Modeling cascading failures in the North American power grid

A study on casing deformation failure during multi-stage hydraulic fracturing for the stimulated reservoir volume of horizontal shale wellsZhanghua Lian a,*,Hao Yu a,Tiejun Lin a,Jianhua Guo ba State Key Lab of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu610500,Chinab Research Institute of Gas Recovery Engineering,PetroChina Southwest Oil and Gas Field Company,Guanhan618300,Chinaa r t i c l e i n f oArticle history:Received24November2014 Received in revised form26January2015Accepted26February2015 Available online10March2015Keywords:Stimulated reservoir volume Shale gasCasing deformation failure Numerical simulationRock damage mechanics Stress deficit a b s t r a c tVolume fracturing technique has effectively helped develop unconventional oil and gas reservoirs in recent years.At the same time,new problems of casing deformation failure occurred.Based on the drilling and well completion data,microseismic surveillance data,theories of fracture mechanics,rock damage mechanics and rock failure criterion,this paper established afinite element model of the for-mation of effective stimulated reservoir volume,including clustering perforation casing for X-1h shale gas horizontal well,to address the problems.The research results indicate:1)the stress deficit of zero stress areas and tension stress areas occurred within the range of stimulated reservoir volume during the process of volume fracturing.And,the state of this stress deficit,which would make clustering perfo-ration casings of horizontal wells“hanging”in the formation to some extent,resulted in certain degree of deflection deformation radically and S-shape deformation axially.2)the problem of casing deformation failure remains fundamentally unsolvable through simply improving casing grade and wall thickness to increaseflexural strength.3)the key to solve casing deformation failure is the reasonable spacing design of multi-stage fracturing.The methods and achievements in the paper provide theoretical supports for the popularization and application of shale stimulated reservoir volume and controlling the S-shape deformation failures of the horizontal multi-cluster perforation casing.©2015Elsevier B.V.All rights reserved.1.IntroductionIn recent years,volume fracturing has become an effective technique for the development of unconventional oil and gas res-ervoirs(Wang et al.,2012,2014a).Using volume fracturing mea-sures,the stimulated reservoir volume(SRV)can be realized to achieve industrial production volume(Wang et al.,2015).In2006, Mayerhofer et al.(2006)first mentioned the concept of the Stim-ulated Reservoir Volume(SRV).Later on,other experts in thisfield (Cipolla et al.,2009;Mayerhofer et al.,2010;Wu et al.,2011;Chen et al.,2012)developed the concept and revealed its basic contents, optimization design and the implementation methods.Meanwhile, Wu et al.(2011)clearly presented a new concept of volume frac-turing technique,i.e.the technique can break up reservoirs to form complicated fracture networks,and“create”artificial permeability. This technique successfully breaks the traditional fracturing seepage theory model and greatly shortens the effective seepage distance and is especially applicable to the stimulation of highly brittle rock layer.Meanwhile,multi-stage and multi-cluster perfo-ration modes are also applied(Wu et al.,2012).The SRV theory completely subverts the traditional fracturing theory(Wu et al., 2011).The volume fracturing technique no longer forms symme-try bi-wing fractures,but generates complex fracture networks. Also,this theory illustrates that fracture initiation and extensions are not a simple tension-fracture,but have completed mechanics behaviors with shear failure,leap and slip(Chipperfield et al., 2007).This volume fracturing was put into use for thefirst time in the multi-stage and multi-cluster SRV process of W201well and W201-H1well in Weiyuan,Sichuan Province,China,and good stimulation effect was obtained(Wu et al.,2012).After that,this technique was applied and developed in the SRV process of ultra-low permeability and tight oil reservoirs in Changqing oilfield, Jilin oilfield,Tarim oilfield,Southwest oil-gasfields and Sulige gas*Corresponding author.State Key Lab of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu610500,China.E-mail address:milsu1964@(Z.Lian).Contents lists available at ScienceDirectJournal of Natural Gas Science and Engineering journal ho mep age:www.elsevier.co m/lo cate/jngse/10.1016/j.jngse.2015.02.0281875-5100/©2015Elsevier B.V.All rights reserved.Journal of Natural Gas Science and Engineering23(2015)538e546field in China(Chen et al.,2007;Ye et al.,2013;Tang et al.,2013).2.A new problem in volume fracturing-casing deformation failureA new problem of casing deformation failure has occurred during multi-stage hydraulic fracturing of SRV.Due to the charac-teristics of big fracturing volume,excessive stimulated segments and big pumping delivery rate in the volume fracturing process, there will exist complicated mechanical behaviors such as shear failure,leap and slip around the horizontal casing and the change of in-situ stressfield(Chipperfield et al.,2007;Hossain et al.,2007). All these problems will frequently lead to casing deformation fail-ure and make it difficult to run subsequent completion tools. Consequently,normal well completion operations and stimulation treatments can't be performed,which would seriously influence stimulation effect(Chen et al.,2007;Hossain et al.,2007;Yang et al.,2013;Tang et al.,2013;Yu et al.,2014;Brantley et al.,2014).Daneshy(2005)showed that during the fracturing operations, casing could fail under tension across the fractured interval either by de-threading of the collars of the casing or by tensile failure at perforations.He presented the main factors contributing to this failure are off-balance fracture growth,and pseudo-openhole environment;other contributing factors are borehole inclination with respect to the fracture plane,and the quality of the cement bond.Furui et al.(2010)developed a comprehensive liner-deformation model to analyze casing failure in fracturing and acidizing.Their computed results indicated that Their computed results indicated that fracturing and acidizing can lead to the compaction effect,borehole instability and casing deformation.Yu et al.(2014)constructed a three-dimensionalfinite element model of casing failure during the multi-layer fracturing of vertical wells. According to the comparison of MIT multi-arms logs after frac-turing,casing failure is coefficient results of rock properties decrease,asymmetry treatment zones,high fracturing pressure and terrestrial stressfield redistribution.Wang et al.(2014b)indicated that local buckling,crushing,connection failure and shear failure are the main modes of casing failure.The researches above have demonstrated the effect of traditional hydraulic fracturing on cas-ing damage from different perspectives.However,studies on casing failure in multi-stage hydraulic fracturing of SRV are still relatively few.In this work,we analyzed the information of a shale gas hori-zontal well in Sichuan,well X-h1,when serious casing failure occurred during its volume fracturing process.Based on micro-seismic monitoring data,areas affected by rock-cracking,tectonic map around casing,in-situ stress data,casing size and so on,using finite element analysis software,mechanical models for the research on the mechanism of casing damage of horizontal well volume fracturing could be established.Throughfinite element numerical modeling research and analysis under different condi-tions,wefigured out the forms,reasons and mechanisms of the casing failure,and presented the related protection and control measures for the casing damage.3.Finite element modeling(FEM)of horizontal section volume fracturing3.1.Basic data of X-1h wellX-1h is a horizontal shale gas well in West China,with measured depth(MD)of2649m and horizontal section length of 1250m,as shown in Fig.1.In the horizontal section,casing dimension is139.7mmÂ9.17mm with a steel grade of P110.The material parameters of casing and cement sheath are shown in Table1.Twelve-stage volume fracturing was adopted in this well.Each stage of multi-cluster perforation had an effective length of20m and the spacing of adjacent fracturing sections was80m,as shown in Fig.1.Each section was injected about2000m3fluids into the layer at theflow back rate of9.8%and maximum pump pressure of 64MPa.On the basis of microseismic information,some layers appeared repetitive volume fracturing,as shown in Fig.2.After volume fracturing,during the process of drilling bridge plug,the block of F117mm milling shoe at the depth of2331.5m evidenced the possibility of casing failure.The casing failure section was located in thefifth fracturing section,which was the biggest stimulated volume.Therefore,finite element(FE)model must contain all the information of this section.3.2.Basic theory of rock damage and division of volume fracturing spread rangeDuring each staged fracturing process,pump pressure would rise with the increase of delivery capacity,andfluid seepage pres-sure that forced on areas of volume fracturing would also go up, which changed the formation stressfield.Whereas,the changing stressfield could result in formation damage of stimulated areas and decrease of rock mechanical properties(Zhao et al.,2012). Based on microseismic principles,it can be considered that any signal of microseismogram is the initiation fracture of a microcrack that continues to expand(Dong and Gao,2004).However,it is impossible to accurately depict the changes of rock mechanical properties that appeared after the generation of each microcrack. Therefore,it is assumed that the rock mass in themicroseismic Z.Lian et al./Journal of Natural Gas Science and Engineering23(2015)538e546539areas during each staged fracturing process is a homogeneous fracture cube (Tian,2007).According to the related theories of fracture mechanics and several fracture criteria,we use cohesive element type to simulate the decrease of rock mechanical proper-ties (Turon et al.,2006).The critical stress criterion is Eq.1s n s max n2þs ss max s2þs ts max t2¼1(1)The linear degradation criterion of cohesive element elasticitymodulus is Eq.2E ¼ð1Àd ÞÂE 0(2)The formula to calculating the damage factor d is Eq.3d ¼d f m Âd max m Àd 0md max m  d f m Àd 0m(3)The location of casing failure is at 2331.5m,which lies between the fourth perforation and fifth perforation.Based on themicroseismic monitoring images,the third to sixth staged frac-turing section can be found closely related to the casing failure.As the research object is casing,we use some basic geometric shapes to represent the volume fracturing spread range on microseismic image of each stage near the casing.While those signals that are far from casing or isolated from each other are neglected in the model for their little in fluence on casing deformation.The use of basic geometric shapes can provide convenience for subsequent mesh-ing.As shown in Fig.3,there are ellipses and rectangles in the planar projection of microseismic monitoring images of X-1h well and the division range is displayed in Fig.3a e d.These areas are viewed as homogeneous cracks,which are directly under fracturing fluid pressure and have decreased rock mechanical properties.3.3.Finite element modeling and meshingOn the basis of the above-mentioned theory and microseismic monitoring data,the fracture areas with crevices in third to sixth microseismic results as displayed in Fig.3are drawn in a coordinate paper,showing in bined with geological data,borehole trajectory data,completion technology and the working condition of multi-stage fracturing,a FEM of multi-cluster perforation casing is established to simulate the third to sixth staged fracturing pro-cess of horizontal section formation with the length of 643m,the width of 476m and the thickness of 200m.This model contains horizontal section with MD of 2035m e 2678m,as shown in Fig.5.In Fig.5,the elliptic cylinders and rectangular columns are frac-turing areas,and the superimposed zones indicate repeated frac-turing areas.Theoretical research and practice show that rock mechanical parameters of re-fracturing areas willdecreaseFig.1.Schematic diagram of X-1h basic data.Table 1Material parameters of casing and cement sheath.MaterialElastic modulus/GPa Poisson's ratio Cohesion/MPaFriction angle/Yield strength/MPa Casing 210.00.30758.0Cement15.00.2312.026Table 2Fracturing operation parameters of X-1h.StageWell section (m)Hydrostatic pressure (MPa)Working pressure (MPa)Pressure gradient (MPa/m)Max.Min.32556e 246515.056.551.60.02442465e 237015.062530.02452370e 227015.064590.02462270e 217015.065580.024Z.Lian et al./Journal of Natural Gas Science and Engineering 23(2015)538e 546540obviously,especially stress field variation,after two or more than two fracturing treatments.We can see from Fig.4that the casing failure locates in the re-fracturing areas.Besides,we can know from Fig.5a that fracturing stimulation areas is extremely unsymmetri-cal.Non-uniform load appears around the multi-cluster perforation casing and put casing into complicated stress conditions after fracturing.In FEM of Fig.5,the range of the formation model is 643Â476Â200m.However,the size of casing diameter is only 0.1397m and the ratio of casing diameter to its length is 2.1710À4,which means it is dif ficult and arduous for meshing.To address this issue,first partition and subdivide the complex three-dimensional regions,such as microseismic areas,casing,cement sheath and overlaps,into simpler regions that the automatic mesh generator can mesh.Then control the global mesh size,and a local re finement is applied for casing.Finally,using Arbitrary Lagrange-Euler (ALE)adaptive mesh for casing to provide control of mesh distortion,and structured or sweep technique to generate global mesh.According to the function “Verify mesh ”,an estimate of mesh quality is good.After the intelligent meshing technical process,the partial enlarged meshing of cement sheath and casing is shown in Fig.5b.For the formation,casing and cement sheath in the areas that is not frac-tured,we use C3D8P element type.For the crack in fractured areas,cohesive element can be used to simulate the rock damage after each stage fracturing,and the meshed model has 354,827nodes and 336,390elements in Fig.5b.Through the comparison of computed results after mesh re finement,the calculation accuracy can be improved with the increasing mesh density but the in flu-ence is very little.Therefore,the node number is suf ficient for this particular problem.3.4.Boundary and initial conditionsThe six facets of the model in Fig.5are the far field boundary conditions.According to the logging information,the Prede finedField function is applied to calculate the in-situ stress distribution before multi-stage fracturing which is shown in Fig.6.In the finite element numerical simulation,the in-situ stress will be automati-cally redistributed toward the volume fracturing areas around the wellbore.In Fig.6,the direction of minimum horizontal in-situ stress is parallel to the axial direction of borehole and the value of three-dimensional stresses varies with MD.The average minimum hori-zontal in-situ stress is 30Mpa,combining with the average vertical in-situ stress of 35Mpa and the average maximum horizontal in-situ stress of 60Mpa,indicates the heterogeneity of in-situ stress field is not strong.As shown in Fig.6,the in-situ stress distribution is relatively uniform.On this condition,casing is under good me-chanical environment and the in-situ stress field has little in fluence over casing failure.3.5.Basic data of numerical simulation in SRV fracturingThis paper has set up multi-analysis steps to simulate each stage of the volume fracturing with FE software.In the process of volume fracturing,the basic parameters of fracturing fluid pressure in casing and corresponding crack areas (pore pressure)are shown in Table 2.For re-fracturing areas which would be subjected to two or more repeated fracturing treatments under fracturing fluid pres-sure of different stages,the decreased elasticity modulus around the casing will be calculated with Eqs.(1)e (3)after each stage volume fracturing.By use of the keyword *Field,mechanical property values of formation that decrease after rock damage are reset to materials of repeated fracturing areas.4.Analysis of the FE numerical simulationBased on the finite element model,including clustering perfo-ration casing (as shown in Fig.5)in this paper,the in-situ stress field in the areas with more microseisms has redistributed for many times under the effect of fracturing fluid pressure (pore pressure)by the numerical simulation of third to sixth stage volume frac-turing.The casing is affected by variable terrestrial stress field,whose magnitude and direction in the whole model changes dramatically,even the “tension stress ”areas and “zero stress ”areas occur in the range of the volume fracturing (the circle surrounded by red (in the web version)curve,as shown in Fig.7).In this paper,these areas are called “stress de fict areas ”and the one where simulated reservoir volume fracturing “broken up ”reservoirs.Fig.7shows the vector distribution contours of 3D in-situ stress field after sixth staged fracturing.By contrast of the analysis of the stress field in Figs.7and 6,the maximum value of three-dimension stress near the microseismic areas varies from 36.3MPa,47.2MPa,72.7MPa e 73MPa,52.4MPa,116.0MPa,which means the nu-merical value and heterogeneity degree of the in-situ stress field increase signi ficantly.The collapsing strength of the casing de-creases with the increasing three-directional non-uniform compression forces,which makes the casing in a worse mechanical environment.In some microseismic areas,due to strong fracturing pressure and breakage of initial formation volume,in-situ stress disappears temporarily,then,tension stress areas and zero stress areas occur in the formation with broken volume under complex mechanical environment (the positive values of three-dimensional stress,as shown in Fig.7).The appearance of tension stress areas is favorable to the increase of crack width and conductivity of oil and gas,while “stress de ficit ”areas leads the horizontal casing to “overhang ”in volume fracturing formation in different degrees.Then the asymmetry of SRV areas made in-situ stress transverse shear force acting on casing and the formation,which leads to casing deformation along the radial direction to some extent.TheFig.2.Collective diagram of microseismic surveillance data of Total 12stages in X-1h.Z.Lian et al./Journal of Natural Gas Science and Engineering 23(2015)538e 546541FE results in this paper show in some multiple refracturing areas,rock mechanical properties,including modulus of elasticity,pois-son's ratio and compressive strength sequentially decrease,accompanying with the successive increase of heterogeneity and de ficiency of in-situ stress field and the enhancement of shear ef-fect,meanwhile,radial elliptical deformation and axial S-shape deformation of casing gradually strengthen.All these changes will result in casing deformation failure,which makes it dif ficult to lower completion tools,causing the failure of stimulation treat-ment according to its deformed section.Fig.8is showing the axial deformation and Von Mises stress distribution contours of casing in the horizontal section after the third-to-sixth staged fracturing.FE analysis reveals that in the process of multi-staged volume fracturing operation,terrestrial stress field constantly redistributes,which also leads to the mixed “tensile stress ”and “compressive stress ”.Under the shear effect ofstress field and the gravity effect of suspending casing in “stress de ficit ”section,deformation failure often occurs.In the sections of unbroken formation,the casing has little deformation,while in other sections,casing is in fluenced by variable stress field.There-fore,there will appear several “S ”shape deformation failures in the whole horizontal segment of the casing.From Fig.8,we can see the axial deformation of the casing.In Fig.8,the maximum stress of 760.3MPa,which is beyond the casing yield strength of 758.0MPa,occurs near the location of casing deformation failure at 2331.5m,indicating that the casing not only occurs large deformation,but also plastic failure.In order to study the deformation failure mechanism of the casing cross-section direction during multi-stage hydraulic frac-turing,the ellipticity (Yu et al.,2014)of casing calculated by Eq.(4)after sixth stage volume fracturing is shown in Fig.9.The maximum ellipticity was only 1.2%,found at 2320e 2340m.Because oftheFig.3.Planar diagram of each stage's microseismic surveillance data of X-1h well.Z.Lian et al./Journal of Natural Gas Science and Engineering 23(2015)538e 546542comparatively small ellipticity of the casing's section,in this case,the casing oval deformation is not a main factor that prevents the access of running tools.z ¼2ðD max ÀD min ÞðD max þD min ÞÂ100%(4)Therefore,we obtained the curvature and displacement of cas-ing deformation by A e A (vertical in-situ stress direction)and B e B (maximum horizontal in-situ stress direction)of the casing cross section with MD from FE numerical results,as shown in Fig.10.The bending deformations are clearly found on the segments of the horizontal casing.The maximum displacement of 44mm appears somewhere near 2335m in A e A direction,with a large curvature and small casing segment length;in B e B direction,there emerges distortion of displacement,the maximum displacement is 40mm,and the casing becomes an obvious S-shape,as shown in Fig.10b.This results in the dif ficult access to the S-shape casing for long rigid tubular string.Based on the field investigation of X-1h well,using F 117mm milling shoe to mill bridge plug was blocked when tool ran into 2331.5m.The bottom outside milling shoe was worn seriously after taken out,as shown in Fig.11a;there were also tiny iron filings in the mud ditch,as shown in Fig.11b.The casing was thought as deformation failure.Then,a lead impression block (LIB)was run to help determine its nature.The LIB has a malleable lead baseinFig.4.Collective diagram of microseismic surveillance data of 3rd to 6th stages in X-1hwell.Fig.5.The FE model of the SRV formation including clustering perforation casing of 3rd to 6th stages in X-1h.Fig.6.The in-situ stress field vector distribution contours.Z.Lian et al./Journal of Natural Gas Science and Engineering 23(2015)538e 546543which the deformed casing can leave an impression when they meet.The deformation failure was con firmed through the squashed side of LIB,as shown in Fig.11c.By contrast,the FE results showing in this paper are the same to the actual working condition.There-fore,the current method and FEM are correct.The study above shows that the casing deformation failure is mainly resulted from the increasing non-uniform degree of the three-directional compression load,caused by the changing terrestrial stress field during multi-stage fracturing process.Therefore,increasing the flexural strength by simply increasing steel grade and wall thickness cannot radically solve the failure of axial S-shaped deformation of casing.In order to achieve industrial production of SRV,delivery ca-pacity and pump pressure can't be changed,so the reasonable spacing design of multi-stage fracturing is the key to solve casing deformation failure.The optimal spacing design based on geolog-ical data,rock properties and in-situ stress filed,should be applied instead of the simple uniform one,and adjusted timely on the basis of micro-seismic monitoring results,which makes the terrestrial stress load on the casing as uniform as possible.5.Conclusions(1)The constructed FEM is based on rock damage mechanics and fracturing areas data from microseismic monitoring.The FEM can be applied to effectively and quantitatively study casing deformation failure during multi-stage hydraulic fracturing for SRV of horizontal shale wells.(2)After FE numerical study of the third-to-sixth staged frac-turing,it can be known that there will be the “tensile stress ”areas and “zero stress ”areas in the fracture areas after sixth staged volume fracturing.Although the existence of tensile stress areas is favorable for increasing fracture width and flow conductivity,the “stress de ficit ”areas will make casing remain in suspending state and lead to radial deformation.(3)Some areas receive refracturing treatment,leading the for-mation rock mechanical properties to decrease,the in-situ stress field non-uniform degree and stress de ficit areas to increase,the shear effect to increase,and the radial ellipse deformation and axial S-shaped deformation of casing to increase at the same time.All these changes will result in casing deformation failure.(4)Increasing the flexural strength by simply increasing steel grade and wall thickness cannot radically solve the failure of axial S-shaped deformation of casing.The key to solve casing deformation failure is the reasonable spacing design ofmulti-Fig.7.The vector distributions of formation three dimensional in-situ stress field after 6th stagedfracturing.Fig.8.Von Mises stress ofcasing.Fig.9.Changed curves of casing's ellipticity with well depth.Z.Lian et al./Journal of Natural Gas Science and Engineering 23(2015)538e 546544stage fracturing,which makes the terrestrial stress load on the casing as uniform as possible.AcknowledgmentsThe authors thank the National Natural Science Foundation of China (No.50774063)and the Research Fund for the Doctoral Program of Higher Education of China (No.20135121110005),for their contributions to this paper.ReferencesBrantley,S.L.,Yoxtheimer, D.,Arjmand,S.,Grieve,P.,Vidic,R.,Pollak,J.,Llewellyn,G.T.,Abad,J.,Simon,C.,2014.Water resource impacts during un-conventional shale gas development:the Pennsylvania experience.Int.J.Coal Geol.126,140e 156.Chen,M.,Qian,B.,Ou,Z.,Zhang,J.,Jiang,H.,Chen,R.,2012.Exploration and Practiceof Volume Fracturing in Shale Gas Reservoir of Sichuan Basin,China.IADC/SPE 155598.Chen,Z.,Wang,Z.,Zeng,H.,2007.Status quo and prospect of staged fracturingtechnique in horizontal wells.Nat.Gas Ind.27(9),78e 80.Chipper field,S.T.,Wong,J.R.,Warner,D.S.,Cipolla,C.L.,Mayerhofer,M.J.,Lolon,E.P.,Warpinski,N.R.,2007.Shear Dilation Diagnostics:a New Approach for Evalu-ating Tight Gas Stimulation Treatments.SPE 106289.Cipolla,C.L.,Lolon,E.P.,Mayerhofer,M.J.,Warpinski,N.R.,2009.Fracture 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reservoir.Acta Pet.Sin.Sin.33(Suppl.1),149e 158.Wang,Y.,Watson,R.,Rostami,J.,Wang,Y.,Limbruner,M.,He,Z.,2014b.Study ofborehole stability of Marcellus shale wells in longwall mining areas.J.Pet.Explor.Prod.Technol.4(2014),59e 71.Wu,Q.,Xu,Y.,Wang,T.,Wang,X.,2011.The revolution of reservoir stimulation:anintroduction of volume fracturing.Nat.Gas Ind.31(4),7e 12.Fig.10.Curves of bending deformation of casing after 6th staged fracturing varied with welldepth.Fig.11.Field images of casing deformation after 6th staged fracturing.Z.Lian et al./Journal of Natural Gas Science and Engineering 23(2015)538e 546545。

