drive. In caseyour PC is configured appropriately, installation will start automatically. Otherwise, pleasestart installation of SIMIT manually by double clicking the program Start.exe in the root folderof the SIMIT-CD using Microsoft Windows Explorer.Some notes on required user input during setup:SIMIT can be installed in any folder. Do not specify a folder that already contains data! Forusing SIMIT, only read-access to this installation folder is required.In addition, SIMIT requires a workspace for placing data. For using SIMIT, you need read andwrite access to this workspace. The workspace is usually located at the (hidden) folderC:\ProgramData\Siemens\Automation\SIMIT.SIMIT projects may be placed at any location on the file system, independent from these twoinstallation folders.NoteSIMIT registers itself in Microsoft Windows system files. You must not delete, moveor rename SIMIT files and folders using Microsoft Windows utilities such as theExplorer or modify SIMIT data in the Microsoft Windows registry. The program mayno longer run properly after such modifications.4.2Copy protection dongleBefore using SIMIT, you need to plug the dongle that was delivered into an available usb portof your PC. Please do not use extensions or USB-hubs.Using the DEMO modeIf there is no valid SIMIT dongle plugged, you can launch SIMIT in DEMO mode. Thefunctionality is restricted in DEMO mode. The purpose of the DEMO mode is to make youfamiliar working with SIMIT. Productive work is not possible in DEMO mode.For details please see the SIMIT manual.4.3Upgrade from SIMITCoexistent installation with SIMIT V7.xThe installation of SIMIT V9.1 does not affect an existing SIMIT V7.0 or SIMIT V7.1. You canuninstall the older SIMIT version manually.Coexistent installation with SIMIT V8.xA coexistent installation of SIMIT V8.x and V9.x is not possible. You have to uninstall anexisting installation of SIMIT V8.x first.Transfer of existing SIMIT componentsComponents, macros and templates, that you created with SIMIT V7 have to be transferredmanually into the workspace of SIMIT V9.1 (C:\ProgramData\Siemens\Automation\SIMIT\8.0\FULL).Projects that were created with SIMIT V7.x, V8.x or V9.0 can be opened with SIMIT V9.1.When doing this the first time it may last a while to automatically convert the project to thenew version. Afterwards this project can no longer be opened with a previous SIMIT version!CompatibilityComponents, macros, templates and projects that were created with SIMIT V8.x can still beused with SIMIT V9.x. Please be aware of the following incompatibilities:- A coupling to the Virtual Controller V3.0 that has been created with SIMIT V8.1 will not be carried to SIMIT V9.x. You have to create this coupling once again in SIMITV9.x, if applicable.-The syntax of the module addresses used by the “unit connector” has been changed with version 9.0. Instead of using keywords like “Slv” and “Slt” the subsystem, slaveand slot is now indicated by numbers in square bracket. If applicable, you have tocorrect these specifications in the “unit connectors”. Alternatively, you may drag anew “unit connector” from the coupling editor, which provides a connector with theproper setting.-When processing the XML file used for the CMT import and provided by theautomation interface, SIMIT will still use a “\” to separate the CMT hierarchy levels.However, the signal or parameter name is separated by a “.” from SIMIT V9.0onwards as used by PCS 7. You have to adopt your templates if applicable.-In order to optimize the model calculation in SIMIT V9.1 differential equations are assembled and solved no longer regarding the complete SIMIT project but onlyregarding single components. This may cause a slightly changed behavior of thesimulation results if components containing differential equations are used. Thesolvers used for the library FLOWNET and CHEM-BASIC are not affected.License keys for upgradesIf you purchased an upgrade license you have to provide the according license key when starting SIMIT V9.1 the first time. Please note that all license keys that you received for your dongle number are required!Software for Simulation Unit (formerly “Simulation Unit” resp. “SIMBA”) The software necessary for using the Profinet or Profibus coupling in SIMIT V9.x is delivered in conjunction with the hardware (Simulation Unit) and is no longer part of the SIMIT installer. If you already possess this hardware you may get the necessary software (SIMULATIONUnit, abbreviated SU-Software) free of charge:https:///cs/ww/en/view/1097461924.4Uninstalling SIMITNoteSoftware products must be removed according to Microsoft Windows.To do this, use Microsoft Windows application "Software" (Settings > Control Panel >Software) to remove your software package (for example "SIMIT").During uninstall the entire SIMIT installation folder will be deleted, too. Furthermore, allentries in the registry, the startup menu and the desktop will be removed.The SIMIT workspace will not be deleted.Projects that were created with SIMIT will not be deleted.During installation of SIMIT the following software packages were installed, in case they werenot installed yet:-Microsoft Visual C++ 2010 Redistributable-Microsoft Visual C++ 2012 Redistributable-Microsoft Visual C++ 2013 Redistributable-Microsoft Visual C++ 2015 Redistributable-OPC Core Components Redistributable-Microsoft .NET-Framework 4.6Since these software packages may be used by other applications, they will not beautomatically removed during SIMIT uninstall. If you are sure that you do not need thesesoftware packages any more, you may uninstall them using the control panel.5Specific features of the SIMIT Unit (“SIMBA”) The new SU coupling replaces the previous Profibus DP and Profinet IO couplings.In a SU coupling all Profibus and Profinet lines of a S7 station are now concentrated. Thisallows to switch from a …Hardware-in-the-Loop“ configuration to a …Software-in-the-Loop“configuration more easily because the I/O signals in the process model do no longer dependon the corresponding line and need not be modified.However, if you open a SIMIT project of a previous version that contains Profibus or Profinetcouplings, each line will be converted into an individual SU coupling. This allows you to usethe SIMIT project without any modifications. If you want to organize the SU couplingaccording to S7 stations as intended in SIMIT V9.1, please delete the SU Coupling andrecreate it.When using the SIMIT Unit as Profibus DB- or Profinet IO-coupling please note that pausingthe simulation for more than about 30 seconds will abort the connection between theSimulation Unit and SIMIT.Shared Devices attached to the Profinet are currently not supported completely. Only thedevices that are located in the same station as the interface module can be accessed bySIMIT.You can import GSD resp. GSDML files via the “SU administration” to publish deviceinformation to SIMIT. They are marked as “User” in the table. If there is device informationalready delivered by the SIMIT installation it is marked as “System”. The “User” informationalways overrides the “System” information!6Specific features of the Virtual Controller(“SIMIT VC”)The Virtual Controller is able to handle IP-based S7-connections. However, this does notinclude the redundant S7H-protocol for redundant connections unrestrictedly. The S7H-protocol can only be used for connections between Virtual Controllers belonging to the sameSIMIT project.Each delta download is registered in the controller by a time stamp. This time stamp is usedto decide if the program in the controller (“online”) is identical to the program in theengineering tool (“offline”). The Virtual Controller does not provide such a time stamp if thesimulation is restarted, even though the program is loaded correctly. The engineering tool willtherefor report an inconsistent state of the program after delta download and simulationrestart which is not appropriate. You can avoid this message by executing a full download.The Virtual Controller is able to emulate S7-300 controllers. However, technology functionsare not supported. I/Q-addresses must be unique on technology CPUs as well.Please make sure that the subnet masks of all computers that are involved in the simulationare configured in a way that each configured IP address can be assigned to an unambiguoussubnet. Otherwise the licensing of the Virtual Computer might not work properly.Make sure, that IP addresses are not added automatically (e.g. by SIMATIC NETCommunication Settings) to prevent such error-prone configurations.The Virtual Controller supports the Library …S7 F Systems Lib V1_3” under regular conditions(no failure conditions).“Distributed Safety” is currently not supported.7Remote Control InterfaceOther than described in the documentation of the RCI interface the library“Siemens.Simit.API.Coupling.dll“ is no longer located in the installation folder of SIMIT but inthe Global Assembly Cache (GAC) of your computer:"%Windir%\\assembly\GAC_MSIL\Siemens.Simit.API.Coupling\v4.0_1.0.0.0__fd3415afd42094c5\Siemens.Simit.API.Coupling.dll"Please refer to this library at this location and do not copy it to your project and do not deliverthis file as part of your software!8UnlockHWConfig.exeIn STEP 7, the sdb files are by default deleted following compilation of the hardware configuration.To prevent this permanently, launch the program "UnlockHWConfig.exe" once on the STEP 7 PC.Please note that this program must be executed with administration rights!9Terms of License and Disclaimer of Liability for Open Source SoftwareBefore installation, please read the Readme_OSS.rtf file in the root directory of the SIMITCD.。
核范数随机矩阵求解新方法及其RPCA应用
