modeling study

1数谱分布：number size distribution2表面积谱分布3质量浓度谱分布4不同研究者对城市大气气溶胶的质量浓度,化学组份和粒径分布特点进行了研究[Alfarra, 2004b; Allan J D, et al., 2003; Canagaratna, et al., 2007; Duan, et al., 2006; Sun, et al., 2010; Takami A, et al., 20055滤膜6惯性分级装置7凝结!蒸发!成核现象!吸附!吸收化学反应8电分级器对亚微米级粒子进行分离9凝结核计数器(CPC)10空气动力学粒度分级器(APS)11粒子加速喷嘴12激光检测系统13光学粒子计数器(OPC)粒子数量的测量14激光粒子计数器(LPC),15飞行时间粒度分级器16浊度17时间分辨率18飞行时间质谱仪19新粒子生成事件20前体物21正交矩阵因子分析法(PMF)22气溶胶质谱仪(Aerosol mass spectrometer, AMS)）23 1Torr=101325/760=133pa24飞行时间(TOF)25，borealadj. 北的；北方的；北方地区的；北风的26，modal模态27，subroutine子程序28，hygroscopic吸湿的29 ，wind regime风况，风态30，entrainment 带走，夹带Sidewise侧面的Isotropic各向同性的profile分布non-creepingflowskin frictiondissipation耗散scheme模式polymerization聚合作用whilst 同时=whileradiation budget辐射收支平衡albedo反照率megacity 人口超过百万的大城市scavenging净化，清除source rateorder-of-magntudeammoniumbisulphate硫酸请按theorem原理，定理arithmetic算术《大气环境化学（第二版）》唐孝炎张远航邵敏主编高等教育出版社绪论气溶胶霾（灰霾）aerosol haze氟氯烃类CFCs炭黑EC大气自由基OH、HO2、RO2、RO烟雾箱smog chamber电子探针EPMA经验动力学模拟方法empirical kinetic modeling approach EKMA三维城市-区域光化学污染模式CIT、UAM、ROM、RTM、SAQM区域酸沉降模式RADM、STEM-II、ADOM碳氢化合物HC第一章地球的大气环境太阳常数solar constant太阳光通量solar flux源source汇sink储库reservoir非甲烷碳氢化合物NMHCs气体扩张激光诱导荧光技术FAGE激光诱导荧光技术LIF差分吸收光谱技术DOAS化学离子质谱技术CIMS电子自旋共振技术MIESR第二章大气化学组分的源、汇与循环过氧乙酰硝酸酯peroxyacetyl nitrate PAN 光化学氧化剂Ox源清单source inventory扩散模型diffusion model受体扩散模型receptor model化学质量平衡CMB干沉降dry deposition湿沉降wet deposition雨除rainout冲刷（洗脱）washout氧硫化碳COS二甲基硫（（CH3）2S）DMS海水边界层MBL奇氮化合物odd-nitrogen compounds氧化亚氮N2O燃料型NOx fuel NOx温度型NOx thermal NOx鼠尾草sage对异丙基苯甲烷p-cymene异戊（间）二烯isoprene单萜烯monoterpene非甲烷的VOCs NMVOCs有机碳OC元素碳EC实际操作过程定义practical definition多氯联苯PCBs氟氯甲烷CFM一氟三氯甲烷CFC-11或F-11二氟二氯甲烷CFC-12或F-12氟利昂FreonCFC chloroflro carbon哈龙Halon一溴一氯二氟甲烷Halon 1211蒙特利尔议定书Montreal protocol臭氧损耗潜势值ozone depletion potential ODP氢氟氯烃HCFCs氢氟碳化物HFCs全氟代烃类PFCs二恶英类化学物质dioxin-like chemicals DLCs芳香烃受体Ah-R多氯代二苯并二恶英polychlorinated dibenze-p-dioxins PCDDs或CDDs多氯代二苯并呋喃polychlorinated dibenzofurans PCDFs或CDFs多溴代二苯并二恶英polybrominateddibenzodioxins PBDDs或BDDs多溴代二苯并呋喃polybrominateddibenzofurans PBDFs或BDFs同族体congeners2,3,7,8-四氯代二苯并二恶英2,3,7,8-tetrachlorodibenzo-p-dioxin 2,3,7,8-TCDD 国际纯粹化学和应用化学联合会（IUPAC）共平面多氯联苯coplanar PCBs Co-PCBs毒性当量toxic equivalency TEQ毒性当量因子toxicity equivalency factors TEFs光化学氧化剂Ox过氧乙酰基硝酸酯PAN对流层顶折叠tropopause folds过氧丙酰基硝酸酯PPN过氧丁酰基硝酸酯PBN过氧异丁酰基硝酸酯PISOBN过氧苯甲酰基硝酸酯PB2N颗粒particles大气颗粒物particulate matter雨除rainout冲刷washout第三章大气化学反应动力学原理反应的进度extent of reaction级次order假二级pseudo-second-order基元反应elementary reaction总包反应overall reaction反应分子数molecularity阿伦尼乌斯Arrhenius活化能activation energy格罗杜斯-德拉波Grotthus-Draper斯塔克-爱因斯坦Stark-Einstein普朗克Planck朗伯Lambert比尔Beer光化辐射actinic radiation反射率albedo真太阳时间true solar time波特逊Peterson稳态近似法PSSA哈特雷Hartley胡今斯Huggins有机过氧化物ROOH过氧有机酸R(O)OOH过氧醚ROOR动态固体dynamic solid刚性固体rigid solid物质转化系数mass transfer coefficient物质积聚系数mass accommodation coefficient反应摄取系数reactive uptake coefficient表面反应几率reaction probability反应粘滞系数reactive sticking coefficient努森数Knudsen number努森池Knudsen cell漫反射红外傅里叶变换光谱diffuse reflectance infrared Fouier transform spectro scopy DRIFTS初始摄取系数initial uptake coefficient稳态摄取系数steady state uptake coefficient朗格缪尔Langmuir表面吸附水surface absorbed water准液体层quasi-liquid layer一次有机颗粒物（气溶胶）primary organic aerosol POA二次有机颗粒物（气溶胶）secondary organic aerosol SOA第四章对流层气相化学EKMA方法emipirical kinetic modeling approach臭氧等浓度曲线模式OZIPP ozone isopleth plotting package 基于观测的模型OBN observation-based model基于排放的模型EBM emission-based model增量反应性IR incremental