1. artificial intelligence 人工智能2. paper-tape reader 纸带阅读器3. optical computer 光计算机4. neural network 神经网络5. instruction set 指令集6. parallel processing 并行处理7. difference engine 差分机8. versatile logical element 通用逻辑元件9. silicon substrate 硅衬底10. vacuum tube 真空管11. 数据的存储与处理the storage and handling of data12. 超大规模集成电路very large-scale integrated circuit13. 中央处理器central processing unit14. 个人计算机personal computer15. 模拟计算机analogue computer16. 数字计算机digital computer17. 通用计算机general-purpose computer18. 处理器芯片processor chip19. 操作指令operating instructions20. 输入设备input device1. artificial neural network 人工神经网络2. computer architecture 计算机体系结构3. robust computer program 健壮的计算机程序4. human-computer interface 人机接口5. knowledge representation 知识表示6. 数值分析numerical analysis7. 程序设计环境programming environment8. 数据结构data structure9. 存储和检索信息store and retrieve information10. 虚拟现实virtual reality1. data field 数据字段，数据域2. learning curve 学习曲线3. third-party solution 第三方解决方案4. Windows Media Player Windows媒体播放器5. 开始按钮Start button6. 指定输入区designated input area7. 手写体识别系统handwriting-recognition system8. 字符集character set1. function key 功能键，操作键2. voice recognition module 语音识别模块3. touch-sensitive region 触敏区4. address bus 地址总线5. flatbed scanner 平板扫描仪6. dot-matrix printer 点阵打印机（针式打印机）7. parallel connection 并行连接8. cathode ray tube 阴极射线管9. video game 电子游戏10. audio signal 音频信号11. 操作系统operating system12. 液晶显示（器）LCD (liquid crystal display)13. 喷墨打印机inkjet printer14. 数据总线data bus15. 串行连接serial connection16. 易失性存储器volatile memory17. 激光打印机laser printer18. 磁盘驱动器disk drive19. 基本输入/输出系统BIOS (Basic Input/Output System)20. 视频显示器video display1. interrupt handler 中断处理程序2. virtual memory 虚拟存储（器），虚存，虚拟内存3. context switch 上下文转换，语境转换4. main memory 主存（储器）5. bit pattern 位模式6. 外围设备peripheral device7. 进程表process table8. 时间片time slice9. 图形用户界面graphical user interface10. 海量存储器mass storage1. code generator 代码生成程序，代码发生器2. abstract machine 抽象机3. program editor 程序编辑程序，程序编辑器4. configuration item 配置项5. 计算机辅助设计CAD (computer-aided design)6. 数据冗余data redundancy7. 指挥与控制系统command and control system8. 视频压缩与解压缩video compression and decompression1. storage register 存储寄存器2. function statement 函数语句3. program statement 程序语句4. object-oriented language 面向对象语言5. assembly language 汇编语言6. intermediate language 中间语言，中级语言7. relational language 关系（型）语言8. artificial language 人工语言9. data declaration 数据声明10. SQL 结构化查询语言11. 可执行程序executable program12. 程序模块program module13. 条件语句conditional statement14. 赋值语句assignment statement15. 逻辑语言logic language16. 机器语言machine language17. 函数式语言functional language18. 程序设计语言programming language19. 运行计算机程序run a computer program20. 计算机程序员computer programmer1. native code 本机（代）码2. header file 头标文件；页眉文件3. multithreaded program 多线程程序4. Java-enabled browser 支持Java的浏览器5. malicious code 恶意代码6. 机器码machine code7. 汇编码assembly code8. 特洛伊木马程序Trojan horse9. 软件包software package10. 类层次class hierarchy1. bar chart 条形图2. frequency array 频率数组3. graphical representation 图形表示4. multidimensional array 多维数组5. 用户视图user(’s) view6. 下标形式subscript form7. 一维数组one-dimensional array8. 编程结构programming construct1. inference engine 推理机2. system call 系统调用3. compiled language 编译执行的语言4. parallel computing 并行计算5. pattern matching 模式匹配6. memory location 存储单元7. interpreter program 解释程序8. library routine 库程序，程序库例行程序9. intermediate program 中间程序，过渡程序10. source file 源文件11. 解释执行的语言interpreted language12. 设备驱动程序device driver13. 源程序source program14. 调试程序debugging program15. 目标代码object code16. 应用程序application program17. 实用程序utility program18. 逻辑程序logic program19. 墨盒ink cartridge20. 程序的存储与执行program storage and execution1. messaging model 消息接发模型2. common language runtime 通用语言运行时刻（环境）3. hierarchical namespace 分层名称空间4. development community 开发界5. CORBA公用对象请求代理（程序）体系结构6. 基本组件base component7. 元数据标记metadata tag8. 虚拟机virtual machine9. 集成开发环境IDE（integrated development environment）10. 简单对象访问协议SOAP（Simple Object Access Protocol）1. procedure call 过程调用2. fault tolerance 容错3. homogeneous system 同构系统4. autonomous agent 自主主体5. 路由算法routing algorithm6. 异构型环境heterogeneous environment7. 多址通信协议multicast protocol8. 通信链路communication(s) link1. system specification 系统规格说明2. unit testing 单位（或单元、部件）测试3. software life cycle 软件生命周期（或生存周期）4. system validation testing 系统验证测试5. evolutionary development process 演化开发过程6. simple linear model 简单线性模型7. program unit 程序单元8. throwaway prototype 抛弃式原型9. text formatting 正文格式编排，文本格式化10. system evolution 系统演变11. 系统设计范例system design paradigm12. 需求分析与定义requirements analysis and definition13. 探索式编程方法exploratory programming approach14. 系统文件编制system documentation15. 瀑布模型waterfall model16. 系统集成system integration17. 商用现成软件commercial off-the-shelf (或COTS) software18. 基于组件的软件工程component-based software engineering (CBSE)19. 软件维护工具software maintenance tool20. 软件复用software reuse1. check box 复选框，选择框，校验框2. structured design 结构化设计3. building block 积木块，构建模块，构件4. database schema 数据库模式5. radio button 单选（按）钮6. 系统建模技术system modeling technique7. 模型驱动开发model-driven development8. 数据流程图data flow diagram9. 下拉式菜单drop-down (或pull-down) menu10. 滚动条scroll bar1. procedural language 过程语言2. common design structure 通用设计结构3. class and object interaction 类与对象交互4. design constraint 设计约束5. 设计模式design pattern6. 可复用软件reusable software7. 面向对象的系统object-oriented system8. 继承层次inheritance hierarchy1. end user 最终用户，终端用户2. atomic operation 原子操作3. database administrator 数据库管理员4. relational database model 关系数据库模型5. local data 本地数据6. object-oriented database 面向对象数据库7. database management system (DBMS) 数据库管理系统8. entity-relationship model (ERM) 实体关系模型9. distributed database 分布式数据库10. flat file 平面文件11. 二维表two-dimensional table12. 数据属性data attribute13. 数据库对象database object14. 存储设备storage device15. 数据类型data type16. 数据插入与删除data insertion and deletion17. 层次数据库模型hierarchical database model18. 数据库体系结构database architecture19. 关系数据库管理系统relational database management system (RDBMS)20. 全局控制总线global control bus1. nonvolatile storage system 非易失性存储系统2. equipment malfunction 设备故障3. wound-wait protocol 受伤―等待协议4. exclusive lock 排它锁，互斥（型）锁5. database integrity 数据库完整性6. 共享锁shared lock7. 数据库实现database implementation8. 级联回滚cascading rollback9. 数据项data item10. 分时操作系统time-sharing operating system1. base class 基（本）类2. data library 数据（文件）库3. data access stack 数据存取栈4. database-specific functionality 数据库特有的功能5. 默认设置default setting6. 异常处理程序exception handler7. β测试beta testing8. 桥接提供程序bridge provider1. microwave radio 微波无线电2. digital television 数字电视3. DSL 数字用户线路4. analog transmission 模拟传输5. on-screen pointer 屏幕上的指针6. computer terminal 计算机终端7. radio telephone 无线电话8. cellular telephone 蜂窝电话，移动电话，手机9. decentralized network 分散型网络10. wire-based internal network 基于导线的内部网络，有线内部网11. 光缆fiber-optic cable12. 传真机fax machine13. 无线通信wireless communications14. 点对点通信point-to-point communications15. 调制电脉冲modulated electrical impulse16. 通信卫星communication(s) satellite17. 电报电键telegraph key18. 传输媒体transmission medium (或media)19. 无绳电话cordless telephone20. 金属导体metal conductor1. bit map 位图，位映象2. parallel port 并行端口3. direct memory access (DMA) 直接存储器存取4. universal serial bus 通用串行总线5. general-purpose register 通用寄存器6. 电路板circuit board7. 串行通信serial communication8. 数码照相机digital camera9. 存储映射输入/输出memory-mapped I/O10. 有线电视cable televisionUnit Seven/Section CI. Fill in the blanks with the information given in the text:1. Transfer2. cells3. label4. integratedII. Translate the following terms or phrases from English into Chinese and vice versa:1. high-definition TV 高清晰度电视2. frame relay 帧中继3. data rate 数据（速）率4. metropolitan area network 城域网5. 机顶盒set-top box6. 多模光纤multi-mode fiber7. 协议堆栈protocol stack8. 虚拟路径标识符VPI (virtual path identifier)1. file server 文件服务器2. carrier sense 载波检测，载波监听3. protocol suite 协议组，协议集4. peer-to-peer model 对等模型5. bus topology network 总线拓扑网络6. inter-machine cooperation 机器间合作，计算机间合作7. Ethernet protocol collection 以太网协议集8. proprietary network 专有网络9. utility package 实用软件包，公用程序包10. star network 星形网络11. 局域网local area network (LAN)12. 令牌环token ring13. 无线网络wireless network14. 封闭式网络closed network15. 环形拓扑结构ring topology16. 客户机/服务器模型client/server model17. 网络应用程序network application18. 进程间通信interprocess communication19. 打印服务器print server20. 广域网wide area network (W AN)1. routing path 路由选择通路2. dual-ring topology 双环形拓扑结构3. extended star topology 扩展星形拓扑结构4. backbone network 基干网，骨干网5. mesh topology 网格拓扑结构6. 同轴电缆coaxial cable7. 逻辑拓扑结构logical topology8. 无冲突连网环境collision-free networking environment9. 树形拓扑结构tree topology10. 目的地节点destination node1. destination address 目的地址2. performance degradation 性能退化（或降级）3. four-interface bridge 4接口网桥4. common bus 公共总线，公用总线5. 数据链路层data-link layer6. 协议转换器protocol converter7. 开放式系统互连OSI (Open Systems Interconnection)8. 物理地址physical address1. cell phone 蜂窝电话，移动电话，手机2. IP address 网际协议地址，IP地址3. autonomous system 自主系统4. dial-up connection 拨号连接5. network identifier 网络标识符6. binary notation 二进制记数法7. mnemonic name 助记名，缩写名8. Internet-wide directory system 因特网范围的目录系统9. name server 名称服务器10. Internet infrastructure 因特网基础结构11. 助记地址mnemonic address12. 网吧cyber cafe13. 宽带因特网访问broadband Internet access14. 顶级域名top-level domain (TLD)15. 因特网编址Internet addressing16. 点分十进制记数法dotted decimal notation17. 因特网服务提供商Internet service provider (ISP)18. 专用因特网连接dedicated Internet connection19. 主机地址host address20. 硬件与软件支持hardware and software support1. incoming message 来报，到来的报文2. application layer 应用层3. utility software 实用软件4. sequence number （顺）序号，序列号5. remote login capabilities 远程登录能力6. 端口号port number7. 软件例程software routine8. 传输层transport layer9. 文件传送协议FTP（File Transfer Protocol）10. 万维网浏览器Web browser1. wildcard character 通配符2. Copy command 复制命令3. search operator 搜索算符4. home page 主页5. 回车键Enter key6. 搜索引擎search engine7. 嵌入代码embedded code8. 超文本标记语言Hypertext Markup Language1. mailing list 邮件发送清单，邮件列表2. proprietary software 专有软件3. cc line 抄送行4. bcc line 密送行5. forwarded e-mail messages 转发的电子邮件6. e-mail convention 电子邮件常规7. click on an icon 点击图标8. confidential document 密件，秘密文件9. classified information 密级信息10. recovered e-mail message 恢复的电子邮件11. 常用情感符commonly used emoticon12. 已删除电子邮件deleted e-mail13. 电子系统electronic system14. 附件行Attachments line15. 版权法copyright law16. 电子邮件网规e-mail netiquette17. 信息高速公路information superhighway18. 签名文件signature file19. 电子数据表程序spreadsheet program20. 文字处理软件word processor1. web-authoring software 网络写作软件2. template generator 模版生成程序3. navigation page 导航页面4. corporate logo 公司标识5. splash page 醒目页面，过渡页6. 导航条navigation bar7. 节点页面node page8. 网站地图site map9. 可用性测试usability testing10. 图形交换格式gif（Graphics Interchange Format）Unit Ten/Section CI. Fill in the blanks with the information given in the text:1. technical2. participation3. openness; sharing4. prosumers; wikiII. Translate the following terms or phrases from English into Chinese and vice versa:1. instant messaging 即时通信，即时消息2. content syndication 内容聚合3. user-friendly interface 用户友好界面，方便用户的接口4. Weblog-publishing tool 博客（或网志）发布工具5. 应用程序编程接口application programming interface (API)6. 基于因特网的外包Internet-based outsourcing7. 软件升级software upgrade8. 版本号version number1. customized marketing strategy 定制的营销策略2. B2G transaction 企业对政府交易3. mobile telephone 移动电话4. dot-com bust 网络不景气5. smart card 智能卡，灵巧卡6. digital piracy 数字盗版7. dot-com boom 网络繁荣8. C2C transaction 消费者对消费者交易9. Web auction site 拍卖网站10. fingerprint reader 指纹读取器11. 射频识别装置radio-frequency identification (RFID) device12. 电子数据交换electronic data interchange (EDI)13. 库存管理技术inventory management technology14. 知识产权intellectual property15. 条形码bar code16. 货币兑换currency conversion17. 电子图书electronic book18. 视网膜扫描仪retina scanner19. 个人数字助理personal digital assistant (PDA)20. 企业对企业电子商务B2B electronic commerce1. software suite 软件套件2. text box 文本框3. virtual checkout counter 虚拟付款台4. static catalog 静态目录5. browser session 浏览器会话期6. 动态目录dynamic catalog7. 购物车软件shopping cart software8. 供应链supply chain9. 企业资源计划软件enterprise resource planning (ERP) software10. 税率tax rateUnit Eleven/Section CI. Fill in the blanks with the information given in the text:1. credit2. downloading; in-store3. malls4. hackersII. Translate the following terms or phrases from English into Chinese and vice versa:1. privacy policy 隐私政策2. identity theft 身份（信息）盗取3. affiliate marketing 联属网络营销4. postal money order 邮政汇票5. 零售网站retail website6. 信用卡credit card7. 货到付款cash on delivery8. 安全套接层Secure Sockets Layer1. encryption program 加密程序2. deletion command 删除命令3. authorized user 授权的用户4. backup copy 备份5. voltage surge 电压浪涌6. circuit breaker 断路器7. electronic component 电子元件（或部件）8. data-entry error 数据输入错误9. electronic break-in 电子入侵10. power line 电力线，输电线11. 检测程序detection program12. 电源power source13. 破坏性计算机程序destructive computer program14. 计算机病毒computer virus15. 软件侵权software piracy16. 硬盘驱动器hard-disk drive17. 病毒检查程序virus checker18. 主存储器primary storage19. 电子公告板electronic bulletin board20. 浪涌电压保护器surge protector1. phishing attack 网络钓鱼攻击2. graphics card 显（示）卡3. heuristic analysis 试探性分析4. infected file 被感染文件5. virus dictionary 病毒字典6. 数据捕获data capture7. 恶意软件malic ious software8. 病毒特征代码virus signature9. 防病毒软件antivirus software10. 内存驻留程序memory-resident program1. system platform 系统平台2. install back doors 安装后门3. email attachment 电子邮件的附件4. vulnerability assessment tool 脆弱性评估工具5. 网络安全措施network security measure6. 系统维护人员system maintenance personnel7. 邮件交换记录MX record8. 非法闯入系统hack a system。