核范数随机矩阵求解新方法及其RPCA应用王臻;杨敏【摘要】RPCA(稳健主成分分析)从原始观测数据中恢复低秩成分和稀疏成分.RPCA常用交替方向法迭代求解,算法的效率取决于核范数优化求解,即SVD分解.而RPCA在计算机视觉应用中,图像和视频等巨大的数据量为大规模数据SVD分解带来了很大困难.采用随机矩阵算法对SVD分解进行改进,分别为计数缩略算法、标准随机k-SVD算法和快速随机k-SVD算法.主要是对原有大规模数据矩阵进行降维随机采样,使用随机投影算法得到原数据矩阵的一个近似,对这个近似矩阵进行QR分解,得到对应的酉矩阵.对酉矩阵进行相关操作,得到与原矩阵SVD相似的结果.算法的时间效率和存储空间得到极大改善.基于单张图像和视频前景检测等仿真实验,表明所提方法大大提高了RPCA迭代优化求解的效率.%RPCA ( Robust Principal Component Analysis) recovers sparse and low rank components from the original observation data. It commonly uses ADM ( Alternate Direction Method) for iterative solving,the efficiency of which depends on the nuclear norm optimiza-tion solution,that is SVD. The application of RPCA in computer vision,large amounts of data from images and video make it difficult for large-scale data SVD. Therefore,a random matrix algorithm is adopted to improve the SVD,respectively the algorithm of count sketch, the prototype randomized k-SVD and the faster randomized k-SVD. Its main idea is to reduce the size of the original large-scale data matrix and sample randomly. Using the random projection algorithm to obtain an approximation of the original matrix,and operating QR decomposition of this approximate matrix,the unitary matrixcorresponding to it is obtained,and then the results which is similar to the SVD can be achieved through correlated operation of unitary matrix. The time and space of the algorithm have been greatly optimized. Simulation based on single image and video foreground detection shows that the proposed method can greatly improve the efficiency of RPCA iterative optimization.【期刊名称】《计算机技术与发展》【年(卷),期】2017(027)012【总页数】6页(P71-76)【关键词】稳健主成分分析;交替方向法;标准随机k-SVD;快速随机k-SVD【作者】王臻;杨敏【作者单位】南京邮电大学自动化学院,江苏南京 210023;南京邮电大学自动化学院,江苏南京 210023【正文语种】中文【中图分类】TP391PCA在高维数据样本中寻找和挖掘低维特征空间。
ms分子模拟软件在有机化学立体异构教学中的应用
第48卷第4期2020年2月广 州 化 工Guangzhou Chemical IndustryVol.48No.4Feb.2020MS 分子模拟软件在有机化学立体异构教学中的应用*郑 倩1,潘 睿1,向 卉2,何书引3(1四川师范大学化学与材料科学学院,四川 成都 610066;2重庆德普外国语学校,重庆 400000;3成都美视国际学校,四川 成都 610042)摘 要:以2-溴丁烷分子为例,介绍了Material Studio(MS)分子模拟软件中Visualizer㊁Forcite㊁Conformers 三个功能模块在有机分子空间构象及卤代烷E2消除反应中的教学应用,帮助学生辨析有机化学中有关构型异构㊁构象异构等易混淆的抽象概念,充分认识卤代烷消除反应中的立体化学现象,从定性㊁定量的角度为深入理解由于立体异构所导致的有机物化学反应行为机理奠定基础㊂关键词:Material Studio 软件;有机化学;立体异构;消除反应 中图分类号:G633.8,O6-39 文献标志码:B 文章编号:1001-9677(2020)04-0108-03*基金项目:四川省哲学社会科学重点研究基地 四川省教师教育研究中心资助项目(TER2018-005);四川师范大学2019质量工程教学改革重点项目(20190019);金课建设项目;四川师范大学2019年度大学生 创新创业训练计划”项目(2019175)㊂通讯作者:潘睿㊂Application of Material Studio Software in Stereo-isomerism ofOrganic Chemistry Teaching *ZHENG Qian 1,PAN Rui 1,XIANG Hui 2,HE Shu -yin 3(1Chemistry and Materials Science College,Sichuan Normal University,Sichuan Chengdu 610066;2Depu Foreign Language School,Chongqing 400000;3Chengdu Meishi International School,Sichuan Chengdu 610042,China)Abstract :Taking 2-bromobutane as an example,Material Studio (MS)software with three functional modules:Visualizer,Forcite and Conformer,was applied in the conformation and E2elimination reaction teaching.The dynamic and consecutive visualization of the molecular conformation and potential energy was expected to clarify abstract conceptions and promote the comprehension of reaction mechanism in the stereo -chemistry from the qualitative and quantitative point of view.Key words :Material Studio software;Organic Chemistry;stereo-isomerism;elimination reaction在高中有机化学中,立体异构涉及了有机物分子微观结构在三维空间的立体式呈现,是有机化学概念的重要组成部分,涵盖了有机物分子的构型异构与构象异构,是锯架式,费歇尔投影式㊁纽曼式等有机物立体结构表达式的概念基础㊂其教学目的是促进学生对有机物立体结构的认知,是从三维空间理解分子结构㊁反应行为及化学性质相互关系,掌握有机立体化学反应机理的重要理论基础[1]㊂然而,在前置教学章节中,关于有机化合物结构的特征仅从平面二维的角度(比如:路易斯构造式㊁短线构造式㊁键线构造式或缩简构造式)使学生有了初步认识,并未涉及相关结构的三维空间立体呈现㊂在后续有机物立体异构的教学中,如果延续二维平面图片结合文字解释的教学方式,常常会使学生难以在头脑中形成立体的三维空间模型,并产生概念上的理解模糊及辨析困难㊂在课堂教学中,常用到的实体分子球棍模型虽然能够在一定程度上帮助学生对构型异构中的顺反异构及光学异构进行认知和理解,但该模型局限于其具有的静态性特征,对于对立体异构中的构象异构则不便于进行C-C 单键的连续性转动,同时也不能对构象进行势能数据的计算㊂同样,为广大化学教师所熟悉的ChemSketch㊁ChemWindow㊁Gaussian 等教学辅助软件虽然能够实现分子构象动态的连续性可视化演示及离散的能量计算,但由于其构象与势能数据不能进行实时的一一关联与对应演示,对消除反应中有关立体化学的定性㊁定量反应机理缺乏必要的直接性和直观性的解析[2]㊂Material Studio(MS)是由世界领先的BIOVIA 计算科学公司开发研制出的一款分子模拟计算软件,在集合了量子力学(Quantum Mechanics)㊁分子力学(Molecular Mechanics)㊁分子动力学(Molecular Dynamics)和蒙特卡洛(Monte Carlo)等多种计算理论的基础上,实现了微观原子㊁分子结构的构建,能够准确地计算出分子能量变化的动力学轨迹并进行可视化分析㊂该软件在360°全景分子结构展示的基础上,增添了连续性的动画演示及与之一一对应的势能表征曲线谱图,使化合物分子三维立体结构实现从抽象㊁晦涩的文字语言解释到直观㊁形象的连第48卷第4期郑倩,等:MS 分子模拟软件在有机化学立体异构教学中的应用109 续性动态演示的转变,并从定性㊁定量的角度进行了完整呈现,增添了直接性㊁直观性的机理解析[3-4]㊂基于有机化学立体异构的知识点所需,本文以教材中典型的2-溴丁烷化合物为例,依次介绍MS 软件中所涉及的3个功能模块:Visualizer㊁Forcite 以及Conformers 在分子空间构象及E2消除反应中定性㊁定量的可视化教学辅助应用㊂1 MS 软件相关功能模块介绍1.1 VisualizerVisualizer 提供的是MS 软件图形化界面,也是整个平台的核心,其功能包括:搭建㊁调整各类三维可视的分子结构模型,在建模的基础上进行数据的二维㊁三维显示并给出相关矢量图[5]㊂1.2 ForciteForcite 是分子力学和分子动力学计算程序,用于对单个分子及分子三维聚集体系的结构优化㊁能量计算,并对分子体系的结构参数㊁热力学性质㊁动力学性质以及统计学性质进行分析[5]㊂1.3 ConformersConformers 是以多种力场为基础,高效搜索各类分子(包括环状分子)构象的计算程序,用于建立分子构象与其能量㊁偶极矩及回转半径之间的关系,在有机物反应机理㊁催化等诸多研究领域具有广泛的应用性[5]㊂2 MS 软件在有机化学立体异构教学中的应用2.1 分子的构建与优化打开MS 软件File 功能菜单,创建一个空白项目名称为2BB project,选择3D Atomistic Document 新建一个结构性文件,命名为2BB.xsd,如图1a 所示㊂在当前创建文件的窗口中,使用工具栏中的sketch 工具绘制2BB 分子结构(此部分操作与chemsketch 相同,便于上手),绘制完成后点击clean 按钮对该分子结构进行初始的分子键长及键角的自动调整,如图1右所示㊂图1 2BB 分子结构的文件创建及化学结构在MS 软件中的绘制Fig.1 2BB structure project construction and molecular structurevisualization in MS software从软件界面的顶端打开Modules 菜单,选择Forcite 模块下的Calculation 选项,在对话框中用鼠标点击Setup,在随即跳出Task 下拉列表中选中Dynamics 选项,依次进行运算时间㊁步长及结构输出的设定㊂设定完成后,关闭对话框,点击run 运行,此时软件开始执行分子动力学能量优化运算㊂在软件运算过程中,当前窗口会自动跳出分子结构能量及程序计算优化的动态变化曲线㊂当计算结束,在窗口的左端树形文件图中会随即生成新的文件夹2BB Forcite Dynamics,其中包含了在优化过程中的所有轨迹运行文件及优化后的稳定结构,用于后续构象的能量计算㊂2.2 分子的空间构象及势能计算由于有机物分子构象是通过C-C 单键旋转所形成原子或原子团在空间的相对位置差异,因此在软件构建的分子结构中,可以通过考察关键位置单键的扭转(torsion)来确定其在空间自由旋转所对应的可能构象数及其势能的高低㊂以2BB.xsd 分子结构为当前活动窗口,从菜单Modules 下拉菜单中选择Conformers 计算模块,打开Conformers 中的Calculation 对话框,点击Torsions 按钮,在跳出的表格中列出了2BB 分子中经过C-C单键旋转所形成的扭转角,如图2左所示㊂在本文的示例分子中,勾取C-C-C-C(1)扭转角进行构象势能计算,其位置对应于图2b 中的虚线所示㊂图2 2BB 分子C-C 单键旋转形成的扭转角C-C-C-C(1)Fig.2 2BB molecular C-C-C-C(1)torsion angle formed bysingle C-C rotation在显示选中的蓝色C-C-C-C(1)扭转角设置区域设置输出步长Steps 值为60,其余对应设置由软件自行更新完成,关闭对话框后,点击Run 开始运行构象及势能的计算程序㊂当上述运算程序完成后,软件将自动生成一个名为2BBConformers Calculation 的新文件夹㊂打开该文件夹,找到名为2BB.std 的文档,双击后在当前软件窗口的左侧会出现列表,从左至右依次显示出构象名㊁扭转角度数及对应的势能值㊂选中旋转角度数及对应的势能值所属列,单击菜单栏中的Quick Plot 生成图表按钮,则会出现如图3的扭转角度与势能曲线图,其中横坐标为2BB 形成不同构象中C-C-C-C(1)扭转角的度数,纵坐标为其对应的势能值㊂2.3 卤代烷E 2消除反应中优势构象的确定在图3曲线中依次分布扭转角度数及所对应的势能值大小,双击选中曲线中的任意点,则软件自动在左侧列表中以蓝色区域显示该点所对应的C-C-C-C(1)扭转角度数及其能量值,双击列表中蓝色区域的构象名称,在当前窗口自动跳出该扭转角度数所对应的2BB 瞬时构象三维立体图示,移动鼠标,该构象随即可进行360°可控的全景展示㊂根据势能曲线分布高低,在图3中依次出现了三个相对极大值点D㊁E 和F,对应的构象分别是frame 2,frame 4和frame110 广 州 化 工2020年2月6㊂要形成这三种构象,2-溴丁烷分子需要更多的能量进行扭转且翻越相应能垒,势必会造成一定的困难导致其构象形成的概率较低㊂同时,在曲线中出现的三个势能相对极小值点A㊁B 和C 点,对应的构象分别是frame 1,frame 3和frame 5,其势能相对较低且为负值,表明这三种构象相对较稳定㊂在图3中,选择曲线中的极小值点A,在列表中蓝色区域标识出该极小值所对应的构象所具有的相关数据,其对应的扭转角度为-172.45°,势能为-9.73kcal /mol,基于空间位阻及能量的考量可得出frame 1是最稳定的构象㊂单击该构象名frame 1,则在当前窗口跳出该势能所对应的三维立体构象,经观察发现,此时的2-溴丁烷构象位于反式共平面,该构象经E2消除反应得到产物为反-2-丁烯㊂图3 2BB 分子中C-C-C-C(1)扭转角度数及其势能曲线图Fig.3 Torsional angle vs potential energy plot of C-C-C-C (1)in 2BB molecule 与此类似,在曲线中选择另两个相对极小值点B 和C,重复进行上述操作,在当前窗口跳出对应的三维立体构象分别为frame 3和frame 5,对应的扭转角度分别为-76.45°和61.55°,势能分别为-8.67和-6.00kcal /mol,其能量相较frame 1更高且同样需翻越相应的能垒进行扭转,进而导致其形成的概率相对较低㊂经观察,frame 3和frame 5构象中两个甲基位于顺式共平面,经E2消除反应得到产物为顺-2-丁烯㊂结合文献中实验所测得的结果:2-溴丁烷采用E2消除反应中生成的反-2-丁烯和顺-2-丁烯产量比例约为3︓1,如图4[6]所示㊂由此验证了在2-溴丁烷E2消除反应中,反式共平面㊁势能最低的frame 1构象是优势构象㊂图4 2-溴丁烷中E2消除反应及产物比例Fig.4 Schematic representation of E2elimination reactionin 2-bromobutane and the yields of butene3 结 语MS 分子模拟软件相较其他类型信息化教学工具的突出优势在于:基于360°全景分子结构的展示,实现分子瞬时构象的能量计算并增添连续性的动画演示及与之一一对应的势能表征曲线谱图,能够将有机物分子的组成-结构-能量相关知识点进行有效的关联及整合,在增加直观性的同时,帮助学生深入理解与辨析有机物的分子构型㊁分子构象等易混淆概念,深化物质微观结构与宏观性质之间的化学本质相关性,为学生后续掌握有机立体化学反应机理,从三维空间理解分子结构㊁反应行为及化学性质的相互关系提供有效的教学辅助作用㊂参考文献[1] 李延伟,姚金环,杨建文,等.量子化学计算软件在物质结构教学中的应用[J].中国现代教育装备,2012(5):8-9.[2] 王辉,曾卓.立体化学教学中空间感观能力的培养[J].大学化学,2016,31(11):22-27.[3] 潘睿.Material Studio 7.0分子模拟软件在结构化学晶体结构教学中的应用[J].化学教育,2018,39(12):90-94.[4] 张翼,段吉国,靳刚,等.用3DS MAX 解决立体化学教学难点[J].计算机与应用化学,2004,21(3):505-507.[5] 创腾科技有限公司.Material Studio [EB /OL ].http://www. /product /proinfo /29.html.2019-3-9.[6] 周文富.有机化学总复习指导[M].厦门:厦门大学出版社,2005:209-211.(上接第70页)[6] 王文平,郭祀远,李琳,等.考马斯亮蓝法测定野木瓜多糖中蛋白质的含量[J].食品研究与开发,2008,29(1):115-116.[7] 张毛莉,罗仓学.石榴皮中总酚含量测定方法的比较[J].食品工业科技,2011,32(5):383-384,388.[8] 李春阳,许时婴,王璋.DPPH 法测定葡萄籽原花青素清除自由基的能力[J].食品与生物技术学报,2006,25(2):102-106.[9] 王玲,唐德强,王佳佳,等.铁皮石斛原球茎与野生铁皮石斛多糖的抗菌及体外抗氧化活性比较[J].西北农林科技大学学报(自然科学版),2016,44(6):167-172,180.[10]鲍素华,查学强,郝杰,等.不同分子量铁皮石斛多糖体外抗氧化活性研究[J].食品科学,2009,30(21):123-127.[11]韩强,林惠芬,朱玲莉.几种中药提取物对酪氨酸酶活性的抑制[J].香料香精化妆品,1998(4):22-24.[12]王晗,朱华平,李文钊,等.桑葚提取物中花青素分析及其体外抗氧化活性研究[J].食品与发酵工业,2019,45(15):170-175.[13]陈清西,林建峰,宋康康.酪氨酸酶抑制剂的研究进展[J].厦门大学学报(自然科学版),2007,46(2):274-282.[14]邹先伟,蒋志胜.植物源酪氨酸酶抑制剂研究进展[J].中草药,2004,35(6):702-705.。
基于周期采样的分布式动态事件触发优化算法