reactivity相对增量反应性RIR relative incremental reactivity大气有机物反应活性VOCs reactivity增量反应活性incremental reactivity IR最大O3反应活性MOR maximum《大气环境化学（第二版）》唐孝炎张远航邵敏主编高等教育出版社第五章气溶胶化学气溶胶aerosol颗粒particles大气颗粒物particulate matter超细粒子ultrafine particles总悬浮颗粒物total suspended particulates TSP可吸入颗粒物inhalable particles, IP细粒子fine particle国家环境空气质量标准national ambient air quality standards 一次气溶胶primary aerosol二次气溶胶secondary aerosol均质气溶胶homogenous aerosol单谱气溶胶monodisperse aerosols多谱气溶胶polydisperse aerosols轻雾mist浓雾fog粉尘dust烟尘（气）fume烟smoke烟雾smog烟炱soot霾haze积聚模accumulation mode粗粒子模coarse particle mode碰并coagulation扩散diffusion凝聚模condensation mode液滴模态droplet mode爱根核模Aitken nuclei mode水-硫酸均相成核binary water-sulphuric acid nucleation水-硫酸-氨均相成核ternary water-sulphuric acid-ammonia nucleation 离子诱导成核Ion-induced nucleation冷凝汇condensation sink CS非海盐硫酸盐nss-SO42-气溶胶粒子中的有机物particulate organic matter POM多环芳烃polycyclic aromatic hydrocarbons PAHs水溶性有机物WSOC云凝结核CCN因子分析法factor analysis FA主因子分析principal factor analysis目标变换因子分析TTFA化学质量平衡CMB正矩阵因数分解PMF第六章酸沉降化学酸沉降acid deposition湿沉降wet deposition干沉降dry deposition酸雨acid rain acid precipitation守恒conservative云内清除或雨除in-cloud scavenging or rain-out云下清除或冲刷below-cloud scavenging or washout供应系数accommodation coefficient黏附系数sticking coefficient非降水层云non-precipitating liquid water stratiform clouds第七章大气化学传输模式系统发展公司System Development Corporation大众研究公司General Research Corporation系统、科学与软件公司System, Science and Software加州理工学院/系统应用有限责任公司CIT/Systems Applications Inc.经验动力学模拟方法empirical kinetic modeling approach EKMA四维数据同化技术FDDA离线模拟方法off-line approach在线模拟方法on-line approachFREDS flexible regional emission data systemEPS emission preprocessor systemsEMS-95 emissions modeling system-1995SMOKE sparse matrix operator kernel emissions modeling system 平流通量advection flux湍流通量turbulent flux算符分离operator splitting碳键机理carbon bond mechanismSAPRC statewide air pollution research centerRADM2 the second regional acid deposition model mechanism RACM regional atmospheric chemistry mechanism酚氧自由基PHOMPRM morphecule photochemical reaction mechanismUAM-IV urban airshed model version IVCIT California/Carnegie institute of technology modelCALGRID California air resources board grid modelROM regional oxidantal modelRTM-III regional transport model version IIILOTOS long term ozone simulation modelUAM-V urban airshed model variableSAQM SAMAP air quality modelCAMx comprehensive air quality model with extensionsPIG plume-in-gridSTEM-III sulfur transport and emissions modelADOM acid deposition and oxidant modelEURAD European acid deposition model协同潮解相对湿度mutual deliquescence relative humidityCIT-AERO CIT aerosolGATOR gas, aerosol, transport, radiation and meteorological model RPM regional particulate modelUAM-AERO UAM aerosol操作评价operational evaluation诊断评价diagnostic evaluation臭氧源识别技术ozone source apportionment technology OSAT第八章气候变化的大气化学原理天然温室效应natural greenhouse effect增强的温室效应enhanced greenhouse effect辐射强迫radioactive forcing全球变暖潜势global warming potential GWP消光佯谬extinction paradox云凝结核cloud condensation nuclei CCN云滴数浓度cloud condensation nuclei CCNC卷云cirrus冰的凝结核Ice Nuclei凝结尾迹contrails潮解相对湿度deliquescence relative humidity DRH行星边界层planetary boundary layer城市热岛效应urban heat island effect非甲烷烃化合物nonmethane hydrocarbons NMHCs细雨drizzle极地平流层云polar stratosphere clouds PSCsSSU Stratospheric Sounding UnitWSU Microwave Sounding Unit涡旋相关法eddy correlation或eddy covariance EC松弛涡旋积累法relaxed eddy accumulation REA空气动力学梯度法aerodynamic gradient method AGM 