表因果的英语作文Title: Exploring Cause and Effect Relationships。

In the realm of human understanding, cause and effect relationships form the very fabric of our comprehension of the world. From the simplest occurrences to the most complex phenomena, we constantly seek to unravel the connections between events and their consequences. In this essay, we delve into the significance of cause and effect relationships, their role in various domains, and the methods used to analyze and comprehend them.First and foremost, understanding cause and effect relationships is pivotal in grasping the fundamental principles governing natural phenomena. In the scientific domain, cause and effect serve as the cornerstone of experimentation and hypothesis testing. Scientists meticulously design experiments to isolate variables and elucidate the causal relationships between them. Through rigorous observation, data collection, and analysis, theyuncover the intricate mechanisms underlying natural processes.Moreover, cause and effect relationships permeate numerous aspects of human existence, extending beyond the confines of scientific inquiry. In social sciences such as psychology, sociology, and economics, researchers probe the interplay between various factors to discern patterns of behavior, societal trends, and economic dynamics. By identifying causal links, scholars gain insights into human cognition, social interactions, and the mechanisms driving economic growth or decline.In everyday life, individuals intuitively navigate through a web of cause and effect relationships, albeit often unconsciously. From mundane decisions like choosing what to eat for breakfast to significant life choices such as career paths or relationships, individuals weigh the potential consequences of their actions. While some causal connections may seem straightforward, others unfold in complex and unpredictable ways, posing challenges to our understanding and decision-making processes.Analyzing cause and effect relationships requires a multifaceted approach, drawing upon various methodologies and analytical tools. Statistical techniques such as regression analysis, correlation studies, and structural equation modeling provide quantitative insights into the relationships between variables. Qualitative methods such as case studies, interviews, and content analysis offer in-depth understanding and context to complex causal dynamics.Furthermore, advances in technology and data analytics have revolutionized the study of cause and effect relationships, enabling researchers to analyze vast amounts of data with unprecedented precision. Machine learning algorithms, for instance, can identify hidden patterns and causal connections within large datasets, shedding light on intricate relationships that might elude human perception.However, despite our best efforts, discerning causality remains a daunting challenge in many instances. The inherent complexity of natural systems, coupled with the presence of confounding variables and nonlinear dynamics,often complicates causal inference. In such cases, researchers resort to probabilistic reasoning, causal modeling, and counterfactual analysis to approximate causal relationships and mitigate uncertainty.Moreover, cause and effect relationships are not always unidirectional or linear; they can exhibit feedback loops, cascading effects, and emergent properties, giving rise to nonlinear dynamics and system-wide changes. Understanding such complex causal structures requires interdisciplinary collaboration, drawing insights from diverse fields ranging from systems theory and complexity science to philosophy and epistemology.In conclusion, cause and effect relationshipsconstitute the cornerstone of human understanding, permeating scientific inquiry, social analysis, and everyday decision-making. By unraveling the intricate connections between events and their consequences, we gain deeper insights into the mechanisms governing the natural world and human behavior. While analyzing cause and effect relationships poses challenges and uncertainties, itremains a fundamental endeavor in our quest to comprehend the complexities of existence.。