第38卷第3期2024年5月山东理工大学学报(自然科学版)Journal of Shandong University of Technology(Natural Science Edition)Vol.38No.3May 2024收稿日期:20230323基金项目:江苏省自然科学基金项目(BK20200824)第一作者:夏伦超,男,20211249098@;通信作者:赵中原,男,zhaozhongyuan@文章编号:1672-6197(2024)03-0058-07基于周期采样的分布式动态事件触发优化算法夏伦超1,韦梦立2,季秋桐2,赵中原1(1.南京信息工程大学自动化学院,江苏南京210044;2.东南大学网络空间安全学院,江苏南京211189)摘要:针对无向图下多智能体系统的优化问题,提出一种基于周期采样机制的分布式零梯度和优化算法,并设计一种新的动态事件触发策略㊂该策略中加入与历史时刻智能体状态相关的动态变量,有效降低了系统通信量;所提出的算法允许采样周期任意大,并考虑了通信延时的影响,利用Lyapunov 稳定性理论推导出算法收敛的充分条件㊂数值仿真进一步验证了所提算法的有效性㊂关键词:分布式优化;多智能体系统;动态事件触发;通信时延中图分类号:TP273文献标志码:ADistributed dynamic event triggerring optimizationalgorithm based on periodic samplingXIA Lunchao 1,WEI Mengli 2,JI Qiutong 2,ZHAO Zhongyuan 1(1.College of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China;2.School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China)Abstract :A distributed zero-gradient-sum optimization algorithm based on a periodic sampling mechanism is proposed to address the optimization problem of multi-agent systems under undirected graphs.A novel dynamic event-triggering strategy is designed,which incorporates dynamic variables as-sociated with the historical states of the agents to effectively reduce the system communication overhead.Moreover,the algorithm allows for arbitrary sampling periods and takes into consideration the influence oftime delay.Finally,sufficient conditions for the convergence of the algorithm are derived by utilizing Lya-punov stability theory.The effectiveness of the proposed algorithm is further demonstrated through numer-ical simulations.Keywords :distributed optimization;multi-agent systems;dynamic event-triggered;time delay ㊀㊀近些年,多智能体系统的分布式优化问题因其在多机器人系统的合作㊁智能交通系统的智能运输系统和微电网的分布式经济调度等诸多领域的应用得到了广泛的研究[1-3]㊂如今,已经提出各种分布式优化算法㊂文献[4]提出一种结合负反馈和梯度流的算法来解决平衡有向图下的无约束优化问题;文献[5]提出一种基于自适应机制的分布式优化算法来解决局部目标函数非凸的问题;文献[6]设计一种抗干扰的分布式优化算法,能够在具有未知外部扰动的情况下获得最优解㊂然而,上述工作要求智能体与其邻居不断地交流,这在现实中会造成很大的通信负担㊂文献[7]首先提出分布式事件触发控制器来解决多智能体系统一致性问题;事件触发机制的核心是设计一个基于误差的触发条件,只有满足触发条件时智能体间才进行通信㊂文献[8]提出一种基于通信网络边信息的事件触发次梯度优化㊀算法,并给出了算法的指数收敛速度㊂文献[9]提出一种基于事件触发机制的零梯度和算法,保证系统状态收敛到最优解㊂上述事件触发策略是静态事件触发策略,即其触发阈值仅与智能体的状态相关,当智能体的状态逐渐收敛时,很容易满足触发条件并将生成大量不必要的通信㊂因此,需要设计更合理的触发条件㊂文献[10]针对非线性系统的增益调度控制问题,提出一种动态事件触发机制的增益调度控制器;文献[11]提出一种基于动态事件触发条件的零梯度和算法,用于有向网络的优化㊂由于信息传输的复杂性,时间延迟在实际系统中无处不在㊂关于考虑时滞的事件触发优化问题的文献很多㊂文献[12]研究了二阶系统的凸优化问题,提出时间触发算法和事件触发算法两种分布式优化算法,使得所有智能体协同收敛到优化问题的最优解,并有效消除不必要的通信;文献[13]针对具有传输延迟的多智能体系统,提出一种具有采样数据和时滞的事件触发分布式优化算法,并得到系统指数稳定的充分条件㊂受文献[9,14]的启发,本文提出一种基于动态事件触发机制的分布式零梯度和算法,与使用静态事件触发机制的文献[15]相比,本文采用动态事件触发机制可以避免智能体状态接近最优值时频繁触发造成的资源浪费㊂此外,考虑到进行动态事件触发判断需要一定的时间,使用当前状态值是不现实的,因此,本文使用前一时刻状态值来构造动态事件触发条件,更符合逻辑㊂由于本文采用周期采样机制,这进一步降低了智能体间的通信频率,但采样周期过长会影响算法收敛㊂基于文献[14]的启发,本文设计的算法允许采样周期任意大,并且对于有时延的系统,只需要其受采样周期的限制,就可得到保证多智能体系统达到一致性和最优性的充分条件㊂最后,通过对一个通用示例进行仿真,验证所提算法的有效性㊂1㊀预备知识及问题描述1.1㊀图论令R表示实数集,R n表示向量集,R nˑn表示n ˑn实矩阵的集合㊂将包含n个智能体的多智能体系统的通信网络用图G=(V,E)建模,每个智能体都视为一个节点㊂该图由顶点集V={1,2, ,n}和边集E⊆VˑV组成㊂定义A=[a ij]ɪR nˑn为G 的加权邻接矩阵,当a ij>0时,表明节点i和节点j 间存在路径,即(i,j)ɪE;当a ij=0时,表明节点i 和节点j间不存在路径,即(i,j)∉E㊂D=diag{d1, ,d n}表示度矩阵,拉普拉斯矩阵L等于度矩阵减去邻接矩阵,即L=D-A㊂当图G是无向图时,其拉普拉斯矩阵是对称矩阵㊂1.2㊀凸函数设h i:R nңR是在凸集ΩɪR n上的局部凸函数,存在正常数φi使得下列条件成立[16]:h i(b)-h i(a)- h i(a)T(b-a)ȡ㊀㊀㊀㊀φi2 b-a 2,∀a,bɪΩ,(1)h i(b)- h i(a)()T(b-a)ȡ㊀㊀㊀㊀φi b-a 2,∀a,bɪΩ,(2) 2h i(a)ȡφi I n,∀aɪΩ,(3)式中: h i为h i的一阶梯度, 2h i为h i的二阶梯度(也称黑塞矩阵)㊂1.3㊀问题描述考虑包含n个智能体的多智能体系统,假设每个智能体i的成本函数为f i(x),本文的目标是最小化以下的优化问题:x∗=arg minxɪΩðni=1f i(x),(4)式中:x为决策变量,x∗为全局最优值㊂1.4㊀主要引理引理1㊀假设通信拓扑图G是无向且连通的,对于任意XɪR n,有以下关系成立[17]:X T LXȡαβX T L T LX,(5)式中:α是L+L T2最小的正特征值,β是L T L最大的特征值㊂引理2(中值定理)㊀假设局部成本函数是连续可微的,则对于任意实数y和y0,存在y~=y0+ω~(y -y0),使得以下不等式成立:f i(y)=f i(y0)+∂f i∂y(y~)(y-y0),(6)式中ω~是正常数且满足ω~ɪ(0,1)㊂2㊀基于动态事件触发机制的分布式优化算法及主要结果2.1㊀考虑时延的分布式动态事件触发优化算法本文研究具有时延的多智能体系统的优化问题㊂为了降低智能体间的通信频率,提出一种采样周期可任意设计的分布式动态事件触发优化算法,95第3期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀夏伦超,等:基于周期采样的分布式动态事件触发优化算法其具体实现通信优化的流程图如图1所示㊂首先,将邻居和自身前一触发时刻状态送往控制器(本文提出的算法),得到智能体的状态x i (t )㊂然后,预设一个固定采样周期h ,使得所有智能体在同一时刻进行采样㊂同时,在每个智能体上都配置了事件检测器,只在采样时刻检查是否满足触发条件㊂接着,将前一采样时刻的智能体状态发送至构造的触发器中进行判断,当满足设定的触发条件时,得到触发时刻的智能体状态x^i (t )㊂最后,将得到的本地状态x^i (t )用于更新自身及其邻居的控制操作㊂由于在实际传输中存在时延,因此需要考虑满足0<τ<h 的时延㊂图1㊀算法实现流程图考虑由n 个智能体构成的多智能体系统,其中每个智能体都能独立进行计算和相互通信,每个智能体i 具有如下动态方程:x ㊃i (t )=-1h2f i (x i )()-1u i (t ),(7)式中u i (t )为设计的控制算法,具体为u i (t )=ðnj =1a ij x^j (t -τ)-x ^i (t -τ)()㊂(8)㊀㊀给出设计的动态事件触发条件:θi d i e 2i (lh )-γq i (lh -h )()ɤξi (lh ),(9)q i (t )=ðnj =1a ij x^i (t -τ)-x ^j (t -τ)()2,(10)㊀㊀㊀ξ㊃i (t )=1h[-μi ξi (lh )+㊀㊀㊀㊀㊀δi γq i (lh -h )-d i e 2i (lh )()],(11)式中:d i 是智能体i 的入度;γ是正常数;θi ,μi ,δi 是设计的参数㊂令x i (lh )表示采样时刻智能体的状态,偏差变量e i (lh )=x i (lh )-x^i (lh )㊂注释1㊀在进行动态事件触发条件设计时,可以根据不同的需求为每个智能体设定不同的参数θi ,μi ,δi ,以确保其能够在特定的情境下做出最准确的反应㊂本文为了方便分析,选择为每个智能体设置相同的θi ,μi ,δi ,以便更加清晰地研究其行为表现和响应能力㊂2.2㊀主要结果和分析由于智能体仅在采样时刻进行事件触发条件判断,并在达到触发条件后才通信,因此有x ^i (t -τ)=x^i (lh )㊂定理1㊀假设无向图G 是连通的,对于任意i ɪV 和t >0,当满足条件(12)时,在算法(7)和动态事件触发条件(9)的作用下,系统状态趋于优化解x ∗,即lim t ңx i (t )=x ∗㊂12-β2φm α-τβ2φm αh -γ>0,μi+δi θi <1,μi-1-δi θi >0,ìîíïïïïïïïï(12)式中φm =min{φ1,φ2}㊂证明㊀对于t ɪ[lh +τ,(l +1)h +τ),定义Lyapunov 函数V (t )=V 1(t )+V 2(t ),其中:V 1(t )=ðni =1f i (x ∗)-f i (x i )-f ᶄi (x i )(x ∗-x i )(),V 2(t )=ðni =1ξi (t )㊂令E (t )=e 1(t ), ,e n (t )[]T ,X (t )=x 1(t ), ,x n (t )[]T ,X^(t )=x ^1(t ), ,x ^n (t )[]T ㊂对V 1(t )求导得V ㊃1(t )=1h ðni =1u i (t )x ∗-x i (t )(),(13)由于ðni =1ðnj =1a ij x ^j (t -τ)-x ^i (t -τ)()㊃x ∗=0成立,有V ㊃1(t )=-1hX T (t )LX ^(lh )㊂(14)6山东理工大学学报(自然科学版)2024年㊀由于㊀㊀X (t )=X (lh +τ)-(t -lh -τ)X ㊃(t )=㊀㊀㊀㊀X (lh )+τX ㊃(lh )+t -lh -τhΓ1LX^(lh )=㊀㊀㊀㊀X (lh )-τh Γ2LX^(lh -h )+㊀㊀㊀㊀(t -lh -τ)hΓ1LX^(lh ),(15)式中:Γ1=diag (f i ᶄᶄ(x ~11))-1, ,(f i ᶄᶄ(x ~1n ))-1{},Γ2=diag (f i ᶄᶄ(x ~21))-1, ,(f i ᶄᶄ(x ~2n))-1{},x ~1iɪ(x i (lh +τ),x i (t )),x ~2i ɪ(x i (lh ),x i (lh+τ))㊂将式(15)代入式(14)得㊀V ㊃1(t )=-1h E T (lh )LX ^(lh )-1hX ^T (lh )LX ^(lh )+㊀㊀㊀τh2Γ2X ^T (lh -h )L T LX ^(lh )+㊀㊀㊀(t -lh -τ)h2Γ1X ^T (lh )L T LX ^(lh )㊂(16)根据式(3)得(f i ᶄᶄ(x ~i 1))-1ɤ1φi,i =1, ,n ㊂即Γ1ɤ1φm I n ,Γ2ɤ1φmI n ,φm =min{φ1,φ2}㊂首先对(t -lh -τ)h2Γ1X ^T (lh )L T LX ^(lh )项进行分析,对于t ɪ[lh +τ,(l +1)h +τ),基于引理1和式(3)有(t -lh -τ)h2Γ1X ^T (lh )L T LX ^(lh )ɤβhφm αX ^T (lh )LX ^(lh )ɤβ2hφm αðni =1q i(lh ),(17)式中最后一项根据X^T (t )LX ^(t )=12ðni =1q i(t )求得㊂接着分析τh2Γ2X ^(lh -h )L T LX ^(lh ),根据引理1和杨式不等式有:τh2Γ2X ^T (lh -h )L T LX ^(lh )ɤ㊀㊀㊀㊀τβ2h 2φm αX ^T (lh -h )LX ^(lh -h )+㊀㊀㊀㊀τβ2h 2φm αX ^T (lh )LX ^(lh )ɤ㊀㊀㊀㊀τβ4h 2φm αðni =1q i (lh -h )+ðni =1q i (lh )[]㊂(18)将式(17)和式(18)代入式(16)得㊀V ㊃1(t )ɤβ2φm α+τβ4φm αh -12()1h ðni =1q i(lh )+㊀㊀㊀τβ4φm αh ðni =1q i (lh -h )+1h ðni =1d i e 2i(lh )㊂(19)根据式(11)得V ㊃2(t )=-ðni =1μih ξi(lh )+㊀㊀㊀㊀ðni =1δihγq i (lh -h )-d i e 2i (lh )()㊂(20)结合式(19)和式(20)得V ㊃(t )ɤ-12-β2φm α-τβ4φm αh ()1h ðni =1q i (lh )+㊀㊀㊀㊀τβ4φm αh 2ðn i =1q i (lh -h )+γh ðni =1q i (lh -h )-㊀㊀㊀㊀1h ðni =1(μi -1-δi θi)ξi (lh ),(21)因此根据李雅普诺夫函数的正定性以及Squeeze 定理得㊀V (l +1)h +τ()-V (lh +τ)ɤ㊀㊀㊀-12-β2φm α-τβ4φm αh()ðni =1q i(lh )+㊀㊀㊀τβ4φm αh ðni =1q i (lh -h )+γðni =1q i (lh -h )-㊀㊀㊀ðni =1(μi -1-δiθi)ξi (lh )㊂(22)对式(22)迭代得V (l +1)h +τ()-V (h +τ)ɤ㊀㊀-12-β2φm α-τβ2φm αh-γ()ðl -1k =1ðni =1q i(kh )+㊀㊀τβ4φm αh ðni =1q i (0h )-㊀㊀12-β2φm α-τβ4φm αh()ðni =1q i(lh )-㊀㊀ðlk =1ðni =1μi -1-δiθi()ξi (kh ),(23)进一步可得㊀lim l ңV (l +1)h -V (h )()ɤ㊀㊀㊀τβ4φm αh ðni =1q i(0h )-16第3期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀夏伦超,等:基于周期采样的分布式动态事件触发优化算法㊀㊀㊀ðni =1(μi -1-δi θi )ðl =1ξi (lh )-㊀㊀㊀12-β2φm α-τβ2φm αh-γ()ð l =1ðni =1q i(lh )㊂(24)由于q i (lh )ȡ0和V (t )ȡ0,由式(24)得lim l ң ðni =1ξi (lh )=0㊂(25)基于ξi 的定义和拉普拉斯矩阵的性质,可以得到每个智能体的最终状态等于相同的常数,即lim t ңx 1(t )= =lim t ңx n (t )=c ㊂(26)㊀㊀由于目标函数的二阶导数具有以下性质:ðni =1d f ᶄi (x i (t ))()d t =㊀㊀㊀㊀-ðn i =1ðnj =1a ij x ^j (t )-x ^i (t )()=㊀㊀㊀㊀-1T LX^(t )=0,(27)式中1=[1, ,1]n ,所以可以得到ðni =1f i ᶄ(x i (t ))=ðni =1f i ᶄ(x ∗i )=0㊂(28)联立式(26)和式(28)得lim t ңx 1(t )= =lim t ңx n (t )=c =x ∗㊂(29)㊀㊀定理1证明完成㊂当不考虑通信时延τ时,可由定理1得到推论1㊂推论1㊀假设通信图G 是无向且连通的,当不考虑时延τ时,对于任意i ɪV 和t >0,若条件(30)成立,智能体状态在算法(7)和触发条件(9)的作用下趋于最优解㊂14-n -1φm -γ>0,μi+δi θi <1,μi-1-δi θi >0㊂ìîíïïïïïïïï(30)㊀㊀证明㊀该推论的证明过程类似定理1,由定理1结果可得14-β2φm α-γ>0㊂(31)令λn =βα,由于λn 是多智能体系统的全局信息,因此每个智能体很难获得,但其上界可以根据以下关系来估计:λn ɤ2d max ɤ2(n -1),(32)式中d max =max{d i },i =1, ,n ㊂因此得到算法在没有时延情况下的充分条件:14-n -1φm -γ>0㊂(33)㊀㊀推论1得证㊂注释2㊀通过定理1得到的稳定性条件,可以得知当采样周期h 取较小值时,由于0<τ<h ,因此二者可以抵消,从而稳定性不受影响;而当采样周期h 取较大值时,τβ2φm αh项可以忽略不计,因此从理论分析可以得出允许采样周期任意大的结论㊂从仿真实验方面来看,当采样周期h 越大,需要的收剑时间越长,但最终结果仍趋于优化解㊂然而,在文献[18]中,采样周期过大会导致稳定性条件难以满足,即算法最终难以收敛,无法达到最优解㊂因此,本文提出的算法允许采样周期任意大,这一创新点具有重要意义㊂3㊀仿真本文对一个具有4个智能体的多智能体网络进行数值模拟,智能体间的通信拓扑如图2所示㊂采用4个智能体的仿真网络仅是为了初步验证所提算法的有效性㊂值得注意的是,当多智能体的数量增加时,算法的时间复杂度和空间复杂度会增加,但并不会影响其有效性㊂因此,该算法在更大规模的多智能体网络中同样适用㊂成本函数通常选择凸函数㊂例如,在分布式传感器网络中,成本函数为z i -x 2+εi x 2,其中x 表示要估计的未知参数,εi 表示观测噪声,z i 表示在(0,1)中均匀分布的随机数;在微电网中,成本函数为a i x 2+b i x +c i ,其中a i ,b i ,c i 是发电机成本参数㊂这两种情境下的成本函数形式不同,但本质上都是凸函数㊂本文采用论文[19]中的通用成本函数(式(34)),用于证明本文算法在凸函数上的可行性㊂此外,通信拓扑图结构并不会影响成本函数的设计,因此,本文的成本函数在分布式网络凸优化问题中具有通用性㊂g i (x )=(x -i )4+4i (x -i )2,i =1,2,3,4㊂(34)很明显,当x i 分别等于i 时,得到最小局部成本函数,但是这不是全局最优解x ∗㊂因此,需要使用所提算法来找到x ∗㊂首先设置重要参数,令φm =16,γ=0.1,θi =1,ξi (0)=5,μi =0.2,δi =0.2,26山东理工大学学报(自然科学版)2024年㊀图2㊀通信拓扑图x i (0)=i ,i =1,2,3,4㊂图3为本文算法(7)解决优化问题(4)时各智能体的状态,其中设置采样周期h =3,时延τ=0.02㊂智能体在图3中渐进地达成一致,一致值为全局最优点x ∗=2.935㊂当不考虑采样周期影响时,即在采样周期h =3,时延τ=0.02的条件下,采用文献[18]中的算法(10)时,各智能体的状态如图4所示㊂显然,在避免采样周期的影响后,本文算法具有更快的收敛速度㊂与文献[18]相比,由于只有当智能体i 及其邻居的事件触发判断完成,才能得到q i (lh )的值,因此本文采用前一时刻的状态值构造动态事件触发条件更符合逻辑㊂图3㊀h =3,τ=0.02时算法(7)的智能体状态图4㊀h =3,τ=0.02时算法(10)的智能体状态为了进一步分析采样周期的影响,在时延τ不变的情况下,选择不同的采样周期h ,其结果显示在图5中㊂对比图3可以看出,选择较大的采样周期则收敛速度减慢㊂事实上,这在算法(7)中是很正常的,因为较大的h 会削弱反馈增益并减少固定有限时间间隔中的控制更新次数,具体显示在图6和图7中㊂显然,当选择较大的采样周期时,智能体的通信频率显著下降,同时也会导致收敛速度减慢㊂因此,虽然采样周期允许任意大,但在收敛速度和通信频率之间需要做出权衡,以选择最优的采样周期㊂图5㊀h =1,τ=0.02时智能体的状态图6㊀h =3,τ=0.02时的事件触发时刻图7㊀h =1,τ=0.02时的事件触发时刻最后,固定采样周期h 的值,比较τ=0.02和τ=2时智能体的状态,结果如图8所示㊂显然,时延会使智能体找到全局最优点所需的时间更长,但由于其受采样周期的限制,最终仍可以对于任意有限延迟达成一致㊂图8㊀h =3,τ=2时智能体的状态36第3期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀夏伦超,等:基于周期采样的分布式动态事件触发优化算法4 结束语本文研究了无向图下的多智能体系统的优化问题,提出了一种基于动态事件触发机制的零梯度和算法㊂该机制中加入了与前一时刻智能体状态相关的动态变量,避免智能体状态接近最优值时频繁触发产生的通信负担㊂同时,在算法和触发条件设计中考虑了采样周期的影响,在所设计的算法下,允许采样周期任意大㊂对于有时延的系统,在最大允许传输延迟小于采样周期的情况下,给出了保证多智能体系统达到一致性和最优性的充分条件㊂今后拟将本算法向有向图和切换拓扑图方向推广㊂参考文献:[1]杨洪军,王振友.基于分布式算法和查找表的FIR滤波器的优化设计[J].山东理工大学学报(自然科学版),2009,23(5):104-106,110.[2]CHEN W,LIU L,LIU G P.Privacy-preserving distributed economic dispatch of microgrids:A dynamic quantization-based consensus scheme with homomorphic encryption[J].IEEE Transactions on Smart Grid,2022,14(1):701-713.[3]张丽馨,刘伟.基于改进PSO算法的含分布式电源的配电网优化[J].山东理工大学学报(自然科学版),2017,31(6):53-57.[4]KIA S S,CORTES J,MARTINEZ S.Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication[J].Automatica,2015,55:254-264.[5]LI Z H,DING Z T,SUN J Y,et al.Distributed adaptive convex optimization on directed graphs 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IEEE Transactions on Automatic Control,2018,63(7):2248 -2255.[9]LIU J Y,CHEN W S,DAI H.Event-triggered zero-gradient-sum distributed convex optimisation over networks with time-varying topol-ogies[J].International Journal of Control,2019,92(12):2829 -2841.[10]COUTINHO P H S,PALHARES R M.Codesign of dynamic event-triggered gain-scheduling control for a class of nonlinear systems [J].IEEE Transactions on Automatic Control,2021,67(8): 4186-4193.[11]CHEN W S,REN W.Event-triggered zero-gradient-sum distributed consensus optimization over directed networks[J].Automatica, 2016,65:90-97.[12]TRAN N T,WANG Y W,LIU X K,et al.Distributed optimization problem for second-order multi-agent systems with event-triggered and time-triggered communication[J].Journal of the Franklin Insti-tute,2019,356(17):10196-10215.[13]YU G,SHEN Y.Event-triggered distributed optimisation for multi-agent systems with transmission delay[J].IET Control Theory& Applications,2019,13(14):2188-2196.[14]LIU K E,JI Z J,ZHANG X F.Periodic event-triggered consensus of multi-agent systems under directed topology[J].Neurocomputing, 2020,385:33-41.[15]崔丹丹,刘开恩,纪志坚,等.周期事件触发的多智能体分布式凸优化[J].控制工程,2022,29(11):2027-2033. [16]LU J,TANG C Y.Zero-gradient-sum algorithms for distributed con-vex optimization:The continuous-time case[J].IEEE Transactions on Automatic Control,2012,57(9):2348-2354. [17]LIU K E,JI Z J.Consensus of multi-agent systems with time delay based on periodic sample and event hybrid control[J].Neurocom-puting,2016,270:11-17.[18]ZHAO Z Y.Sample-baseddynamic event-triggered algorithm for op-timization problem of multi-agent systems[J].International Journal of Control,Automation and Systems,2022,20(8):2492-2502.[19]LIU J Y,CHEN W S.Distributed convex optimisation with event-triggered communication in networked systems[J].International Journal of Systems Science,2016,47(16):3876-3887.(编辑:杜清玲)46山东理工大学学报(自然科学版)2024年㊀。