羰基硫COS第九章平流层化学臭氧洞ozone hole大气臭氧的损耗ozone depletion臭氧损失ozone loss源分子source molecules自由基radicals储库（汇）分子reservoir/sink molecules临时储库分子temporal reservoirs极地平流层云polar stratospheric clouds PSCs极地涡旋polar vortex去活化过程denoxification再活化过程renoxification脱氮作用denitrification脱水作用dehydrate高速民用运输机high-speed civil transport HSCT消耗臭氧层物质ozone-depleting substances ODS等效活性氯effective equivalent chlorine EECl极地涡旋polar vortex第十章室内空气污染病态建筑综合症SBS国际癌症研究机构IARC静电除尘器electrostatic air filters穿透penetration亚油酸linoleic acid亚麻酸linolenic acid双自由基biradical二次气溶胶secondary organic aerosol双自由基criegeebiradical室内化学和暴露模型ICME indoor chemistry and exposure model 三甲基苯trimethyl benzene清除剂scavenger全氯乙烯perchloroethylene柠檬烯d-limonene羧基血红素COHb酚醛树脂PF强挥发性有机物VVOCs苯benzene甲苯toluene乙苯ethylbenzene邻二甲苯o-xyleneATSDR agency for toxic substances and disease registry苯并[a]芘B[a]P蒎烯pinene国家癌症研究所National Cancer Institute国家疾病预防控制中心Centers for Disease Control and Prevention 美国儿童研究学院American Academy of Pediatrics苯汞基phenylmercuric acetate PMA现场和实验室释放小室field and laboratory emission cell FLEC统计模型statistical models环境总暴露total exposure流体力学模型CFD模型测试房test-house美国食品药品监督管理局FDA职业安全和健康管理部OSHA能源部DOE消费产品安全委员会CPSC未观测到不良反应的暴露水平NOAEL参考剂量RFD终生日平均剂量LADD慢性日摄入量CDI室内空气质量参数indoor air quality parameter可吸入颗粒物particles with diameters of 10μm or less PM10总挥发性有机化合物total volatile organic compounds TVOC标准状态normal state均质层homosphere（80-100km）非均质层heterosphere（100km以上）电离层ionosphere根据大气的温度分布再把均质层分为三层｛对流层（troposphere），平流层（stratosphere），中间层mesosphere 对流层（边界层（1到几千米），自由对流层）。

Modeling the Spatial Dynamics of Regional Land Use:The CLUE-S ModelPETER H.VERBURG*Department of Environmental Sciences Wageningen UniversityP.O.Box376700AA Wageningen,The NetherlandsandFaculty of Geographical SciencesUtrecht UniversityP.O.Box801153508TC Utrecht,The NetherlandsWELMOED SOEPBOERA.VELDKAMPDepartment of Environmental Sciences Wageningen UniversityP.O.Box376700AA Wageningen,The NetherlandsRAMIL LIMPIADAVICTORIA ESPALDONSchool of Environmental Science and Management University of the Philippines Los Ban˜osCollege,Laguna4031,Philippines SHARIFAH S.A.MASTURADepartment of GeographyUniversiti Kebangsaan Malaysia43600BangiSelangor,MalaysiaABSTRACT/Land-use change models are important tools for integrated environmental management.Through scenario analysis they can help to identify near-future critical locations in the face of environmental change.A dynamic,spatially ex-plicit,land-use change model is presented for the regional scale:CLUE-S.The model is specifically developed for the analysis of land use in small regions(e.g.,a watershed or province)at afine spatial resolution.The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysi-cal driving factors.The model explicitly addresses the hierar-chical organization of land use systems,spatial connectivity between locations and stability.Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion.The user can specify these set-tings based on expert knowledge or survey data.Two appli-cations of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.Land-use change is central to environmental man-agement through its influence on biodiversity,water and radiation budgets,trace gas emissions,carbon cy-cling,and livelihoods(Lambin and others2000a, Turner1994).Land-use planning attempts to influence the land-use change dynamics so that land-use config-urations are achieved that balance environmental and stakeholder needs.Environmental management and land-use