Qualitative SimulationBenjamin KuipersIn Encyclopedia of Physical Science and Technology,Third Edition.Robert A.Meyers,Editor-in-Chief.Academic Press,2001,pages287–300.Qualitative simulation predicts the set of possible behaviors consistent with a qualitative differential equation model of the world.Its value comes from the ability to express natural types of incomplete knowl-edge of the world,and the ability to derive a provably complete set of possible behaviors in spite of the incompleteness of the model.A qualitative differential equation model(QDE)is an abstraction of an ordinary differential equation, consisting of a set of real-valued variables and functional,algebraic and differential constraints among them.A QDE model is qualitative in two senses.First,the values of variables are described in terms of their ordinal relations with afinite set of symbolic landmark values,rather than in terms of real numbers. Second,functional relations may be described as monotonic functions(increasing or decreasing over par-ticular ranges)rather than by specifying a functional form.These purely qualitative descriptions can be augmented with semi-quantitative knowledge in the form of real bounding intervals around unknown real values and real-valued bounding envelope functions around unknown real-valued functions.Qualitative and semi-quantitative models can be derived by composing model fragments and collecting the associated modeling assumptions.Qualitative simulation starts with a QDE and a qualitative description of an initial state.Given a qual-itative description of a state(called a qstate),it predicts the qualitative state descriptions that can possibly be direct successors of the current state description.Repeating this process produces a graph of qualitative state descriptions,in which the paths starting from the root are the possible qualitative behaviors.The graph of qualitative states is pruned according to criteria derived from the theory of ordinary differential equations, in order to preserve the guarantee that all possible behaviors are predicted.Abstraction methods have also been developed to simplify the resulting qualitative behaviors.The resulting graph of qualitative states(the behavior graph)can still be quite large,requiring automated methods based on temporal logic model-checking to determine whether the qualitative prediction implies a desired conclusion.Conclusions derived in this way can be used in the design and validation of dynamical systems such as controllers.A set of qualitative models and their associated predictions can also be unified with a stream of observations to monitor an ongoing dynamical system or to do system identification on a partial model.Ongoing research topics include qualitative simulation and abstraction methods,the use of various types of quantitative knowledge,automated ways to determine the conclusions to draw from a predicted behavior graph,design and verification methods,online monitoring frameworks,and modeling methods suited forQualitative Simulation2 particular application domains.The specific notations in this article are those used in the QSIM representa-tion[20,21],but the concepts covered include the related ideas from[8,12,30,31].1The Qualitative Model RepresentationLike an ordinary differential equation,a qualitative differential equation model consists of a set of variablesrelated by constraints.(Figure1shows an example of the QSIM code for a QDE describing a simple U-tubesystem consisting of two tanks,A and B,connected by a thin channel.)A variable represents a continuouslydifferentiable function over the extended real number line,,including.However,in a QDE model,the range of each variable,including the independent variable time,is described qualitatively by aquantity space.A quantity space is afinite,totally ordered set of symbolic landmark values representingqualitatively important values in the real number line(seefigure1).Every quantity space includes landmarksfor zero and positive and negative infinity.A purely qualitative model specifies only the ordinal relationsamong landmarks,though as we shall see below,semi-quantitative extensions may provide bounds on thepossible real values corresponding to a landmark.The algebraic and differential constraints in a QDE are simple and familiar equations,universally quan-tified over.(add x y z)(mult x y z)(minus x y)(d/dt x y)Since they are asserted as individual constraints,rather than composed as hierarchical expressions intraditional algebra,a QDE must include explicit variables for subexpressions.However,a QDE may alsoinclude constraints representing unknown functions in the set of monotonically increasing continuously differentiable functions(satisfying additional technical conditions discussed in[21]).(M+x y)(M-x y)The and constraints make it possible to express a QDE model including functions whose ex-plicit form is not known,and which are only described in terms of monotonicity.An algebraic or functionalconstraint may specify corresponding values,which are tuples of landmark values known to satisfy the con-straint.A QDE may also explicitly describe the boundaries of its domain of applicability by specifyingtransition conditions that carry the behavior into a different model.The U-tube model in Figure1illustrateseach of these features.The qualitative magnitude of a variable is described either as a landmark value or as an open intervalbetween two adjacent landmarks in the quantity space of that variable.The qualitative value of a variable isdescribed as its qualitative magnitude and the sign of its derivative(its direction of change:inc,std,or dec).(Note that the antecedents of the transition conditions in Figure1are specified by the qualitative values ofparticular variables.)A qualitative state of a model is a tuple of associations of qualitative values to eachvariable in the model.Time is described in the same way as every other variable.Since its direction of change is always inc,time progresses through an alternating sequence of landmark values(called time-points)and open intervalsQualitative Simulation3Qualitative Simulation4 between adjacent time-points.The time-points are defined as those points in time when the qualitative stateof the model(i.e.,the qualitative value of any variable)changes.A qualitative behavior is a sequence of qualitative states,where each state is the immediate successor ofthe one before it.Because of the qualitative representation,it is possible for afinite sequence of qualitativestates to represent the behavior of a system from its initial state at to afinal state at.For example,one possible behavior of the U-tube model in Figure1,initialized with Tank A full and Tank Bempty,is the following three-state behavior concluding with a state where both Tank A and Tank B are partlyfull.We see new landmark values being created and inserted into quantity spaces when new critical valuesare defined;i.e.,when a qualitative magnitude lies in an open interval,but direction of change is.Figure2(a)shows a plot of this qualitative behavior(each qualitative value is plotted at a landmark,or midway between two landmarks).Because the tanks have unknown sizes(the landmarks AMAX andBMAX)and geometries(the monotonic functions linking amount and pressure),the behavior graph for thismodel is a tree of three behaviors.There are extensions to the representation not discussed here,including the region transition and discon-tinuous changes shown in Figure2(b).See[21]for details.2Qualitative SimulationThe QSIM algorithm[20,21]performs qualitative simulation by deriving the immediate successors of eachqualitative state,repeating this step to grow the behavior graph from the initial state at its root.In orderto guarantee that all possible behaviors are predicted,we requirefirst that all possible qualitative valuetransitions are predicted,and second,that combinations of qualitative values are only deleted when they areinconsistent.Table1enumerates all transitions from each qualitative value description to its possible successors.The validity of this table follows directly from the Intermediate Value and Mean Value Theorems fromelementary calculus.The successor generation phase of QSIM consists of the following steps,given a QDE and a currentstate.Qualitative Simulation5Qualitative Simulation6 I-Successors:interval to point.Qualitative Simulation71.(Value generation.)For each variable in,generate all possible successor values using Table1.2.(Constraintfiltering.)For each constraint in the QDE,which applies to a tuple of variables,generateall corresponding tuples of successor values.Delete each tuple that violates its constraint.3.(Local consistencyfiltering.)For each pair of constraints that are adjacent,in the sense that they sharea variable,and for each tuple of one constraint that assigns a value,say,to,delete that tuple ifthere is no tuple associated with the other constraint that also assigns the value to.4.(State generation.)From the remaining tuples of values associated with constraints,exhaustivelyenumerate all consistent complete assignments of values to variables.Create a successor state for from each of these assignments.Once successor states have been added to the behavior graph,they can be analyzed and the description augmented in several ways.In some cases inconsistencies can be identified that were not visible at the successor-generation level,allowing states to be pruned from the graph.Statefilters consider information local to the current state and perhaps its immediate predecessor.In-consistency can be propagated from a state to its predecessors.A quiescent state(fixed point)can be recognized because all directions of change are.In somecases,its stability can also be determined.Transitions to infinite values and infinite times must satisfy additional constraints.Higher-order derivative constraints can sometimes be derived algebraically from the QDE and applied to eliminate certain successor states.New landmarks and new corresponding value tuples can be created explicitly to describe critical values and other uniquely determined values in quantity spaces.A region transition is identified when the current state matches the antecedent to a transition rule.Thecurrent state is linked to a new state,created with respect to the QDE for the new operating region, with values mapped from the current state.The transition may represent a discontinuous change,or it may represent a re-description of the current state within a new model.Behaviorfilters derive properties of the entire behavior terminating in the current state,to augment the behavior description and sometimes determine its inconsistency.A periodic behavior can be identified by matching the new state to one of its predecessors in thebehavior graph.The phase space trajectory of a dynamical system can only intersect itself if the behavior is periodic.Qualitative behaviors that self-intersect without creating a cycle are inconsistent.Terms equivalent to potential and kinetic energy,and conservative and non-conservative work,can be derived from some QDE models,and tested for consistency.When semi-quantitative information is associated with the QDE,it can be propagated to refine or refute the behavior description.(Section3.)Qualitative Simulation8 The QSIM Guaranteed Coverage Theorem states that the QSIM behavior graph describes all real solu-tions to ODE models consistent with the given QDE and initial qualitative state.This follows directly from the fact that all possible successor values are generated,and that states and behaviors are deleted only when proved to be inconsistent.There is no converse guarantee that every predicted qualitative behavior corresponds to a real solution to some ODE described by the QDE.While the constraint satisfaction algorithm in QSIM is sound and com-plete,there may well be a G¨o del-like incompleteness theorem stating that the properties of real dynamical systems are too rich to be captured by anyfinite set of symbolic constraints.2.1TractabilityThe set of behaviors generated by qualitative simulation may include many distinctions unimportant to the model-builder,due to thefixed level of description implied by the qualitative value and qualitative state representations.There are two classes of such unimportant distinctions.In thefirst,there is a region of the state space of the QDE where the qualitative behavior is unconstrained.Recently,methods have been developed for identifying such a“chattering”region and replacing it with an abstract state whose predecessors and successors describe the trajectories into and out of that region[4].In the second class,two or more events(qualitative value transitions)take place,but their temporal order is not constrained by the QDE.Based on the concept of interacting histories[14,32]methods have been developed for qualitative simulation of a QDE decomposed into sub-models[5].The interactions between the histories of the sub-models are considered only when they are needed to permit simulation of the sub-model.Events internal to separate sub-models are not explicitly related,so they do not require explicit branches in the graph of qualitative behaviors.While the behavior graph predicted for a complex QDE model may still be quite large,these two methods have,in principle,eliminated the problem of intractable branching in qualitative simulation by eliminating the two sources of explicit distinctions unimportant to the model-builder.2.2Querying the behavior graphAn individual qualitative behavior is a description of the time-evolution of the variables in the QDE,and is not difficult to interpret,whether it is purely qualitative or if it is augmented with bounding intervals and envelopes.However,a large behavior graph represents a disjunctive prediction with many disjuncts,and requires automated interpretation tools.A particularly interesting tool that has been developed recently is temporal logic model-checking,ap-plied to the behavior graph output by QSIM[29].The branching-time temporal logic CTL[11]is particu-larly well suited to expressing statements of interest about the QSIM behavior graph.We can express:QSIM predicates on states qvalue,quiescent,cycle,etc.Logical connectives and,or,not,implies.Temporal path relations eventually,always,next,until,etc.Modal quantifiers necessarily,possibly.The behavior graph output by QSIM can be interpreted as a branching-time temporal model,against which a temporal assertion can be checked for validity.There are efficient incremental model-checking algorithms that can be used to check whether a temporal model structure is an interpretation of a given statement in CTL[2].The model-checking algorithm is sound and complete.However,the QSIM Guaran-teed Coverage Theorem provides only a one-sided guarantee about the relation between the QSIM behaviorQualitative Simulation9 graph and the set of predicted behaviors:QSIM predicts every real behavior,but some predictions could be spurious,and not correspond to any real behavior.Therefore,model-checking can prove a universal statement in temporal logic(one of the form),but not an existential statement(one of the form),since the behavior that is identified as the interpretation for could be a spurious be-havior[29].Temporal logic model checking can be used to prove properties of dynamical systems such as non-linear controllers,even with incomplete knowledge[22].2.3Guided SimulationThe relation between temporal logic and qualitative simulation can be carried one step farther,to allow as-sertions in temporal logic to be treated as part of the model[3].The extended qualitative simulator,TeQSIM, generates only qualitative behaviors that satisfy the temporal logic assertions as well as the constraints in the QDE and the requirements of continuity.This approach has two major uses.First,it extends the expressive power available to the model-builder to state properties of the system that are difficult to capture in the constraint language of the QDE.An example is the ability to describe time-varying behavior of exogenous variables,including specifying bounds on the time of occurrence of discrete events.The second use is to allow the model-builder to focus the simulator’s attention on a subset of the state space of the model described by the temporal logic assertions,rather than to explore the larger space of all possible behaviors.3Semi-Quantitative SimulationPartial knowledge can be quantitative as well as purely qualitative.The QDE and the qualitative behaviors produced by QSIM can serve as a symbolic and algebraic framework for reasoning with several representa-tions of incomplete quantitative knowledge.A landmark value is a symbolic name for an unknown real number,described in terms of its ordinal relations with other landmark values,and the corresponding value tuples it participates in.A natural form of partial quantitative knowledge about the unknown real number corresponding to a landmark is a bounding interval,whose endpoints can be real numbers or.Two assertions of bounding intervals for the same landmark can be combined simply by intersecting the intervals.A smaller resulting interval corresponds to more precise knowledge about the value of that landmark.An empty intersection means that no value can be consistently assigned to that landmark,so the current qualitative behavior is refuted.A monotonic function constraint(M+x y)is a qualitative description of an unknown function,describing the shape of only as monotonically increasing.A natural form of partial quantitative information about is to provide a pair of real-valued“static envelope”functions that bound above and below:for all(Figure3).It can also be useful to assert bounds on the slope of a monotonic function.The QDE,augmented with bounds on landmark values and static envelopes on monotonic function constraints,is referred to as a“semi-quantitative differential equation”or SQDE.Qualitative simulation augmented with semi-quantitative inference is called“semi-quantitative simulation”or SQSIM.Figure3 shows a SQDE model of a water tank.Purely qualitative simulation of the water tank SQDE predicts three qualitative behaviors:equilibrium partly full,overflow,and equilibrium exactly at the brim.The following table of qualitative values shows the three states of the equilibrium-partly-full behavior,including the creation of new landmarks for uniquely specified values.Qualitative Simulation10Qualitative Simulation113.1Propagating Interval BoundsThe simplest semi-quantitative extension to QSIM,called Q2,is based on interval arithmetic[21,chapter9].A qualitative behavior can be interpreted as a set of algebraic and functional constraints among landmark values.The following constraints,called the“Q2equations”,are derived from the corresponding landmark value tuples implied by time-point qstates in the qualitative behavior above,from the bounding envelopes on monotonic function constraints,and from time-interval states via the Mean Value Theorem.represents the distance between landmarks and;is the interval enclosing both and;and is the range bound on for a monotonic function.The initially given interval values associated with the landmarks,and are propagated across the Q2equations,following the rules in Table2.Newly derived values are intersected with old values until afixed point is reached or until an empty interval is derived.The result of propagation for the equilibrium-partly-full behavior is the following set of bounds for landmark values.In the other two behaviors,an empty interval is derived for some landmark,so the behaviors are refuted.Qualitative Simulation12ifandThe entries for and describe only the cases where.It is straight-forward to extend to the full case split on possible combinations of signs,and to handle as bounds.Table2:Interval arithmeticQualitative Simulation13 Semi-quantitative inference is implemented in QSIM as afilter applied to each partial behavior whenever a successor state is added to the behavior graph.When a partial behavior is refuted,its extensions need not be computed,reducing the branching factor of the behavior graph.3.2Order-of-Magnitude ConstraintsA different form of partial quantitative knowledge is order-of-magnitude constraints on landmark values [6,26].These relations can also be propagated across the Q2equations derived from a behavior.The result is additional predicted order-of-magnitude relations,or contradictions that refute behaviors from the behavior graph,just as in Q2.3.3State InterpolationThe temporal granularity of the qualitative behavior description predicted by QSIM,and hence of the Q2 equations,is determined by the qualitative value changes that take place in the behavior.Thus,semi-quantitative inference takes place over time intervals that are quite large and sometimes infinite,making it difficult to draw strong conclusions.Q3[1]addresses this problem by interpolating new landmarks into intervals in quantity spaces,including new time-points into large time-intervals.This provides smaller intervals of change,so the derived error bounds are tighter.The effect is essentially the same as Euler integration,approximating a continuous curve above and below by rectangles.It is possible to show that as the uncertainty in the SQDE approaches zero,and as the size of largest time-interval in the behavior approaches zero,the resulting semi-quantitative prediction converges to the real-valued solution to the corresponding ODE[1].3.4Dynamic EnvelopesThe rectangular bounds on a variable’s behavior derived for time-interval states by Q2and Q3are con-sequences of the Mean Value Theorem and the bounds on the rate of change of the variable over that time-interval.In many cases,we can derive stronger bounds.Just as static envelopes define real-valued functions providing upper and lower bounds to partially known monotonic functions,it is possible to infer real-valued functions providing upper and lower bounds on the values of variables over time.These“dynamic envelope”functions are the solutions to a real-valued ODE model that can be derived from the bounds and static envelopes in the SQDE and simulated numerically [19].For example,consider a model of an open-ended U-tube,with constantflow into tank A and a pressure-drivenflow out of tank B:The SQDE includes interval bounds on the landmark value and static envelopes,,, on the unknown functions.The bounding ODE system,which must have double the order of the original QDE,is:Qualitative Simulation14 Dynamic envelopes give improved bounds on the behavior over an interval starting at the initial state,but eventually diverge and provide no constraint farther away(Figure4).Thus,dynamic envelopes shouldbe combined with inference using the symbolic Q2and Q3methods.3.5Research Problem:Soft BoundsSemi-quantitative inference based on intervals and envelopes preserves the QSIM Guaranteed Coverage Theorem:only behaviors that are provably inconsistent are deleted.An important research direction is extending semi-quantitative inference to partial knowledge of quantity in the form of probability distribution functions:Gaussian distributions and more general pdfs.These representations seldom support inference of direct contradictions,making it difficult to refute a behavior entirely.Rather,the goal must be to infer a degree of belief in a behavior,and a distribution of belief over a set of behaviors.In the monitoring context (next section),it will be useful to distribute belief over a set of alternate hypothesized models as well.4Monitoring and System IdentificationMonitoring is the process of comparing an observation stream with predictions from a model of the system being observed.Monitoring is typically used to detect failures by detecting differences between the observa-Qualitative Simulation15 tion stream and predictions from a model of the healthy system[24].System identification is the process of combining a partially-specified model with observations from a system to converge on a more accurate and precisely specified model[23].Traditional approaches to monitoring and system identification deal with incomplete knowledge of the system being observed by attempting to select precise models that are close approximations to the unknown true system.In the qualitative framework,by contrast,the attempt is to select SQDE models that cover sets of precisely-specified models and behaviors.SQUID[17]uses semi-quantitative simulation to unify the quan-titative observation stream with a SQDE model to derive a more precisely specified model,still guaranteed to cover all ODE models consistent with the SQDE and the observations;or to derive a contradiction,refut-ing an entire family of ODE models.MIMIC[10]is an approach to monitoring that tracks multiple SQDE models in parallel,proposing and doing system identification with potential fault models even before the nominal model is refuted.4.1SQUID:Semi-Quantitative System IdentificationAn SQDE model represents a hypothesis about the qualitative structure of the system being observed.The quantitative uncertainty in a given SQDE model is represented by bounds on landmark values,static en-velopes around monotonic functions,and dynamic envelopes around predicted rmation from the observation stream can be used to shrink each of these types of uncertainty.SQUID[17]first segments the observation stream into qualitatively distinct regions of monotonic change, called trends,separated by critical points(Figure5(a)).Then it uses MSQUID[18],a specialized neural-net-based method for estimating monotonic functions and bounding envelope functions covering the observed data points out to a specified confidence level(Figure5(b)).Since the observations and the predictions are now in the same bounds-and-envelopes representation, they can be combined to either refine(Figure5(c))or refute(Figure5(d))the current hypothesized model. Refinements to the dynamic envelopes predicted by the SQDE model can then be propagated back to the landmark bounds and the static envelopes around monotonic functions,so the SQDE model will be able to make more precise predictions for future cases.More precise predictions are useful for many purposes,of course,but in particular they make the model easier to refute,so that more subtle contradictions between observation and prediction can be detected in the future.4.2MIMIC:Monitoring with Semi-Quantitative ModelsStarting with an SQDE representing the nominal(“healthy”)state of a system,SQUID can be used to monitor the system by using the information in the observation stream to progressively refine the uncertainty in the model(Figure6).If the observation stream refutes the nominal model,then fault diagnosis is required.However,if the system is complex and there is significant uncertainty in the SQDE,then indications of possible faults may have appeared in the observation stream well before the nominal model could actually be refuted.It is well known that operator failure in complex dynamic systems(e.g.the Three Mile Island nuclear plant failure)often occurs due to operatorfixation on a single hypothesized model of the system that is only refuted after it is too late tofix a developing problem[25].The MIMIC approach to monitoring[10,27]tracks multiple hypotheses in parallel,each expressed as an SQDE model.Any desired features in the observation stream can be used to trigger fault hypotheses, launching additional trackers to run in parallel,even before the nominal model is refuted.Multiple active trackers and their predictions can be analyzed to select observations or plan experiments for differential。