Visual Components 3D制造仿真软件系列说明书
LAYOUT PLANNING AND OPTIMIZATION WITH VISUAL COMPONENTSHow 3D manufacturing simulation canhelp you increase flexibility, reduce costs,and improve production performance.What Is Layout Planning?Layout planning is the discipline of designing an effective facility layout that prioritizes worker safety and wellbeing, facilitates streamlined processes and ensures the production of high-quality products — all while simultaneously allowing for quick and easy modifications. It involves the optimal usage and placement of all resources including personnel, equipment, materials and storage space to facilitate a smooth workflow in the production process. Consideringthe savings that can be realized from a well-planned layout, and the heavy costs that can be incurred to address a poor facility design retrospectively, there’s a strong incentive for manufacturers to implement sound layout planning from the beginning of a new project 1.A well planned and optimized layout offers several benefits:It ensures the layout design is functional and achievable and prevents mistakes or surprises further along in the process. It streamlines the flow of materials through the plant, maximizing throughput while reducing material handling costs and capital bound in unfinished goods / inventories.It ensures the effective and efficient utilization of labor, equipment, and space, helping manufacturers to reduce both CapEx and OpEx while maximizing use of plant resources.Layout planning can and should be used whenplanning both new production projects or changes to existing production systems.The Layout Planning ProcessThere are many books, methodologies, and best practices on the subjects of layout, factory, and facility planning; this has been an area of interest by academics and practitioners since the 1950s. The methodology we present here is a simulation-based approach to layout planning, design, and optimization. It’s based on existing approaches to layout planning and grounded in on our experience of having worked with and supported several manufacturers on projects throughout the years. The methodology we’ve outlined in this guide consists of the following steps:1. Define the manufacturing program2. Equipment selection3. Initial layout design4. Define the flow5. Validate the model6. Layout optimization1DEFINE THEMANUFACTURING PROGRAMDefining the manufacturing program involves considering the key drivers for the project — such as customer requirements, sales expectations and product mix — in order to define the product portfolio and production requirements for the project, such as production volume and lead time.The next step is to define the structure of the manufacturing system 2. Here, planners determine the abstract and theoretical steps, as well as the resources that are required to assemble a product. This includes buffers, possible control strategies, the selection and definition of functional subsystems as well as the production principle.and Optimization with Visual Components1. Define the manufacturing program2. Equipment selection3. Initial layout design4. Define the flow5. Validate the model6. Layout optimization2 Hawer, S., Sager, B., Braun, H. and Reinhart, G. (2017). An Adaptable Model for the Factory Planning Process: Analyzing Data Based Interdependencies. ScienceDirect.The goal for this phase is to define the key elements of the layout: production principle and strategy, product and manufacturing requirements, and the production sectors. This will lay the groundwork for the next steps.2EQUIPMENT SELECTIONOftentimes, the next step is equipment selection, at least for critical or high-value equipment. In addition to manufacturing and material handling requirements, equipment must meet the project’s financial objectives, such as ROI and total cost of ownership. Operational Equipment Effectiveness (OEE) is another important and closely monitored metric tied to ROI.Depending on your organization’s budget and purchasing requirements, as well as the availability and diversity of equipment suppliers that cater to your applications, there may be a broad or narrow range of options available. Some projects have firm requirements for certain equipment brands or to use existing available equipment, while others offer more flexibility. For projects with tight cycle time requirements or space constraints, it might be necessary to first virtually validate the layout and workflow with the equipment. If you’re deciding between multiple equipment options, try to obtain the CAD models of the equipment that you’re considering. You can also check if the equipment is availableand Optimization with Visual Components1. Define the manufacturing program2. Equipment selection3. Initial layout design4. Define the flow5. Validate the model6. Layout optimizationin the Visual Components eCatalog, or request CAD data from the equipment suppliers. This way you’ll have accurately sized models when designing and analyzing the layout.Next, the quantity of equipment is estimated. In general, the goal is to achieve the project goals without overspending on equipment. Planners should take into account operational considerations, such as production volumes, number of SKUs, station setup times, planned downtime and maintenance, and shift models, in order to make an initial estimate on the quantity of equipment needed. This can be validated when simulating the workflow.Discover what’s available in the Visual Components eCatalog3INITIAL LAYOUTDESIGN1. Define the manufacturing program2. Equipment selection3. Initial layout design4. Define the flow5. Validate the model6. Layout optimizationOne of the first steps in layout design is to develop an accurate model of the space or facility. 2D drawings and point cloud models are both good sources of data that can be imported directly into Visual Components. Alternatively, you can model your space using Visual Component’s simple CAD modeling toolkit. If vertical clearance is a concern for the project, it’s important to accurately model the space in 3D.For larger projects, a next step might be to determine the space requirements for the production sections or areas. The size of production areas can be approximated via key figures for area estimation (e.g. for producing 200 parts per day, about 5,000 m 2 of space is necessary). These production areas should be designed and labeled in the layout.When it comes to designing the initial layout, the goal is to come up with a functional and achievable layout of equipment and resources that accommodates the production flow. Equipment should be placed in the correct position and orientation, and stations, walkways,buffers, fixtures, and spacing requirements should all be factored into the design. If the project includes human workers, they should also be included in the layout.With Visual Components, this initial layout can be designed using components from the eCatalog and/or CAD data, which you can import directly into software. It’s common to use comparable components from the eCatalog to represent processes and equipment that won’t have a material effect on validating the layout design but help to simulate the flow. CAD models can be used to represent fixtures and equipment, but they aren’t simulation-ready until their behaviors and properties have been defined in Visual Components, so appear as static. It’s common to use a combination of simulation-ready components and CAD geometry to design the initial layout.For some projects, especially robot cells, it’s often possible to design layouts using mostly components from the eCatalog.and Optimization with Visual Components4 DEFINETHE FLOW1. Define the manufacturing program2. Equipment selection3. Initial layout design4. Define the flow5. Validate the model6. Layout optimization In defining the production flow, planners mustspecify the production processes, the sequenceof processes, and the capacity and availabilityof resources and spaces. Planners should alsoconsider the following:Process times, batch sizes, control logic, shiftmodels, equipment setup / down times, andscheduled maintenance should all be modeledin.Routings for people, forklifts, and AGVs shouldbe defined.Feeder rates (or distributions) of parts enteringthe system and priority assignments for partsand / or resources should be checked.If there are processes with randomness ormeaningful variability (i.e. arrival of parts,loading/unloading times, process times, etc.),this should also be factored into the model.Another important consideration is the extentto which physics should be modeled into yoursimulation. The material properties of parts caninfluence important decisions such as batchsizes, equipment selection, handling procedures,and speeds / acceleration rates. If you believethe interactions of parts and resources inyour model could have a meaningful influenceon their kinematics or dynamics, then it’sadvisable to define their physical properties.Visual Components utilizes the NVIDIA PhysXphysics engine, which allows users to simulateand visualize functionality affected by physicalforces, such as collisions, gravity, and materialproperties.and Optimization with Visual Components5 VALIDATETHE MODEL1. Define the manufacturing program2. Equipment selection3. Initial layout design4. Define the flow5. Validate the model6. Layout optimization To validate the model means that theassumptions, operating philosophy, process flow,operating and material handling specifications,input data analysis, and runtime parameters forthe model have been accepted by the projectstakeholders. It should account for the variables,logic, boundary conditions, and special casesthat drive the model and outcomes. If the projectis to replace an existing production system, itcan also be used as a source to validate the newproduction system.An important consideration is ensuring themodel is focused on the correct level of detail.For example, if you’re planning to expand apalletizing cell in a large packaging facility, youmight create a model of only that cell and area,without including the upstream and downstreamprocesses (unless you’re also consideringchanges to the layout or operation of thoseprocesses).For many robotics applications, it’s helpful atthis stage to perform reachability, collision, andcycle time analyses of the different robot modelsunder consideration. If you’re still deciding betweendifferent robots, ensure your model works with thedifferent robot options. This includes using correctlysized end effectors and dimensioned cells andverifying the robot can perform the required taskstaking collision-free paths.You’ll likely have to iterate through several changesto the layout and / or production flow in order toarrive at a valid model. Especially for projects withmore complex requirements, the final model can lookvery different from the initial estimate.and Optimization with Visual Components6 LAYOUTOPTIMIZATION1. Define the manufacturing program2. Equipment selection3. Initial layout design4. Define the flow5. Validate the model6. Layout optimization With the validated model, you can conductexperiments to identify optimizations. Layoutscan be optimized to achieve a number ofimprovements, including:Reduce use of spaceReduce travel distance for parts andresourcesReduce investment in resources (equipment,labor, etc.)Reduce non value-added work, waste, andmaterial handling costsReduce WIP and minimize inventoriesImprove line balancingImprove flexibilityImprove safetyIncrease OEEThe improvements that can be realized fromlayout optimization can be substantial, and thereare many examples of manufacturers that haveused simulation to achieve significant savings innew manufacturing projects. Here’s a case studyabout a major white goods manufacturer thatused Visual Components to achieve impressiveresults in the design of a new a flexible assemblyline, including:15% reduction in total costs10% reduction in floor area45% reduction in labor20% improvement in line balancing10% increase in production capacityReduced reject ratio from 1200 defects permillion (dpm) to 120dpm20% reduction in construction scheduleIn some cases, typically projects with moreflexible production requirements, it’s useful toconsider multiple layout options. Taking thisscenario-based approach, planners test differentproduction scenarios (e.g. fluctuation of customerdemand, number of product variants, etc.) againstdifferent layout variants (e.g. low automation,medium automation, high automation), optimizeeach layout, then choose the best variant.and Optimization with Visual ComponentsLayout planning is part-art and part-science. Good planners areable to draw from experience, best practices, and oftentimes past mistakes, in order to have some good ideas on where to start with a new project. Great planners know the limits of their experience; they take a disciplined approach to the planning process and use data to inform their decisions.Using a combination of careful planning and simulation to designand optimize production layouts, manufacturers are able to achieve significant financial and operational benefits, including improved flexibility, reduced costs, and improved performance. Visual Components 3D manufacturing simulation software provides planners, engineers, and management with a powerful and easy-to-use platform on which to plan,design, optimize, and visualize production layouts.Midea GroupIncreasing the capacity and flexibilityof a high-end washing machineassembly lineVirtual ManufacturingDriving Sales of Lean ManufacturingProducts with 3D SimulationJSC Savushkin Product andConcern R-ProPackaging Automation in the DairyIndustryFFG FeelerDesigning and optimizing a FlexibleManufacturing SystemCambridge AutomationSimulationSimulate your production workflowO&O Technology and SMB TechnicA Winning Partnership Lands a KeyProject with an Automotive SupplierCORPORATE HEADQUARTERS Visual Components OyVänrikinkuja 2FIN-02600 Espoo, Finland Tel. +358 9 252 40800NORTH AMERICA Visual Components North America Corporation2633 Lapeer Road, Suite G Lake Orion, MI 48360 Tel. +1 586 873 0631GERMANYVisual Components GmbH Elsenheimerstrasse 61 80687 Munich, Germany Tel. +49 174 44 30008Want to find out how Visual Components 3D manufacturing simulation solutions can help you save time, reduce costs and improve production performance?GET A DEMO。
基于CorVis ST测量人角膜的弹性模量
( 1 天 津 大 学 机 械 工 程 学 院 力 学 系 3 0 0 3 5 0 天津； 2 天津市眼科医院天津市眼科学与视觉科学重点实验室3 0 0 0 2 2 天津）
摘 要 ：根据临床可视化角膜生物力学分析仪(CorVis ST)检測得到近视患者屈光手术前角膜动态变形
历程曲线，采用有限元数值模拟逆解方法确定其角膜在体有效弹性模量。采用计算流体动力学(CFD)
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测量角膜弹性力学性能的实验包括离体实验 和 在 体 实 验 。采 用 较 广 泛 的 离 体 实 验 有 单 轴 拉 伸 实 验 [M]和 膨 胀 实 验 [5]。单 轴 拉 伸 试 验 可 以 通 过 由 拉 伸 切割得到的角膜矩形条带获得角膜沿拉伸方向的应 力-应 变 关 系 。膨 胀 实 验 可 模 拟 角 膜 的 生 理 状 态 ，在 角膜的后表面施加压力使角膜变形，通过比较数值 模拟结果和实验结果逆解角膜材料参数。离体测量 可以加深对角膜生物力学特性的了解，但不同患者 角膜的弹性性能具有个体化差异，因此将离体实验 结 果 用 于 临 床 实 践 会 有 误 差 。在 体 实 验 方 法 众 多 ， 例 如 逐 步 压 痕 法 W、用 于 测 量 角 膜 杨 氏 模 量 的 剪 切 波成像技术[7],以 及 使 用 光 学 相 干 弹 性 成 像 (OCE) 测量角膜不同厚度位置的位移[8]等 。这些方法的缺 点是不能得到角膜的非线性弹性材料特性，只能测 量角膜局部的弹性性能。
考虑旅客到达准时性的城市值机移动站点动态分布模型
考虑旅客到达准时性的城市值机移动站点动态分布模型——张铭霞周航胡小兵167考虑旅客到达准时性的城市值机移动站点动态分布模型*张铭霞1,2周航1,2▲胡小兵1,3（1.中国民航大学体系安全和智能决策实验室天津300300；2.中国民航大学中欧航空工程师学院天津300300；3.中国民航大学安全科学与工程学院天津300300）摘要：现有城市值机移动服务站点设施分布模型在优化中未考虑旅客到达服务站点的时间不确定性，其优化结果通常与实际情况存在差异，导致无法对提前或延误到达的旅客进行服务。
为解决时间不确定性对优化求解造成的不利影响，研究基于旅客准时性概率函数的动态设施分布模型。
针对城市值机移动服务站点布局优化问题，构建完整的数学模型，并提出动态设施分布的优化评价指标。
采用正态分布型旅客准时性概率函数，用以预估旅客实际到站时间与申报到站时间的差异。
基于不同服务时段客源点的位置分布，采用涟漪扩散算法和遗传算法优化服务站点位置并计算所有旅客与站点间的最优路径。
基于天津市路网和旅客分布的真实数据，对旅客准时到站和考虑旅客到站时间不确定2种场景进行仿真对比实验。
结果表明：旅客到站时间概率模型优化结果优于旅客准时到站模型，动态设施分布评价指标提升4.31%。
其中，旅客到达站点的平均路径长度减少0.35%，旅客可接受距离总超出量减少6.26%，站点服务容量总超出量减少4.13%。
旅客到站时间概率模型能够充分考虑到站时间不确定性，并基于旅客实际到站时间更好地优化设施布局。
基于旅客准时性概率函数的城市值机移动服务站点动态分布模型具有可移植性，可应用于物流服务的动态选址等问题。
关键词：智能交通；城市候机楼；城市值机移动服务站点；涟漪扩散算法；遗传算法；正态分布；到站时间概率中图分类号：U121文献标识码：A doi：10.3963/j.jssn.1674-4861.2023.05.017A Dynamic Distribution Model of Urban Mobile StationsConsidering Passengers'Arrival PunctualityZHANG Mingxia1,2ZHOU Hang1,2▲HU Xiaobing1,3(boratory of Complex System Safety and Intelligent Decisions,Civil Aviation University of China,Tianjin300300,China;2.Sino-European Institute of Aviation Engineering,Civil Aviation University of China,Tianjin300300,China;3.College of Safety Science and Engineering,Civil Aviation University of China,Tianjin300300,China)Abstract:The existing distribution model for urban mobile stations(UMS)has not considered the uncertainty in the arrival times of passengers to the service stations,resulting in discrepancy between the optimization outcomes and practical scenarios.This discrepancy can cause the inability to provide services for early-arrival and delayed passen-gers.To address the detrimental impact of the time uncertainty on optimization solutions,A dynamic facility-distri-bution model based on the probability function of passengers'arrival punctuality is proposed.In response to the lay-out optimization problem of UMS,a comprehensive mathematical model and evaluation indexes for the optimiza-tion of dynamic facility distribution are proposed.A punctuality probability function with a normal distribution form is introduced to estimate the difference between passengers'actual and declared arrival times.Based on the location收稿日期：2023-03-12*中央高校基本科研业务费中国民航大学专项（2000530441）资助第一作者简介：张铭霞（1998—），硕士研究生.研究方向：空管智能决策.E-mail：********************▲通信作者：周航（1990—），博士，讲师.研究方向：计算智能，空管智能决策，计算电磁学.E-mail：***************.cn交通信息与安全2023年5期第41卷总246期0引言随着民用航空的发展，飞机出行已经在大家日常生活中越来越普遍。
A Virtual Assembly Design Environment