ForewordNational Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .ForewordThe construction industry is in the middle of a growing crisis worldwide. With 40% of the world’s raw materials being consumed by buildings, the industry is a key player in global economics and politics. And, since facilities consume 40% of the world’s energy and 65.2% of total U.S.electrical consumption, the construction industry is a key player in energy conservation, too! With facilities contributing 40% of the carbon emissions to the atmosphere and 20% of material waste to landfills, the industry is a key player in the environmental equation. Clearly, the construction industry has a responsibility to use the earth’s resources as efficiently as possible.Construction spending in the United States is estimated to be $1.288 trillion for 2008. The Construction Industry Institute estimates there is up to 57% non-value added effort or waste in our current business models. This means the industry may waste over $600 billion each year.There is an urgent need for construction industry stakeholders to maximize the portion of services that add value in end-products and to reduce waste.Another looming national crisis is the inability to provide enough qualified engineers. Someestimate the United States will be short a million engineers by the year 2020. In 2007, the United States was no longer the world’s largest consumer, a condition that will force United States industry to be more competitive in attracting talented professionals. The United States construction industry must take immediate action to become more competitive.The current approach to industry transformation is largely focused in efforts to optimize design and construction phase activities. While there is much to do in those phases, a lifecycle view is required. When sustainability is not adequately incorporated, the waste associated with current design, engineering, and construction practices grows throughout the rest of the facility’s lifecycle. Products with a short life add to performance failures, waste, recycling costs, energyconsumption, and environmental damage. Through cascading effects, these problems negatively affect the economy and national security due to dependence on foreign petroleum, a negative balance of trade, and environmental degradation. To halt current decline and reverse existing effects, the industry has a responsibility to take immediate action.While only a very small portion of facility lifecycle costs occur during design and construction, those are the phases where our decisions have the greatest impact. Most of the costs associated with a facility throughout its lifecycle accrue during a facility’s operations and sustainment. Carnegie-Mellon University research has indicated that an improvement of just 3.8% in productivity in the functions that occur in a building would totally pay for the facility’s design, construction, operations and sustainment, through increased efficiency. Therefore, as industry focuses on creating, maintaining, and operating facilities more efficiently, simultaneous action is required to ensure that people and processes supported by facilities are optimized.BIM stands for new concepts and practices that are so greatly improved by innovative information technologies and business structures that they will dramatically reduce the multiple forms of waste and inefficiency in the building industry. Whether used to refer to a product – Building Information Model (a structured dataset describing a building), an activity – Building Information Modeling (the act of creating a Building Information Model), or a system – Building Information Management (business structures of work and communication that increase quality andefficiency), BIM is a critical element in reducing industry waste, adding value to industry products, decreasing environmental damage, and increasing the functional performance of occupants.ForewordNational Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .The National Building Information Model Standard™ (NBIMS) is a key element to building industry transformation. NBIMS establishes standard definitions for building information exchanges to support critical business contexts using standard semantics and ontologies. Implemented in software, the Standard will form the basis for the accurate and efficientcommunication and commerce that are needed by the building industry and essential to industry transformations. Among other benefits, the Standard will help all participants in facilities-related processes achieve more reliable outcomes from commercial agreements.Thus, there is a critical need to increase the efficiency of the construction process. Today’s inefficiency is a primary cause of non-value added effort, such as re-typing (often with a new set of errors) information at each phase or among participants during the lifecycle of a facility or failing to provide full and accurate information from designer to constructor. With the implementation of this Standard, information interoperability and reliability will improve significantly. Standard development has already begun and implementable results will beavailable soon. BIM development, education, implementation, adoption, and understanding are intended to form a continuous process ingrained evermore into the industry. Success, in the form of a new paradigm for the building construction industry, will require that individuals andorganizations step up to contribute to and participate in creating and implementing a commonBIM standard. Each of us has a responsibility to take action now.David A. Harris, FAIAPresidentNational Institute of Building SciencesTable of ContentsNational Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .ForewordTable of ContentsSection 1 – Introduction to the National Building InformationModeling Standard™ Version 1 - Part 1: Overview,Principles, and MethodologiesChapter 1.1 Executive SummaryChapter 1.2 How to Read Version 1 -Part 1 of the NBIMStandard Navigation guide for readers with varied interests, responsibilities, and experience with BIM.Section 2 – Prologue to the National BIM StandardChapter 2.1 BIM Overall Scope An expansive vision for building informationmodeling and related concepts.Chapter 2.2 Introduction to the National BIM Standard Committee The Committee’s vision and mission,organization model, relationships to otherstandards development organizations,philosophical position, and the Standardproduct.Chapter 2.3 Future Versions Identifies developments for upcoming versionsof the Standard including sequence ofdevelopments, priorities, and planned releasedates.Section 3 – Information Exchange ConceptsChapter 3.1 Introduction to ExchangeConcepts What is an information exchange? Theory and examples from familiar processes.Chapter 3.2 Data Models and the Role of Interoperability.High level description of how BIM informationwill be stored in operational and projectsettings. Compares and contrasts integrationand interoperability and the NBIM Standardrequirement for interoperability.Chapter 3.3 Storing and SharingInformation Description of conceptual need for a shared, coordinated repository for lifecycle information.Presents an approach to providing the sharedinformation for a BIM which can be used byinformation exchangesTable of ContentsNational Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .Chapter 3.4 Information Assurance Discusses means to control information inputand withdrawal from a shared BIM repository.Section 4 – Information Exchange ContentChapter 4.1 BIM MinimumDefines quantity and quality of information required for a defined BIM. Chapter 4.2 Capability Maturity Model Building on the BIM Minimum chapter, furtherdefines a BIM and informs planning to improvethe capability to produce a mature BIM.Section 5 – NBIM Standard Development ProcessChapter 5.1 Overview of ExchangeStandard Developmentand Use ProcessDiagrams and describes major components in NBIM Standard development process. Chapter 5.2 Workgroup Formationand RequirementsDefinition Introduces the concept of forums and domain interest groups forming around needed exchange definitions. Discusses theInformation Delivery Manual (IDM) process andtools for requirements definition activities.Chapter 5.3 User-Facing Exchange Models Covers the IDM requirements for IFC-independent data model views.Chapter 5.4 Vendor-Facing Model View Definition, Implementation and Certification Testing Explains Model View Definition (MVD)requirements for schema-specific modeldefinition and the NBIMS Committee’s role infacilitating implementation and certificationtesting.Chapter 5.5 Deployment Discusses Project Agreements and use ofGeneric BIM Guides associated with BIMauthoring (creating a BIM) using certifiedapplications, validating the BIM construction,validating data in the BIM model, and using theBIM model in certified products to accomplishproject tasks through interoperable exchanges.Chapter 5.6 Consensus-Based Approval MethodsDescribes various methods of creating,reviewing, and approving the NBIM StandardExchange Requirements, Model ViewDefinitions, Standard Methods, Tools, andReferences used by and produced by theNBIMS Committee.Table of ContentsNational Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .AcknowledgementsReferencesGlossaryAppendicesIntroduction to AppendicesAppendix A Industry Foundation Classes(IFC or ifc) IFC define the virtual representations of objects used in the capital facilitiesindustry, their attributes, and theirrelationships and inheritances.Appendix B CSI OmniClass ™OmniClass is a multi-table facetedclassification system designed for useby the capital facilities industry to aidsorting and retrieval of informationand establishing classifications forand relationships between objects ina building information model.Appendix C International Framework for Dictionaries (IFDLibrary ™)A schema requires a consistent set ofnames of things to be able to work.Each of these names must have acontrolled definition that describeswhat it means and the units in which itmay be expressed.Section 1 – Introduction to the National BIM Standard V 1 - Part 1Chapter 1.1National Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .Chapter 1.1 Executive SummaryNational Building Information Modeling Standard™ Version 1 - Part 1:Overview, Principles, and MethodologiesIntroductionThe National Building Information Modeling Standard (NBIMS) Committee is a committee of the National Institute of Building Sciences (NIBS) Facility Information Council (FIC). The vision for NBIMS is “an improved planning, design, construction, operation, and maintenance process using a standardized machine-readable information model for each facility, new or old, which contains all appropriate information created or gathered about that facility in a format useable by all throughout its lifecycle.”1 The organization, philosophies, policies, plans, and working methods that comprise the NBIMS Initiative and the products of the Committee will be the National BIM Standard (NBIM Standard), which includes classifications, guides, recommended practices, and specifications.This publication is the first in a series intended to communicate all aspects of the NBIMS Committee and planned Standard, which will include principles, scope of investigation,organization, operations, development methodologies, and planned products. NBIMS V1-P1 is a guidance document that will be followed by publications containing standard specifications adopted through a consensus process .Wherever possible, international standards development processes and products, especially the NIBS consensus process, American Society for Testing and Materials (ASTM), AmericanNational Standards Institute (ANSI), and International Standards Organization (ISO) efforts will be recognized and incorporated so that NBIMS processes and products can be recognized as part of a unified international solution. Industry organizations working on open standards, such as the International Alliance for Interoperability (IAI), the Open Geospatial Consortium (OGC), and the Open Standards Consortium for Real Estate (OSCRE), have signed the NBIMS Charter inacknowledgement of the shared interests and commitment to creation and dissemination of open, integrated, and internationally recognized standards. Nomenclature specific to North American business practices will be used in the U.S. NBIMS Initiative. Consultations with organizations in other countries have indicated that the U.S.-developed NBIM Standard, once it is localized, will be useful internationally as well. Continued internationalization is considered essential to growth of the U.S. and international building construction industries.BIM Overall Scope and DescriptionBuilding Information Modeling (BIM) has become a valuable tool in some sectors of the capital facilities industry. However in current usage, BIM technologies tend to be applied within vertically integrated business functions rather than horizontally across an entire facility lifecycle. Although the term BIM is routinely used within the context of vertically integrated applications, the NBIMS Committee has chosen to continue using this familiar term while evolving the definition and usage to represent horizontally integrated building information that is gathered and applied throughout the entire facility lifecycle, preserved and interchanged efficiently using open and interoperable technology for business, functional and physical modeling, and process support and operations. 1 Charter for the National Building Information Modeling (BIM) Standard, December 15, 2005, pg.1. See /bim/pdfs/NBIMS_Charter.pdf .Section 1 – Introduction to the National BIM Standard V 1 - Part 1Chapter 1.1National Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .NBIM Standard Scope and DescriptionThe NBIMS Initiative recognizes that a BIM requires a disciplined and transparent data structure supporting all of the following.x A specific business case that includes an exchange of building information. x The users’ view of data necessary to support the business case. x The machine interpretable exchange mechanism (software) for the required information interchange and validation of results.This combination of content selected to support user needs and described to support open computer exchange form the basis of information exchanges in the NBIM Standard. All levels must be coordinated for interoperability, which is the focus of the NBIMS Initiative. Therefore, the primary drivers for defining requirements for the National BIM Standard are industry standard processes and associated information exchange requirements.In addition, even as the NBIM Standard is focused on open and interoperable informationexchanges, the NBIMS Initiative addresses all related business functioning aspects of the facility lifecycle. NBIMS is chartered as a partner and an enabler for all organizations engaged in the exchange of information throughout the facility lifecycle.Data Modeling for BuildingsKey to the success of a building information model is its ability to encapsulate, organize, and relate information for both user and machine-readable approaches. These relationships must be at the detail level, relating, for example, a door to its frame or even a nut to a bolt, whilemaintaining relationships from a detailed level to a world view. When working with as large a universe of materials as exists in the built environment, there are many traditional verticalintegration points (or stovepipes) that must be crossed and many different languages that must be understood and related. Architects, engineers, as well as the real estate appraiser or insurer must be able to speak the same language and refer to items in the same terms as the first responder in an emergency situation. Expand this to the world view where systems must be interoperable in multiple languages in order to support the multinational corporation. Over time ontologies will be the vehicles that allow cross communication to occur. In order to standardize these many options, organizations need to be represented and solicited for input. There are several, assumed to be basic, approaches in place that must come together in order to ensure that a viable and comprehensive end-product will be produced.The Role of InteroperabilitySoftware interoperability is seamless data exchange at the software level among diverseapplications, each of which may have its own internal data structure. Interoperability is achieved by mapping parts of each participating application’s internal data structure to a universal data model and vice versa. If the employed universal data model is open, any application canparticipate in the mapping process and thus become interoperable with any other application that also participated in the mapping. Interoperability eliminates the costly practice of integrating every application (and version) with every other application (and version).The NBIM Standard maintains that viable software interoperability in the capital facilities industry requires the acceptance of an open data model of facilities and an interface to that data model for each participating application. If the data model is industry-wide (i.e. represents the entire facility lifecycle), it provides the opportunity to each industry software application to become interoperable.Section 1 – Introduction to the National BIM Standard V 1 - Part 1Chapter 1.1National Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .Storing and Sharing InformationOne of the innovations, demonstrated by some full-service design and engineering firms and several International Alliance for Interoperability (IAI) demonstration projects, has been the use of a shared repository of building information data. A repository may be created by centralizing the BIM database or by defining the rules through which specific components of BIM models may be shared to create a decentralized shared model. As BIM technology and use matures, thecreation of repositories of project, organization, and/or owner BIM data will have an impact on the framework under which NBIMS operates. Owners are likely to create internally as-built and as-maintained building model repositories, which will be populated with new and updated information supplied via design/construction projects, significant renovations, and routine maintenance and operations systems.Information AssuranceThe authors caution that, while a central (physical or virtually aggregated) repository of information is good for designing, constructing, operating, and sustaining a facility, and therepository may create opportunities for improved efficiency, data aggregation may be a significant source of risk.Managing the risks of data aggregation requires advanced planning about how best to control the discovery, search, publication, and procurement of shared information about buildings and facilities. In general, this is addressed in the data processing industry through digital rights management. Digital rights management ensures that the quality of the information is protected from creation through sharing and use, that only properly authorized users are granted access, and only to that subset of information to which they should have access. There is a need toensure that the requirements for information are defined and understood before BIMs are built, so that facility information receives the same protection that is commonplace in world-wide personnel and banking systems.Minimum BIM and the Capability Maturity ModelThe NBIM Standard Version 1 - Part 1 defines a minimum standard for traditional vertical construction, such as office buildings. It is assumed that developing information exchange standards will grow from this minimum requirement.The Standard also proposes a Capability Maturity Model (CMM) for use in measuring the degree to which a building information model implements a mature BIM Standard. The CMM scores a complete range of opportunity for BIMs, extending from a point below which one could say the data set being considered is not a BIM to a fully realized open and interoperable lifecycle BIM resource.The U.S. Army Corps of Engineers BIM Roadmap 2 is presented as a useful reference for building owners seeking guidance on identifying specific data to include in a BIM from a design or construction perspective.2 See https:///default.aspx?p=s&t=19&i=1 for the complete roadmap.Section 1 – Introduction to the National BIM Standard V 1 - Part 1Chapter 1.1National Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .NBIM Standard Process DefinitionProposals for the processes the NBIMS Committee will employ to produce the NBIM Standard and to facilitate productive use are discussed. A conceptual diagram to orient the user is provided. Components of this diagram correspond to section 5 chapters.Both the process used to create the NBIM Standard and the products are meant to be open and transparent. The NBIMS Committee will employ consensus-based processes to promote industry-wide understanding and acceptance. Additionally, the Committee will facilitate the process whereby software developers will implement standard exchange definitions and implementations tested for compliance. Finally, the NBIMS Committee will facilitate industry adoption and beneficial use through guides, educational activities, and facilitation of testing by end users of delivered BIMs.The Information Exchange Template, BIM Exchange Database, the Information Delivery Manual (IDM), and Model View Definition (MVD) activities together comprise core components of the NBIM Standard production and use process. The Information Exchange Template and BIM Exchange Database are envisioned as web-based tools to provide search, discovery, and selection of defined exchanges as well as a method of providing initial information necessary to propose and begin a new exchange definition discussion. The NBIMS workgroup formation phase teams will use the IDM, adapted from international practices, to facilitate identification and documentation of information exchange processes and requirements. IDM is the user-facing phase of NBIMS exchange standard development with results typically expressed in human-readable form. MVD is the software developer-facing phase of exchange standard development. MVD is conceptually the process which integrates Exchange Requirements (ERs) coming from many IDM processes to the most logical Model Views that will be supported by softwareapplications. Implementation-specific guidance will specify structure and format for data to be exchanged using a specific version of the Industry Foundation Classes (IFC or ifc) specification. The resulting generic and implementation-specific documentation will be published as MVDs, as defined by the Finnish Virtual Building Environment (VBE) project,3 the Building Lifecycle Interoperability Consortium (BLIS),4 and the International Alliance for Interoperability (IAI).5 The Committee will work with software vendors and the testing task team members to plan and facilitate implementation, testing, and use in pilot projects. After the pilot phase is complete, the Committee will update the MVD documents for use in the consensus process and ongoing commercial implementation. Finally, after consensus is reached, MVD specifications will be incorporated in the next NBIMS release.NBIMS AppendicesReference standards in the NBIM Standard provide the underlying computer-independent definitions of those entities, properties, relationships, and categorizations critical to express the rich language of the building industry. The reference standards selected by the NBIMSCommittee are international standards that have reached a critical mass in terms of capability to share the contents of complex design and construction projects. NBIMS V1-P1 includes three candidate reference standards as Appendix documents: IAI Industry Foundation Classes (IFC or ifc), Construction Specifications Institute (CSI) OmniClass ™, and CSI IFDLibrary ™.3http://cic.vtt.fi/projects/vbe-net/4 5Section 1 – Introduction to the National BIM Standard V 1 - Part 1Chapter 1.1National Building Information Modeling Standard™©2007 National Institute of Building Sciences. All rights reserved .The IFC data model consists of definitions, rules, and protocols that uniquely define data sets which describe capital facilities throughout their lifecycles. These definitions allow industrysoftware developers to write IFC interfaces to their software that enable exchange and sharing of the same data in the same format with other software applications, regardless of the internal data structure of the individual software application. Software applications that have IFC interfaces are able to exchange and share data with other application that also have IFC interfaces.The OmniClass ™ Construction Classification System (OmniClass or OCCS) is a multi-tableclassification system designed for use by the capital facilities industry. OmniClass includes some of the most commonly used taxonomies in the capital facilities industry. It is applicable for organizing many different forms of information important to the NBIM Standard, both electronic and hard copy. OCCS can be used in the preparation of many types of project information and for communicating exchange information, cost information, specification information, and other information that is generated throughout the facility’s lifecycle.IFDLibrary ™ is a kind of dictionary of construction industry terms that must be used consistently in multiple languages to achieve consistent results. Design of NBIMS relies on terminology and classification agreement (through OmniClass ) to support model interoperation. Entries in the OmniClass tables can be explicitly defined in the IFDLibrary once and reused repeatedly,enabling reliable automated communications between applications – a primary goal of NBIMS. ReferencesNBIMS References in this document represent the work of many groups working in parallel to define BIM implementation for their areas of responsibility. Currently there are four types of references.x Business Process Roadmaps are documents that provide the business relationships of the various activities of the real property industry. These will be the basis for organizing the business processes and will likely be further detailed and coordinated over time. The roadmaps will help organize NBIMS and the procedures defined in the InformationDelivery Manuals (IDMs).x Candidate Standards are documents that are candidates to go through the NBIMS consensus process for acceptance as part of future NBIMS. It is envisioned that Part 2 or later releases of the Standard will incorporate these documents once approved.x Guidelines have been developed by several organizations and include items that should be considered for inclusion in NBIMS. Since NBIMS has not existed prior to this, there was no standard from which to work, resulting in a type of chicken-or-egg dilemma.When formal NBIMS exists there will need to be some harmonization, not only between the guidelines and NBIMS, but also in relating the various guidelines to each other.While guidelines are not actually a part of NBIMS, they are closely related and therefore included as references.xOther Key References are to parallel efforts being developed in concert with NBIMS. Not part of NBIMS, they may, in fact, be standards in their own right.。