A Virtual Assembly Design EnvironmentSankar Jayaram, Yong Wang, Uma Jayaram School of Mechanical and Materials Engineering Washington State UniversityPullman, WA 99164-2920jayaram@Kevin Lyons, Peter HartManufacturing Systems Integration Division National Institute of Standards and TechnologyGaithersburg, MD 20899AbstractThe Virtual Assembly Design Environment (VADE) is a Virtual Reality (VR) based engineering application which allows engineers to evaluate, analyze, and plan the assembly of mechanical systems. This system focuses on utilizing an immersive virtual environment tightly coupled with commercial Computer Aided Design (CAD) systems. Salient features of VADE include: 1) data integration (two-way) with a parametric CAD system, 2) realistic interaction of user with parts in the virtual environment, 3) creation of valued design information in the virtual environment, 4) reverse data transfer of design information back to the CAD system, 5) significant interactivity in the virtual environment, 6) collision detection, and 7) physically-based modeling. This paper describes the functionality and applications of VADE. A discussion of the limitations of virtual assembly and a comparison with automated assembly planning systems are presented. Experiments conducted using real-world engineering models are also described.1. IntroductionTraditional automatic assembly planning has used the process of studying the disassembly process on the assumption that "if you can disassemble a part, you can assemble it, and vice versa." In a real-world physical situation, this may not be true due to irreversible fastening processes. Also, for a given product, the number of feasible assembly sequences explodes exponentially as the number of components increases. In addition, choices of optimal plan for disassembly may not represent the best for assembly. "Attempts to accelerate the process through the development of computer aided assembly planning systems have not, in general, been successful even when the design has been carried out using a modern CAD system. One of the main reasons for this lack of success is that assembly is dependent on a great deal of expert knowledge which has proven very difficult to formalize [7].” The development of applications of VR in engineering has opened up a powerful array of tools to solve this problem. Instead of abstract algorithmic assembly planning, an engineer can perform the assembly intuitively in the virtual environment using VR hardware and software. The information generated by this process can be used for assembly planning and verification.Virtual Assembly Design Environment (VADE) [6] is the result of a research and development project which was started in 1995 sponsored by the National Institute of Standards and Technology (NIST). The main purpose of the project is to explore the potential and technical challenges in the use of virtual reality technologies in design and manufacturing by creating a virtual environment for assembly planning and evaluation. This system has been created successfully and is being evaluated using test cases from industry.Previous publications describing VADE [4, 6, 9, 15] have described initial ideas, prototype implementations, and specific methods implemented. In this paper, we describe the overall system, the important features, and examples of use of this system. We also discuss the benefits and limitations of virtual assembly systems. A comparison of virtual assembly systems and automated assembly planning systems is also presented.2. Related WorkRepresentative work on automatic assembly planning can be found in [10, 16]. A successful numerical method to generate swept volume was introduced in [13].Collision detection research can be found in [5, 8, 17].Physically based modeling and dynamic simulation research is described in [2, 11, 15].VR and CAD system interaction has been described in [1]. The system described in [1] allows limited design modifications in the virtual environment through a CAD system (ProEngineer TM ). However, the application is limited by the level of interactivity in the system.In the field of virtual assembly and virtual prototyping,several systems have been created [7, 12, 14]. Jayaram et.al. [9] described a prototype of the Virtual Assembly Design Environment (VADE). The work discussed in this paper evolved from the prototype VADE.3. Virtual Assembly Design EnvironmentVADE has been designed and implemented at Washington State University in collaboration with NIST [4, 9]. The overall system concept is illustrated in Figure 1. Once the engineer designs the mechanical system using any parametric CAD system (e.g., Pro/Engineer), VADE automatically exports the necessary data to the virtual environment through a user selected option in the CAD system.In the virtual environment, the user is presented with an assembly scene. The various parts are initially located where they would be in the real assembly plant (this is user defined). The VR user can then perform the assembly. This enables the user to make decisions, make design changes, and perform a host of other engineering tasks in the virtual environment.During this process, the virtual environment maintains a link with the CAD system and uses the capabilities of the CAD system wherever required. At the end of the VADE session, the user would have generated valued design information which is then automatically made available to the designer in the CAD system.The VADE architecture is based on object-oriented design concepts and methods. There are eight modules in the system:a) InteractionManager : Harmonizes all the modules and features of the whole system;b) InputManager : Obtains user input including tracking data, glove data, keyboard data, etc;c) OutputManager : Creates and updates the graphics display and manages the scene graph;d) ModelManager : Obtains assembly model and environment model information from the CAD system;e) CollisionManager : Provides real-time collision detection;f) SweptManager : Creates and controls the editing of swept volumes and part trajectories;g) DesignManager : Allows the user to perform parametric design modifications in the virtual environment and integrates these modifications with the CAD system; andh) DynamicHandler : simulates dynamic behaviors of the parts.VADE has been created to be capable of supporting a variety of VR peripheral devices. The test cases at Washington State University used an implementation with an SGI Onyx2 processor with 6 processors and two Infinite Reality pipes, Flock of Birds, Cyberglove, and a VR4 head mounted display. Valuable collaborative work was performed with an implementation at NIST that used an ImmersaDesk.CAD EnvironmentVADE EnvironmentDesign for AssemblyAnalysisTrajectory Information,Sequence Information,Suggested Design ChangesTrajectory Information,Sequence InformationVirtual Reality Based Training Systems Computer Aided Process Planning Robot Path Planning SystemsSpecialized Assembly Equipment DesignSystemsPart Geometry,Assembly Information Part Attributes,Tolerances, etc.Figure 1. VADE Usage Scenario [9].4. Representative Features and Capabilitiesof VADE1)Automatic data translation from CAD to VR: Theassembly tree, the assembly constraints, and the geometry of the parts and subassemblies are automatically translated from the parametric CAD system (Pro/Engineer) to the virtual environment. 2)Creation of realistic environment and initiallocation of parts: The user can specify any assembly environment. This environment can be defined in the CAD system or can be imported from any other system using one of the many industry file formats (Inventor, Multigen, etc.). The initial location and orientation of the parts are specified by creating coordinate systems in the CAD system and transferring the information to the virtual environment.3)Two-handed assembly and dexterous mani-pulations: VADE supports both one-handed and two-handed assembly. One of the two hands can be dexterous with a glove device (e.g., Cyberglove).The non-dexterous hand is used to grab and manipulate the “base” sub-assembly on to which the other parts are being assembled. For the dexterous hand, new algorithms have been created and implemented to allow realistic gripping of parts using physics-based modeling. This allows fine motor manipulations of the part held by the gloved hand.Support for tactile feedback is also built into the system.4)Capturing assembly intent from CAD system andusing it in the virtual environment: Modern CAD systems are aimed at capturing the designer’s intent.For the assembly process, intent is specified through the constraints used to assemble the system. VADE assumes that the model has been created with the physical assembly intent in mind. This information is used to constrain and create kinematic motions during virtual assembly. This information is also used to create valuable assembly sequence information.5)Constrained motion simulation: VADE simulatesinter-part interaction for planar and axisymmetric parts using constrained motions along axes/planes.These axes and planes are obtained as part of the assembly design intent from the CAD system. This allows simulation of sliding, rotating, etc., without compute-expensive numerical methods (Figure 2). 6)Interactive dynamic simulation of parts: VADEsupports real time collision detection, simulation of dynamic behaviors of the parts held in the user’s hand, dynamic interactions between the user, the parts, the base part, and the environment objects,simulation of ballistic motion of the objects in space, and simulation of dynamic behaviors of the parts while constrained on the base part (Figure 3). All this is achieved using physically based modeling.The properties for the parts are obtained directly from the CAD system.7)Swept volume generation and trajectory editing:VADE allows the user to record the trajectory of thepart as it is being assembled. This trajectory can be edited within the virtual environment. The swept volume of the part is created using numerical methods and can be viewed in the virtual environment. This volume can also be sent back to the CAD system as a feature of the assembly (Figure4).8)Parametric design modifications in the virtualenvironment: The designer can tag model parameters in the CAD system. The tagged parameters are extracted by VADE and presented to the VADE user. These parameters can be modified Figure 3. Dynamic simulation. The pendulum like part is rotating about the shaft andtranslating along the shaft axis.Figure 2. Constrained motion simulation. Thegrabbed part (indicated by the arrow) is onlyallowed to move along the axes during insertion.in the virtual environment using a 3D Graphical User Interface (GUI) (Figure 5). The modified parameters are sent back to the CAD system where the part is regenerated using all the variational and parametric relations defined by the designer. The modified part is re-loaded into VADE. All this happens in near real-time without the user ever leaving the virtual environment. This allows quick design changes and “what-if” evaluations during the assembly evaluation process.All of the above-described capabilities can function together or individually. The system is in constrained motion simulation mode by default since it is the basic functionality which guides the assembly operation. Other capabilities, such as swept volume generation, trajectoryediting, collision detection, design modifications, and dynamic simulation are optional. The user can switch these capabilities on and off during the session as desired.One scenario is the use of swept volume and collision detection together. By using collision detection alone, we can determine whether the part can be assembled safely without interfering with other parts or environment objects. By using swept volume and collision detection together, we can also find where the interference will occur. This identifies the exact instances of the trajectory that are colliding with the assembly or the environment object. This information is valuable for identifying solutions to the problem or for editing the trajectory (Figure 6).5. Assembly Evaluation and PlanningVADE was created to perform or assist assembly design evaluation, analysis, and assembly sequence planning. It can fulfil the design needs at all product realization stages: assembly plan verification (pre-product evaluation), maintenance verification, and alternative plan searching (post-production evaluation).Assembly plan verification lets the user perform the assembly in a pre-defined sequence. In VADE, the user assembles the parts one by one in the virtual environment using constrained motion, swept volume, and collision detection. If there is any interference detected during the process, the user can try to find a way to get around it.Maintenance verification lets the user check the design for disassembly. If a part needs to be taken out for maintenance, e.g. a spark plug or an oil filter, we need to ensure a clear path of disassembly. There are two ways to achieve this using VADE. One way is to try to remove the part from its final position and check for collision detection during the process. The other way is to create a swept volume of the path during the disassembly process,assemble all the other parts, and check the interference of the swept volume with other parts. By observing the created swept volume, we can even get a feel for how much space is available to perform the operation.Figure 4. Swept volume generated in CAD. The generated swept volume has a parametricrepresentation.Figure 5. Parametric design modifications in VADE. The user selects the virtual menu button to modify the diameter of the indicated part.Figure 6. Swept volume creation with collision checks.Sometimes it is necessary to find alternative plans or sequences for operations that are already being carried out in the workshop. It is also common to post-evaluate an assembly operation. Stopping the assembly line to perform the testing is not economical and very few alternatives can be tried out in the limited time available. VADE provides a viable alternative where assembly experts can try various alternatives, choose the best one, suggest design changes, suggest fixturing changes, perform ergonomic evaluations, etc. All of the results of these evaluations in VADE can be presented back to the designer in the original CAD environment. This allows the designer to quickly perform design changes without any other data translation.6. Test Cases and ResultsTo test and verify the performance and capabilities of the virtual assembly design environment, several assembly test cases have been carried out. Several test cases have also been carried out using the VADE implementation at NIST. In this paper two of those test cases are presented to demonstrate the validity of using this system for assembly evaluations. The basic goal of these two tests was to compare the time required to carry out an assembly in a virtual environment with the actual assembly time. A small-scale assembly and a large size assembly are presented. The predicted assembly time was calculated using Boothroyd methods [3]. In each virtual assembly operation, two sets of data were recorded: total time to perform the assembly and total "pure" assembly time for all the parts. For a part, "pure" assembly time represents the time from the moment the part is grabbed to the moment the part is placed on the base part. The time a user spends on reaching and trying to grab the parts (gripping time) can be obtained by the difference between the total time and pure assembly time.The small assembly was a tele-printer with seven parts (Figure 7). The larger assembly was a truck front axle assembly (Figure 8). The axle model had 17 parts and sub-assemblies and represented a real-world industry case. For the axle assembly, a full-scale model and a half-scale model were used. The half-scale model was used not only because it can provide the information on the impact of scale factors in virtual assembly, but also because it can be treated as a mid-sized assembly model. The test results are shown in Appendix A in Table-A1 and Table-A2. Analyzed data is shown in Table-A3.From the test data and the analyzed data in the tables, several interesting and important observations can be made:1)Pure assembly time in a virtual environment islower than actual assembly time (about 10-15%).This can be attributed to the lack of fastening operations in the virtual environment.2)Pure assembly time for each part in the assemblyincreases with the physical size of the part. This is because the difficulty of handling the parts increases with the size of the parts. For example, it takes more time to manipulate and align a truck wheel than it does a tape spool on a printer. This physical reality was adequately reflected in the virtual environment.3)Average gripping time for each part in the assemblyremains almost the same for different sizes of the parts. Although gripping difficulty does depend on the shape of the part (a thin, long shaft is more difficult to grab than a cubic block), gripping time mainly depends on practice and experience in the virtual environment. For new users, it takes much longer to grab a part. Providing touch feedback did not have an effect on the gripping time. It is anticipated that providing grip feedback (e.g.Cybergrasp) will influence the gripping time.4)When considering the relationship of pure assemblytime and total assembly time, the correlation coefficient is low for large assembly (0.7 for large assembly, 0.98 for half size large assembly and 0.9 for small assembly). This indicates that humanconsiderations start influencing the assembly time Figure 7. A tele-printer assembly model in VADE.Figure 8. A truck axle assembly.for larger models. As the assembly models become larger, the user needs to walk some distance to grab the part, find better viewing positions to look at the model, and align the parts, etc. Some of the truck axle assemblies were performed by simulating a “hoist” with one user manipulating the part and another one guiding the manipulation. This seemed to work better (as it does in the real world).Besides the quantitative information, qualitative information was also obtained. The virtual assembly of course does not require physical prototypes. The immersion in the full-size assembly provides intuitive and valuable information that is impossible to obtain in conventional CAD assembly modeling. The test case also illustrated other potential capabilities such as training, workspace study, operation time study, etc., in VADE. In addition to providing visualization of the assembly process, virtual assembly is a viable tool to study, analyze, evaluate, and improve the assembly design and operation processes.7. DiscussionWith the assistance of all the capabilities of VADE, the user can perform assembly design evaluation, maintenance verification, alternative assembly plan searching, and part design modification as described above. Since the system involves the experience and actions of the human, the plans generated from this system automatically include the input from the knowledge of experienced engineers. However, there are some limitations of the system.Since the environment is achieved in a full immersion mode using a Head Mounted Display (HMD), it is difficult to put the user in the environment for a long time as it tires the user. Hence this system is not suitable for assemblies with a large number of parts. However, we can get around this problem by combining parts into sub-assemblies in some cases. For example, when a chassis assembly is evaluated, the radiator and the engine can be brought into the environment as single sub-assemblies and treated as a single part even though each is made of several hundred parts.Overall, virtual assembly evaluation and planning is