汽车文摘蒋永峰王晓燕田玲玲郝文权（中国第一汽车股份有限公司研发总院，长春130013）【摘要】车辆动力学性能开发包括性能目标设定、目标分解、优化设计、底盘调校，逐渐向目标达成逼近，开发结束后实现目标达成。

性能目标分解即指标分解，用简单有物理意义的理论公式关联整车指标与总成指标，将整车的客观性能指标分解至系统特性，是整车性能目标达成的关键环节，在性能开发中承上启下，是各主机厂的核心技术。

性能指标分解正常在车辆开发初期，用于指标分解的模型应采用尽可能少的建模参数，建模分析迅速且模型能明确表达系统参数对整车性能的影响规律。

ADAMS 或CarSim 模型，由于模型结构过于复杂，不适用于车辆性能的指标分解。

本文建立了用于性能指标分解的模型，并基于此模型研究底盘动力学操稳转向性能指标的分解及应用方法，为车辆动力学性能开发工作提供理论指导。

主题词：车辆动力学性能底盘指标分解中图分类号：U461.1文献标识码：ADOI:10.19822/ki.1671-6329.20190035Development of Handling and Steering Performance Target CascadingFor Passenger CarJiang Yongfeng,Wang Xiaoyan,Tian Lingling,Hao Wenquan（General Research and Development Institute,China FAW Corporation Limited,Changchun 130013）【Abstract 】Vehicle dynamic development includes targets setting,target cascading,optimization design,chassis tuning,gradually approaching the goal,and achieving the goal after development.Target cascading is the key link to achieve the performance target of the whole vehicle,which decomposes objective performance indexes of the whole vehicle into system characteristics.It is the core technology of the OEM.Performance index cascading is normal in the early stageof vehicle development.The model used for index cascading should adopt as few modeling parameters as possible.Modeling analysis is fast and the model can clearly express the influence of system parameters on vehicle performance.ADAMS or Carsim models are not suitable for vehicle performance index cascading because of their complex structure.This paperestablishes a model for performance index cascading,and studies the decomposition and application method of vehicle dynamic handling and steering performance index based on this model,which provides theoretical guidance for vehicledynamic performance development.Key words:Vehicle dynamics,Chassis,Target cascade【引用】蒋永峰，王晓燕，田玲玲，等.乘用车操稳转向性能指标分解技术应用研究[J].汽车文摘，2020（4）：39-43.【Citation 】Jiang Y.,Wang X.,Tian L.,et al.Development of Handling and Steering Performance Target Cascading For Passenger Car[J].Automotive Digest (Chinese),2020(4):39-43.乘用车操稳转向性能指标分解技术应用研究1概述车辆动力学性能开发过程中，根据产品定位、市场需求、竞品表现、品牌DNA 进行整车性能目标设定，以此为依据开发具有竞争力车型。