suitable for complex assembly operations that involve human beings. Automatic assembly planning systems are suitable for assembly models with a large number of parts where the assembly operations are simple (only involve translation and one axis rotation) and usually performed by robots. In many cases, a combination of the two might be the best solution. The automatic assembly planning system can be used to find some feasible assembly process plans. The expert assembler could then enter the virtual assembly environment for evaluation and verification, consideration of practical problems related to the realization of the assembly design, and optimization.8. Summary and ConclusionsVADE presents a complete scenario for assembly design. Multiple parts can be manipulated efficiently for assembly evaluations. Constrained motion simulation and dynamic simulation assist the assembly evaluation operation. The overall process is simulated realistically by mimicking the physical assembly processes. Dynamic behaviors of objects in the virtual environment are implemented using physical laws. Dynamic simulation increases realistic feeling, but its contribution to the assembly evaluation operations is still unclear.Interactive editing of assembly path and swept volume directly by the user is achieved in the virtual environment. The editing includes swept instance addition, removal, and modifications of positions and orientations. The editing of the swept volume before the assembly geometry is finalized ensures the validity and significance of the swept volume. The swept volume is also converted to a parametric model and loaded back into the CAD system for further evaluation. Collision detection functionality is also provided in the system.Bi-directional interaction is achieved between VADE and CAD systems. For simple parts, the interaction cycle is almost real-time. For sophisticated parts with more than 100 dimensions, the interaction speed is slower (about 1 minute).Test cases have been carried out with models from industry. Results from VADE compare very well with results from the Boothroyd methodology [3] (which is widely used in industry) for predicting assembly time.A significant deviation from reality occurs in the process of gripping the part. This occurs primarily from the “sluggishness” of VR systems created by tracking frequency, tracking latency, frame rates, and graphics latency. This sluggishness does not seem to affect gross motor movements (moving a part into place and aligning it) except in acute situations with large databases. However, it significantly affects fine motor movements(e.g., finger and wrist movements).9. AcknowledgementsThis research is supported by NIST grant no. 60NANB5D0066. The collision detection software used in VADE were derived from I-Collide and RAPID from the University of North Carolina.10. DisclaimerCommercial product or company names in this paper are given for informational purposes only. Their use does not imply recommendation or endorsement by the National Institute of Standards and Technology.11. References1.S. R. Angster, and S. Jayaram, "Open ArchitectureFramework for Integrated Virtual Product Development Systems,” International Journal of Virtual Reality, Vol. 3, No. 1, 1997.2. D. Baraff, "Dynamic Simulation of Non-PenetratingRigid Bodies,” Ph.D. Thesis, Cornell University, 1992.3.G. Boothroyd, P. Dewhurst and Winston Knight,"Product design for Assembly and Manufacture,”Marcel Dekker, Inc., New York City, NY 1994.4.H. Chandrana, “Assembly Path Planning UsingVirtual Reality Techniques,” MS thesis, School of Mechanical and Materials Engineering, Washington State University, May 1997.5.J. Cohen, M. Lin, D. Manocha and K. Ponamgi, "I-Collide: An Interactive and Exact Collision Detection System for Large Scaled Environment,” Proceedings of the ACM International 3D Graphics Conference, pp189-196, 1995.6.H. Connacher, S. Jayaram, and K. Lyons, “VirtualAssembly Design Environment,” Proceedings of 1995 Computers in Engineering Conference, Boston, MA, September 1995.7.R. G. Dewar, I. D. Carpenter, J. M.Ritchie and J. E.Simmons, "Assembly planning in a Virutal Environment,” Proceedings of PICMET, Portland, 1997.8.S. Gottschalk, M.C. Lin, and D. Manocha, "OBB-Tree: A Hierarchical Structure for Rapid InterfaceDetection,” Technical Report TR96-013, Dept. of Computer Science, UNC, 1996.9.S. Jayaram, H. Connacher, and K. Lyons, “VirtualAssembly using Virtual Reality Techniques,”Computer-Aided Design, Vol. 29, No. 8, August 1997.10.S. Kaufman, R. Wilson, R. Jones, T. Calton and A.Ames, "The Archimedes 2 Mechanical Assembly Planning System,” Proceedings of 1996 IEEE International Conference on Robotics and Automation, pp. 3361-3368.11. B. Mirtich, "Impulse Based Dynamic Simulation ofRigid Body Systems,” Ph.D. Dissertation, Department of Computer Science, Univ. of California, Berkeley, 1996.12.J. Oliver and R. Kuehne, "A Virtual Environment forInteractive Assembly Planning and Evaluation,”Proceedings of ASME Design Automation Conference, 1995.13.W. J. Schroeder, H. Martin and B. Lorensen, "TheVisualization Toolkit- an Object Oriented Approach to 3D Graphics,” Prentice Hall, Engelwood Cliffs, NJ, 1996.14.M. Schulz, Th. Ertl and Th. Reuding, "Crashing inCyberspace-Evaluating Structural Behavior of Car Bodies in a Virtual Environment,” Proceedings of VRAIS98, Atlanta, GA March 1998.15.Y. Wang, “Physically Based Modeling in VirtualAssembly,” Ph.D. Dissertation, Washington State University, December 1998.16.R. Wilson, "Tool Reasoning in the MechanicalAssembly Planning System,” Proceedings 1996 IEEE International Conference on Robotics and Automation, pp. 2567-2575.17.G. Zachmann, “Rapid Collision Detection byDynamically Aligned DOP-Trees,” Proceedings of VRAIS98, Atlanta, Georgia, 1998.Appendix ATest Case (printer)"Pure" AssemblyTime (PAT)Total AssemblyTime (TT)Gripping Time(GT=TT - PAT)Percentage timeGripping(GT/TT)1431015857.4% 2451035856.3% 346985253.6% 4501308061.5% 5431036058.2% 6531327959.8% 7491287961.7% Average47.0113.666.658.4% Table-A1: Recorded time for small assembly (in seconds)Test Case (axle) Half/Full Size "Pure" AssemblyTime (PAT)Half Size Full SizeTotal AssemblyTime (TT)Half Size Full SizeGripping Time(GT=TT - PAT)Half Size Full SizePercentage ofGripping(GT/TT)Half Size Full Size1264 332 431 521167 18938.7% 36.3% 2242 353422 518180 16542.6% 31.8% 3318 347537 534219 18740.8% 35.2% 4237 375398 523161 14840.4% 28.3% 5229 395390 582161 18741.3% 32.1% 6267 337462 509195 17242.2% 29.2% 7239 339411 542172 20341.8% 37.5% Average256 354436 533179 17941% 33% Table-A2: Recorded time for large assembly with full size and half-size (in seconds)Test Cases PAT /SD TT/SD r of PATand TT GT PAT/GTeach partActual Timefrom [3]Printer47.0/3.8113.6/15.50.90366.6 7.8/11.155.2Axle(half Size)256/31436/510.983179 16.0/11.2Axle(full size)354/23533/240.701179 22.1/11.2403.8Table-A3: Analysis of test data (time in seconds). Key: PAT-pure assembly time; TT-total assembly time; GT-gripping time; r-correlation coefficient; SD-standard deviation。
非均质油藏井网注采参数优化的可视化模拟驱替实验
非均质油藏井网注采参数优化的可视化模拟驱替实验王东英;任熵;蒋明洁;张航艳【摘要】油藏储层的非均质性是影响油、水渗流规律及采收率的内在因素,注采参数是影响非均质油藏采收率的外在因素,可视化模拟驱替实验是确定非均质油藏合理注采参数的有效手段.利用可视化动态仿真驱替实验装置进行水驱油的驱替实验,通过设计7套采用不同的注采参数的实验方案,研究非均质油藏的物性差异、井网形式以及注采参数对水驱程度的影响.研究结果表明,对于非均质油藏,注水开发初期,渗流阻力主要为油对水的阻力,驱替受储层非均质性影响不明显,随着驱替不断进行,孔隙喉道的阻力成为主要阻力,驱替越来越不均衡.对比不同实验方案,通过优化调整非均质油藏的井网形式和注采参数,可以实现最大程度的均衡驱替,改善水驱油藏开发效果,获得更高的采收率和经济效益.%The heterogeneity of oil reservoir is the major internal factors affecting the oil-water seepage and oil and gas recovery of the reservoirs. The injection and production parameters have a great influence on recovery of heterogeneous reservoirs, and visualization study of microcosmic model is an effective method of determining the reasonable injection and production parameters of heterogeneous reservoirs. In this paper, seven experimental schemes are designed according to different injection-production methods and injection-production rate. Conducting water-driving-oil displacement experiments using visual dynamic simulation displacement experiment device, we study the influence on water flooding degree by physical property difference, well-pattern type and injection and production parameters of heterogeneous reservoirs. The results show that the change of waterinjection mode and injection and productionrnparameters in high and low permeability areas have a great influence on recovery of heterogeneous reservoirs. The high recovery can bernobtained by flooding according to certain ratio and surrounding equilibrium flooding in high and low permeability areas.【期刊名称】《油气地质与采收率》【年(卷),期】2013(020)002【总页数】4页(P95-98)【关键词】采收率;注采参数;非均质油藏;可视化;驱替实验【作者】王东英;任熵;蒋明洁;张航艳【作者单位】中国石油大学(华东)石油工程学院,山东青岛266580【正文语种】中文【中图分类】TE313.7随着油田开发的不断深入，可视化模拟技术被广泛应用于剩余油分布和注采参数优化研究中［1］。
U形管换热器尾部支撑结构的应用
U形管换热器尾部支撑结构的应用董玉群 (中国石化工程建设公司北京100101) 摘要:针对U 形管换热器尾部无支撑跨距大于 GB 151 规定的最大无支撑跨距时,U 形管尾部如何支撑问题,介绍了石化装置建设工程中常用的2 种结构形式,并对不同结构形式的制作过程进行了介绍,对其优缺点进行了比较。
关键词: U 形管换热器; 尾部; 支撑结构 1 问题的提出 GB 151 - 1999《管壳式换热器》对 U 形管换热器尾部支撑提出了的要求是 [ 1 ] : U 形管换热器中,靠近弯管段起支撑作用的折流板(图1) ,结构尺寸 A + B + C之和应不大于最大无支撑跨距(见 GB 151 - 1999 表42) ,超过表中数值时,应在弯管部分加特殊支撑。
在工程实际中,结构尺寸 A + B + C 之和大于 GB 151 规定的最大无支撑跨距的情况时有发生, 但 GB151 并没有给出“特殊支撑”的具体结构,较难处理;笔者提供了2 种“特殊支撑”结构形式。
2 支撑结构所谓特殊支撑结构,就是将所有换热管尾部通过一定的方式连接起来,减少各换热管之间由于流体冲击而产生振动和碰撞,避免管束产生破坏。
本文介绍2 种在工程中应用过的“特殊支撑” 结构,均以<19 的换热管按正三角形排列为例,换热管间距25mm ,最内排 U 形管弯曲半径40 mm。
2. 1 A型支撑结构由内往外,在相邻两排 U 形管弯曲部分之间, 并排插入 2 根直径为 8 mm 的圆钢(见图 2、图 3 的 A - A 剖面图给出了较多层管束时的情况,且图3 仅绘制了一半,另一半以B - B 轴对称) ,在管束外将所有圆钢用 2 块扁钢(夹持板) 进行固定, 以达到固定换热管的目的。
因相邻 2 排换热管外壁距离仅 2. 65 mm ,为了能够插入圆钢,需要将外层换热管的直管段长度增加 6 mm。
具体制造过程为,由内往外,每安装一层 U 形管,插入 2 根圆钢,直到最外层换热管配管技术石油化工设计 Pet rochemical Design 2009 ,26 (1) 56～57 安装完毕,最后将所有圆钢端部都焊接在夹持板上,达到固定圆钢的目的。