In the vast tapestry of life on Earth, wildlife represents an irreplaceable and invaluable treasure trove of diversity, beauty, and ecological balance. World Wildlife Day, observed annually on March 3rd, serves as a global platform to raise awareness about the intrinsic worth of these creatures, the threats they confront, and the collective responsibility we bear in conserving them. This essay, with a minimum length of 1438 words, delves into the profound significance of this day, while also offering a comprehensive, multi-faceted analysis of the challenges faced by the world's fauna and the strategies necessary for their preservation.**The Significance of World Wildlife Day**World Wildlife Day was established by the United Nations General Assembly in 2013 to mark the adoption of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) in 1973. This international observance underscores the crucial role that wildlife plays in maintaining the ecological, economic, social, scientific, and cultural fabric of our planet.Firstly, wildlife is the bedrock of biodiversity, which underpins the resilience and stability of ecosystems. Each species, no matter how small or seemingly insignificant, contributes to intricate food webs, pollination, seed dispersal, nutrient cycling, and other vital ecological processes. The loss of any species can trigger cascading effects that undermine ecosystem health and services, ultimately threatening human well-being.Secondly, wildlife holds immense economic value. Ecotourism, for instance, relies heavily on the presence of diverse and healthy wildlife populations, generating billions of dollars annually and providing livelihoods for millions worldwide. Moreover, many plants and animals serve as sources of food, medicine, and raw materials for various industries.Thirdly, wildlife is deeply entwined with human culture and spirituality. Indigenous communities around the globe have developed rich knowledge systems and traditions centered around local flora and fauna, which form an integral part of their identity and worldview. Additionally, wildlife inspires art,literature, and scientific inquiry, enriching human experience and fostering a sense of wonder and connection with nature.**Challenges Facing World Wildlife**Despite its importance, wildlife faces a myriad of threats, ranging from habitat destruction and climate change to overexploitation and illegal trade. These challenges are interconnected and mutually reinforcing, creating a formidable obstacle to conservation efforts.**Habitat Loss and Fragmentation:** Unsustainable land use practices, such as deforestation, urbanization, agriculture expansion, and infrastructure development, have led to massive habitat loss and fragmentation. This not only reduces the available space for wildlife but also disrupts migration routes, breeding patterns, and gene flow, increasing the vulnerability of species to extinction.**Climate Change:** Rising temperatures, altered precipitation patterns, sea-level rise, and extreme weather events are reshaping ecosystems at an unprecedented pace, pushing many species beyond their physiological limits or rendering their habitats inhospitable. Climate change exacerbates existing threats, such as habitat loss and disease outbreaks, and creates novel challenges, like mismatches between species' life cycles and shifting seasons.**Overexploitation and Illegal Wildlife Trade:** Driven by demand for wildlife products, including bushmeat, traditional medicines, ornaments, and pets, overexploitation and illegal trade pose severe threats to numerous species. The illicit wildlife trade, estimated to be worth billions of dollars annually, fuels corruption, undermines governance, and finances criminal networks, making it a serious transnational organized crime.**Invasive Species and Disease Outbreaks:** Invasive alien species, introduced through human activities, often outcompete native wildlife for resources, introduce new diseases, or alter habitats. Concurrently, disease outbreaks, sometimes exacerbated by climate change and human encroachment, can decimate wildlife populations, as witnessed in the case of chytridiomycosis inamphibians or white-nose syndrome in bats.**Conservation Strategies and the Way Forward**Addressing these complex challenges requires a multifaceted, integrated approach that combines science-based policy, effective law enforcement, community engagement, and global cooperation.**Policy and Legislation:** Strengthening and enforcing national and international laws, such as CITES and the Convention on Biological Diversity, is essential to curb illegal wildlife trade, protect critical habitats, and promote sustainable land use. Governments must prioritize biodiversity conservation in national development plans and allocate adequate funding for conservation initiatives.**Science and Innovation:** Advancements in technology, such as remote sensing, genetic analysis, and artificial intelligence, can greatly enhance monitoring, research, and management of wildlife populations and habitats. Moreover, predictive modeling can help anticipate and mitigate the impacts of climate change on wildlife.**Community Engagement and Empowerment:** Recognizing the rights and knowledge of indigenous peoples and local communities, and involving them in decision-making processes, is crucial for successful conservation. Community-based conservation initiatives, such as ecotourism, agroforestry, and payment for ecosystem services, can provide incentives for wildlife-friendly land management while improving local livelihoods.**Education and Awareness-Raising:** World Wildlife Day serves as a catalyst for educational campaigns that foster appreciation for wildlife, highlight the consequences of its decline, and promote responsible consumer choices. Engaging the public, particularly young people, through social media, documentaries, and educational programs can inspire behavioral changes and nurture a new generation of conservation advocates.In conclusion, World Wildlife Day is a timely reminder of the immense value and vulnerability of our planet's wildlife. As we celebrate this occasion, itis imperative that we acknowledge the multifaceted challenges faced by wildlife and commit to implementing comprehensive, collaborative, and innovative solutions to ensure their survival for generations to come. By doing so, we safeguard not only the rich tapestry of life on Earth but also the very foundations of our own existence.。

基于社团划分的复杂网络级联抗毁攻击策略作者：丁超姚宏杜军彭兴钊李浩敏来源：《计算机应用》2014年第06期摘要：为研究在社团划分基础上复杂网络的级联抗毁攻击策略，采用节点及其邻居节点介数定义初始负荷，这种定义方式综合考虑了节点的信息，采用局部择优分配策略处理故障节点负荷，研究了网络耦合强度，WS（WattsStrogatz）小世界网络、BA（BarabásiAlbert）无标度网络、ER（ErdsRényi）随机网络、局域世界（WL）网络在社团划分攻击策略下抗毁性，以及不同攻击策略下具有重叠和非重叠社团结构网络的抗毁性。

仿真结果表明，网络的耦合强度与抗毁性成负相关；不同类型网络在快速分裂算法识别社团前提下，攻击介数最大节点时网络抗毁性最弱；具有重叠社团结构的网络在集团渗流算法（CPM）识别后，采用攻击重叠部分介数最大节点的策略时网络抗毁性最弱。

结论表明采用社团划分的攻击策略可以最大规模破坏网络。

关键词：攻击策略；社团划分；复杂网络；级联抗毁性；网络模型中图分类号： TP393；N945.1文献标志码：A6 结语级联故障普遍存在现实网络中，研究网络的攻击策略对网络抗毁性的影响对于有效打击敌方网络，指导我方网络建设提高网络抗毁性具有重要意义。

本文提出了一种基于社团划分的网络级联抗毁攻击策略，节点初始负荷根据节点及其邻居节点介数定义的“负荷容量”模型，故障负荷分配方式采用局部择优策略。

仿真分析了网络的负荷分配指数对抗毁性的影响，结果表明当α=1时网络的抗毁性最强。

研究分析了WS、BA、ER、WL四种网络模型在社团划分下的攻击策略，重点研究了具有重叠和非重叠社团结构网络的抗毁攻击策略，仿真结果表明基于社团划分的蓄意攻击策略在四种网络模型中均具有较好攻击效果。

研究了社团结构参数对网络抗毁性的影响，结果表明网络的耦合强度与抗毁性成正相关。

在非重叠社团网络中首先快速实现社团划分，然后分社团攻击介数最大的节点取得了最好的攻击策略；在重叠社团结构网络中，实现社团划分后蓄意攻击重叠部分介数最大的节点，然后分社团攻击介数最大节点为最有效网络攻击策略。

1Abstract —Cascading fail ures present severe threats to power grid security, and thus vulnerability assessment of power grids is of significant importance. Focusing on ana ytic methods, this paper reviews the state of the art of vul nerabil ity assessmentmethods in the context of cascading failures. These methods arebased on steady-state power grid mode l ing or high-l eve lprobabi l istic mode l ing. The impact of emerging techno l ogies inc l uding phasor techno l ogy, high-performance computing techniques, and visua l ization techniques on the vu l nerabi lity assessment of cascading fail ures is then addressed, and future research directions are presented.Index Terms —Cascading Fail ure; Vul nerabil ity Assessment; Probabi l ity Ana l ysis; E l ectric Power Systems; Phasor Technology.I. I NTRODUCTION HE concept of a vulnerable system is defined in [1] as a system that operates with a “reduced level of security that renders it vulnerable to the cumulative effects of a series of moderate disturbances”. The term vulnerability in this paper is defined in the context of cascading events and therefore it is beyond the traditional concept of N-1 or N-2 system security criteria [2]. Vulnerability is a measure of the system’s weakness with respect to a sequence of cascading events that may include line or generator outages, malfunctions or undesirable operations of protection relays, information or communication system failures, and human errors. The potential sources of system vulnerability are categorized in [3]. In this definition, it is noted that “it is rare that a major system failure is the result of one catastrophic disturbance”. In other words, major blackouts often involve a sequence ofcascading events. One event may create an operating condition that triggers another event. In an outage scenario, a line fault can cause rerouting of the power flow, leading to overloading of other lines. The overloaded lines may be tripped by impedance relays due to the low voltage and high current operating conditions. Line outages may also cause lowAuthors: Ross Baldick, Badrul Chowdhury, Ian Dobson, Zhaoyang Dong,Bei Gou, David Hawkins, Zhenyu Huang (task lead), Manho Joung, Janghoon Kim, Daniel Kirschen, Stephen Lee, Fangxing Li, Juan Li, Zuyi Li, Chen-Ching Liu, Xiaochuan Luo, Lamine Mili, Stephen Miller, Marvin Nakayama, MiloradPapic, Robin Podmore, John Rossmaier, Kevin Schneider, Hongbin Sun, KaiSun, David Wang, Zhigang Wu, Liangzhong Yao, Pei Zhang (task force chair), Wenjie Zhang, Xiaoping Zhang.voltage and high reactive power demand on nearbygenerators. The undesirable generator tripping events by over-excitation protection is also a typical cascading pattern. Some other typical patterns of cascading events include line tripping due to loss of synchronism, generator tripping due toabnormal voltage and frequency system condition, and under-frequency or under-voltage load shedding. Identifying these typical patterns of cascading events and studying how thesepatterns can be combined into sequences is an important research subject [4][5]. Vulnerability is an essential indication of a system prone tocascading failures. Many analytical methods have been proposed for vulnerability assessment. Traditionally, a power system can be modeled as a (quasi-) steady-state system or as a dynamic system. A wealth of literature can be found on vulnerability assessment based on these modelingperspectives. Some recent work also introduced vulnerability assessment which is not based on the traditional steady-state or dynamic models of a power system. Examples include hidden failures, wild life impact, weather impact and complex system effects. The research results in the literature show that the trend of vulnerability assessment of power systems is evaluated from individual events to multiple events, from local area to system wide analysis, and from simple analysis tools (e.g., transient analysis or reliability analysis only) to mixed analysis tools (considering dynamics, reliability, different operations, complex network theory, and their combinations), which is implied by the fact that cascading failures or blackouts are very complex events and a number of factors are involved.The first paper [6] generated by this Task Force gives a comprehensive overview of the mechanism and properties in terms of cascading failures. This second paper reviews the state of the art of the above mentioned topics in the context of vulnerability assessment for cascading failures. At present, there is not a commonly accepted vulnerability index or assessment method for power systems. If a power systemloses a significant portion of its ability to carry the powerflows due to cascading line outages, it is considered a vulnerable configuration [7]. This paper reviews this topic based on different modeling approaches: steady-state modeling based analysis is presented in Section II; High-levelprobabilistic models of cascading in Section III; and role of emerging technologies, including phasor technology, high-Vulnerability Assessment for Cascading Failuresin Electric Power SystemsTask Force on Understanding, Prediction, Mitigation and Restoration of Cascading FailuresIEEE PES Computer and Analytical Methods SubcommitteeTIEEE Power and Energy Society Power Systems Conference and Exposition 2009, Seattle, WA Panel Session: Understanding, Prediction, Mitigation and Restoration of Cascading Failures978-1-4244-3811-2/09/$25.00 ©2009 IEEEperformance computing techniques, and visualization techniques on the vulnerability assessment of cascading failures is then addressed in Section IV; Recommendations on industry practices and future research needs in Section V.II. V ULNERABILITY A SSESSMENT WITH S TEADY-S TATEM ODELINGFor most of the time, a power system experiences only slow changes and migrates from one steady state to another. In the context of cascading failures, a scenario could be that power ramping-up of power plants leads to transmission line overloading which triggers impedance relays to trip off the line, and a sequence of cascading events is resulted in. In this case, a (quasi-) steady-state model of the power system is adequate for assessing the vulnerability and analyzing the cascading nature of the events. Based on steady-state modeling, vulnerability can be performed by power flow analysis such as N-x contingency analysis, graph analysis and probability analysis. Though presented separately in the next few sections, the methods may be used in a combined manner.A. Power flow based analysis1) N-x contingency analysis“N-1” contingency analysis is an essential part of industry practices in anticipating potential failures in a power grid [8][9]. In the case of cascading failures, a series of power flows is computed with consideration of sequentially related events such as operations of protective relays and control systems. For example, if a problem occurs on a power grid, such as a transmission line going out of service due to contact with vegetation, there are various issues that may surface across the grid, including line overloading, voltage sag or collapse, and load losses. There may be generation re-dispatch or operator actions in response to those issues. Each switching event results in a power flow case. This approach and its various elaborations are the most systematically developed approach for modeling and simulating cascading failure. Operators refer to such initiating outages as “contingencies” and manage the system in a way that ensures any single credible contingency will not propagate into a cascading blackout, which approximately summarizes the N-1 contingency standard established by the North American Electric Reliability Corporation (NERC) [2]. In order to ensure that a single contingency does not result in cascading outages, grid operators continuously run contingency analysis to study all credible “what-if” cases and check for intolerable consequences.Though it has been a common industry practice, analysis based on the N-1 criterion may not be adequate to assess the vulnerability of cascading failures as the assumption is that a cascading failure is caused by a single credible contingency, but multiple unrelated events may occur in a system and result in cascading failures. Therefore, N-2 and even higher order (N-x) contingency events need to be analyzed. One example is the separation of administrative boundaries – called Balancing Areas (or BAs) – which own, operate, and/or manage their own areas of the grid.When performing contingency analysis, each BA looks no further than its own boundaries. For areas within the interconnection where several BAs reside next to each other, seams issues may come into play. If the BAs all evaluate their system to be “OK” with the contingencies within their own systems, they will not prepare for the simultaneous occurrence of multiple contingencies. Individually, model results from each BA may show that each contingency does not cause a problem. However, if these contingencies occur simultaneously, there will likely be a very large system-wide impact, but the urgency to restore the system is not fully recognized with today’s N-1 or N-1-1 contingency analysis. Past power grid blackouts like the Northeast Blackout in 2003 [10] involve multiple contingencies prior to cascading. This clearly indicates the need for N-x contingency analysis, i.e. analysis of simultaneous occurrence of multiple contingencies. N-x contingency analysis can prepare grid operators with mitigation procedures so as to avoid cascading failures.N-x contingency analysis is very challenging due to the combinatory number of contingencies and the extremely large amount of computational time. In the western North America power grid, there are approximately 20,000 elements that could fail. Checking each element takes about ½ CPU·second, therefore, the entire N-1 contingency case set would take about 104 CPU·seconds. In order to check all the combinations of x contingencies (x = 2,3,4,…), it would take approximately 104x CPU·seconds. In many cases, this is not feasible. Therefore, an important element of a practical solution is how to identify the credible N-x contingencies from a system-wide perspective [11] and apply high performance computing techniques and hardware to check a maximum number of contingencies within time constraints [12]. Many of existing contingency ranking methods can be applied. The performance of high-performance computing application to N-x contingency analysis heavily relies on computational load balancing. Ref. [12] points out static computational load balancing schemes do not make full use of computational resources due to uneven time required for different cases. A well-designed dynamic computational load balancing scheme considering the CPU speed, network bandwidth and data exchange latency is key to the performance.2) Simulation models of cascadingTRELSS (Transmission Reliability Evaluation of Large-Scale Systems) [13][14] is an industrial tool used to identify cascading failure problems. TRELSS provides b oth ac and dc network models. The solution algorithms include fast power flow, unit margin, user participation factor and full or fixed-loss economic generation dispatch, a robust mixed-integer linear programming function for remedial actions, user specified remedial actions such as circuit switching, load transfer or load curtailment when contingencies or system problems occur, and both study and remedial action areas. TRELSS is designed to simulate enumerated contingencies with up to 4 generating unitsand 22circuits taken out, common mode, must-run, maintenance, and protection control group outages, and the impact of normal and adverse weather conditions. System failure criteria include circuit overloads, voltage violations, capacity deficiency, islanding, and area interchange failures. The load model allows three different load interruption classes at each bus, and up to ten load levels can be scaled automatically or specified as separate base cases by the user. TRELSS computes three types of reliability indices: 1) System problem indices include frequency, duration, number of inflicting contingencies, and maximum and average degree of violations of all failure criteria; 2) Load curtailment indices are frequency, duration, number of contingencies resulting in load loss, individual power and energy curtailment for buses, contingencies, failure criteria, average indices, and bulk power interruption indices; and 3) Customer indices include customer interruptions, unserved customer hours, system interruption frequency index, system and customer interruption duration index, and system service availability. Better modeling and sequencing of cascading steps have been identified for further development [15].The Oak Ridge-PSERC-Alaska (OPA) model [16] for a fixed network represents transmission lines, loads and generators with the usual dc load flow approximation. Starting from a solved base case, blackouts are initiated by random line outages. Whenever a line is outaged, the generation and load are re-dispatched using standard linear programming methods. The cost function is weighted to ensure that load shedding is avoided where possible. If any lines were overloaded during the optimization, then these lines are outaged with a fixed probability. The process of re-dispatch and testing for outages is iterated until there are no more outages. The total load shed is, then, the power lost in the blackout. The OPA model neglects many of the cascading processes in blackouts and the timing of events, but it does represent in a simplified way a dynamical process of cascading overloads and outages that is consistent with some basic network and operational constraints. OPA can also represent complex dynamics as the network evolves. The model developed using the PSA suite at Los Alamos National Laboratory represents the timing of blackout events and operator actions [17].The Manchester model is based on ac power plow that represents a range of cascading failure interactions, including cascading and sympathetic tripping of transmission lines, heuristic representation of generator instability, under-frequency load shedding, post-contingency re-dispatch of active and reactive resources, and emergency load shedding to prevent a complete system blackout caused by a voltage collapse [18]. The Manchester model is used by Rios et al.[19] to evaluate expected blackout cost using Monte Carlo simulation with a 53-bus system and by Kirschen et al. [20] to apply correlated sampling to develop a calibrated reference scale of system stress that relates system loading to blackout size on a 1000-bus large power system.A new vulnerability index – called the overload risk index (ORI) – is being developed. The method utilizes a two-fold approach that incorporates both deterministic and stochastic calculations. Security analysis involving line outage distribution factors is used to quickly derive the operational state of a system in terms of power flows, while risk assessment is used to calculate the likelihood of such operational states coming into being. The ORI is a measure designed to reflect the likelihood and severity of line overloads in a power system given current system loading levels and component failure rates. Though the specifics concerning the depth of the ORI calculation are still being developed, a general framework and methodology is more defined. Based on the desire to quickly calculate a representative vulnerability index, network sensitivity factors are used to quickly estimate the power flows for a system over a large number of simulations. Line outage distribution factors (LODFs) are used to solve for up to N-3 events in the system, while forced outage rates are used to determine the frequency of these events. Monte Carlo methodology is then used to simulate the likelihood of failure states in the lines of a transmission system, including common structure and right-of-way connections. Failed lines are considered properly disconnected from the system, with all power flows readjusted through the use of the LODFs. Additional reliability calculations could also determine the proper operation of connected relays and breakers. The redistributed power flows are compared with line limitations, and additional outage conditions are determined, allowing for a propagation of line outages due to overloading. Additional contingency tests are performed as lines are removed due to redistribution and new overloading, until the cascade ends without additional overloads or an N-4 or higher event occurs. The index itself is calculated in relation to the amount of line overloads over the total number of simulations using a level of convergence. The significance of this index has yet to be defined in terms of dangerous levels of vulnerability.3) Hidden failuresThe hidden failure in power system refers to permanent defects that would cause a relay or a relay system to incorrectly and inappropriately react to disturbances [22]. The hidden failures in power system are usually triggered by other events, and not frequently occur, but they may have disastrous consequences [21]. Hidden failures of the protection system are modeled with probabilistic approaches as follows [22][23][24][25]:P hf = P0exp( – Z/3Z3)where P hf is the probability of hidden failure of a relay, Z is the impedance seen by this relay, and Z3 is the zone 3 setting.Fast simulation techniques and heuristic random search are applied to identify critical relays that contribute to many possible cascades. Maintaining these relays is a cost-effective mitigation of cascading failures. The availability of protection data to support simulation and the burden of processing it are issues.4) Risk assessment of cascading failuresAll the simulations require some variation in the cases simulated in order to avoid repeatedly simulating the same cascade. This is done by varying the initial conditions of the cascading event or by introducing randomness by Monte Carlo methods in the cascading mechanisms as the cascading proceeds or both.Several questions can be addressed by these simulations. Some produce likely or high risk cascading sequences and others sample more broadly from all the cascading sequences to approximate the overall cascading risk. Controlling the high risk sequences is one possible tactic to mitigate cascading (e.g. [24][26][27]) and finding the overall cascading risk is basic to evaluating the benefits of mitigation efforts [28]. Some simulations can produce conventional customer availability indices.All the simulations approximately represent only a selected subset of the possible cascading interactions. This is necessary and pragmatic at the current state of the art. Little is currently known about how much of a gap there is between the various simulations and reality. However, even with this gap, the simulation results can still be of good value in detecting vulnerabilities and guiding mitigation efforts.III. H IGH-LEVEL P ROBABILISTIC M ODELS OF C ASCADINGHigh-level probabilistic models refer to those describing the statistics of cascading failures but with no power flow or network modeling. These models capture some generic features of the cascading process but do not attempt to represent details of the cascading mechanisms. The CASCADE model [29] has an initial disturbance overloading the system, many identical components that fail when their load exceeds a threshold, and the additional loading of components by the failure of other components. The initial component loadings vary randomly between upper and lower bounds. The model parameters describe the size of the initial disturbance and the amount of additional loading when another component fails. Branching process models [30][31][32][33] can approximate the CASCADE model and are established models for cascading processes in many other fields. The failures occur randomly in a series of stages. The model parameters are the average number of initial failures and the average tendency for the failures to propagate. An accelerated propagation failure model for the number of transmission line failures is proposed in [11]. In this model, the conditional probability of a further failure increases geometrically as the cascading event proceeds up to a limited number of failures, such as seven failures. For more than seven failures the system is considered to be collapsed. The accelerated propagation and branching process models are both consistent with aggregated historical data for North American line outages [34], and a closer fit is possible with the accelerated propagation model.Power grids are not the only types of systems that suffer from cascading failures. This phenomenon also occurs in fault-tolerant computing systems, which have extremely high dependability requirements. For example, banks use fault-tolerant computers to record financial transactions, and overnight-delivery companies employ such systems to track packages. Several software packages have been developed that allow the user to model cascading failures in these types of systems. For example, Galileo [35] incorporates (dynamic) fault trees, which can model propagating failures by using functional dependency gates. OpenSESAME [36] is another software package that quantitatively evaluates fault-tolerant high-availability systems. The input to this package is an enhanced version of a traditional reliability block diagram, thereby allowing inter-component dependencies like failure propagation, failures with a common cause, different redundancy types, and non-dedicated repair. The combinatorial growth in the number of ways cascading failures can occur make them very difficult to analyze, and [37] develop a continuous-time Markov chain model of a dependability system that explicitly tackles this problem. This paper also describes a software package that was developed to take as input a high-level view of the system as a collection of components, from which it calculates various dependability measures of the overall system.These high-level probabilistic models offer some new possibilities for understanding and monitoring cascading failure. They provide analytic formulas for the total number of failures as functions of easily understood parameters quantifying the overall progression of the cascading failure. If these models become established, they could allow statistical estimation of the model parameters from short observed or simulated data sets and hence the estimation of the total number of failures from short data sets. This would enable the quantification of overall cascading risk from real data or simulations. That is, it could become possible to monitor cascading risk from real data and quantify the risk benefits of simulated improvements. Note that since the high-level probabilistic models do not model detailed cascading mechanisms, they cannot be used as detailed simulations to suggest weaknesses that should be fixed or to understand particular cascading sequences. The high-level probabilistic models are complementary to more detailed models.A. Graph analysis of grid topologyA category of vulnerability assessment for cascading failures is the application of graph theory to study the power grid topology, largely ignoring the electrical quantities and power flow constraints. Several graph analysis techniques have been applied to power grid vulnerability assessment, including small-world networks, scale-free networks, and centrality [38]. The basis of graph analysis applications is the mapping of the power grid topology to a complex network by converting generators and substations to nodes and transmission lines and transformers to links with impedance being the path length. The resulting network is an undirected and sparsely connected graph with N nodes and K links.1) Small-world network The small-world network concept was introduced to study social networks [39][40]. It was discovered that power grids have the properties of a small-world network [41][42]: having relative big clustering coefficient and relative small characteristic length path (i.e. impedance in the case of power grids). Clustering efficient C and characteristic length path L are defined in the following equations: ()∑−=i i i ik k G N C 21in edges of number 1where k i is the number of neighboring nodes of node i and G i is the sub-graph associated with node i .∑≠−=ji ij d N N L )1(1where d ij is the shortest path length between nodes i and j .Figure 1 illustrates the concept of small-world networks. Pis an index of randomness. Small-world properties exist when0 < P < 1. Small-world network theory reveals that a fewremote connections greatly decrease the path length, i.e. theelectrical distance. The loss of those remote connections willincrease the characteristic path length, decrease the transfercapacity of power grid, cause partial power shortage andultimately lead to cascading failures. As a result these remoteconnections have important influence on power systemstability. If we can identify these remote connections, thevulnerable lines in the power grids can be identified [43].A cascading failure model based on small-world networkswas proposed in [44] and can be used to identify thevulnerable lines. The model assumes that a node will fail if agiven fraction γ of its neighbors have failed. Starting withinitial failures on a few isolated nodes, the process willbecome cascading when these initial failures lead tosubsequent failures due to exceeding of the fraction γ. Thoselines with the initial failures would be the remote connectionsand are the vulnerable lines according to the small-worldnetwork theory. A Monte-Carlo search can be conducted in combination with the small-world network cascading failure model to identify all the vulnerable lines.Figure 1 Small-world network2) Scale-Free NetworksThe scale-free network model was proposed by Barabasi and Albert in 1999. The formation of scale-free networks is described as a two-step process in [45] – Growth and Preferential Attachment . Preferential Attachment results in a vast number of links. The step of Growth starts with a small number n 0 of nodes and at every time step, adds a new node with m (≤ n 0) links to m different nodes already existing in thenetwork. The step of Preferential Attachment determines which new node should be added at each time step using the probability of a new node connecting to an existing node i . The probability P i is defined below: ∑=jj ii k k Pwhere k i , the degree of node i , is the total number of itsconnections, and j is the rest of the nodes. Thus, the larger the degree, the “more important” the node is in a network [46]. After α time steps, this process results in a network with N (=α + n 0) nodes and m α links. Scale-free networks have important properties that the degree distribution follows the power-law distribution and a few nodes have a large number of links but most nodes haveonly a few links. Simultaneity scale-free networks have otherproperties, e.g., they are remarkably resistant to accidentalattacks but extremely vulnerable to coordinated ones.Ref. [47] applies the scale-free network theory to thepower grid and derives a measure of “electrical centrality” forac power networks. It was found that when measuredelectrically, power networks appear to have a scale-freenetwork structure. Thus, unlike previous studies of the powergrid topology, the scale-free network application reveals thatpower networks have a number of highly-connected “hub”buses. This result and the structure of power networks ingeneral are likely to have important implications for thereliability and security of power networks. Based ontopo l ogica l characteristics of sca l e-free networks anddiscrete particl e swarm optimization, a skel eton-networkreconfiguration strategy is proposed in [48]. It is expectedthat the theory of scale-free networks will play an importantrole in the study of power system blackouts and relatedproblems.3) Betweenness centralityBetweenness centrality intends to assess the load on the nodes or links of a network with the assumption that communication between any two nodes is done along the shortest path. The betweenness centrality of a node i – C B (i ) –and a link l – C B (l ) are defined as follows [49][50][51]:()()()()()() ,,,∑∑==k j jkjk B k j jk jk B a l a l C a i a i Cwhere a jk is the number of shortest paths between nodes j and k , a jk (i ) is the number of shortest paths between j and k containing node i , and a jk (l ) is the number of shortest paths between j and k containing link l . The a ’s are considered the load of a node or link.If the betweenness centrality exceeds a pre-specified threshold, i.e. C B (l ) > C B max (or C B (i ) > C B max ), link l (or node i ) is overloaded and removed from the graph. All betweenness centrality is updated. As the iteration process goes on, a cascading failure propagates. In this application, an underlying assumption is that the capacity for each link and each node is the same, which is not true for a power grid. To overcome this limitation, diversified capacity is introduced in。

Fusa_006_DependentFailureAnalysis（DFA）Today, let us have a look at the dependent failure.1- What is the dependent failure?In ISO 26262-1, the dependent failures are defined as the failures that are not statistically independent, i.e. the probability of the combined occurrence of the failures is not equal to the product of the probabilities of occurrence of all considered independent failures.The following picture is illustrating the difference between dependent failures and independent failures:Based on the root cause of the failures， there are two kinds of failure groups included in the dependent failures:CCF : Common Cause Failures--CMF : Common Mode FailuresCF : Cascading Failures1.1- Common Cause Failure- CCFCommon Cause Failure (CCF) is a failure where a single root cause leads directly tothe failureof two or more distinct components. In this definition, the componentis to be understood with avery large meaning. It may be a hardware component,such as a resistor or a software component or an Electronic Control Unit (ECU) part of a system or even a sub-system made of several ECUs part of a complete system. The following picture illustrates A Common Cause Failure involving 2 components.Common Mode FailureCommon Mode Failure (CMF) is a particular kind of CCF, where the different components or elements fail in the same manner.examples given by ISO 26262-1 are shown below:1.2- Cascading FailureCascading Failure (CF) is a failure where a first component failure causes another component failure.As an example of Cascading Failure, we may consider software composed of several tasks. A particular failure mode of task A may cause it to corrupt data that are used by task B, thuscausing a failure oftask B.A Cascading Failure maybe graphically represented as shown in the picture below. Obviously, more than 2 components or elements may be involved in the cascading failure.1.3- RelationshipIf a failure is treated as dependent failures, it shall be either common cause failure or cascading failure. The relationship between common cause failures, cascading failures, and commonmode failures are shown in the picture below .2- Reference[1] ISO 26262-6：2018，Road vehicles - Functional safety - Part1 : Vocabulary[2] Independence and Non-interference: Two Cardinal Concepts to Develop EE Architectures Hosting Safety-Critical Systems。