planning therefore need information about the dynamics of land use.Models can help to understand these dynamics and project near future land-use trajectories in order to target management decisions(Schoonenboom1995).Environmental management,and land-use planning specifically,take place at different spatial and organisa-tional levels,often corresponding with either eco-re-gional or administrative units,such as the national or provincial level.The information needed and the man-agement decisions made are different for the different levels of analysis.At the national level it is often suffi-cient to identify regions that qualify as“hot-spots”of land-use change,i.e.,areas that are likely to be faced with rapid land use conversions.Once these hot-spots are identified a more detailed land use change analysis is often needed at the regional level.At the regional level,the effects of land-use change on natural resources can be determined by a combina-tion of land use change analysis and specific models to assess the impact on natural resources.Examples of this type of model are water balance models(Schulze 2000),nutrient balance models(Priess and Koning 2001,Smaling and Fresco1993)and erosion/sedimen-tation models(Schoorl and Veldkamp2000).Most of-KEY WORDS:Land-use change;Modeling;Systems approach;Sce-nario analysis;Natural resources management*Author to whom correspondence should be addressed;email:pverburg@gissrv.iend.wau.nlDOI:10.1007/s00267-002-2630-x Environmental Management Vol.30,No.3,pp.391–405©2002Springer-Verlag New York Inc.ten these models need high-resolution data for land use to appropriately simulate the processes involved.Land-Use Change ModelsThe rising awareness of the need for spatially-ex-plicit land-use models within the Land-Use and Land-Cover Change research community(LUCC;Lambin and others2000a,Turner and others1995)has led to the development of a wide range of land-use change models.Whereas most models were originally devel-oped for deforestation(reviews by Kaimowitz and An-gelsen1998,Lambin1997)more recent efforts also address other land use conversions such as urbaniza-tion and agricultural intensification(Brown and others 2000,Engelen and others1995,Hilferink and Rietveld 1999,Lambin and others2000b).Spatially explicit ap-proaches are often based on cellular automata that simulate land use change as a function of land use in the neighborhood and a set of user-specified relations with driving factors(Balzter and others1998,Candau 2000,Engelen and others1995,Wu1998).The speci-fication of the neighborhood functions and transition rules is done either based on the user’s expert knowl-edge,which can be a problematic process due to a lack of quantitative understanding,or on empirical rela-tions between land use and driving factors(e.g.,Pi-janowski and others2000,Pontius and others2000).A probability surface,based on either logistic regression or neural network analysis of historic conversions,is made for future conversions.Projections of change are based on applying a cut-off value to this probability sur-face.Although appropriate for short-term projections,if the trend in land-use change continues,this methodology is incapable of projecting changes when the demands for different land-use types change,leading to a discontinua-tion of the trends.Moreover,these models are usually capable of simulating the conversion of one land-use type only(e.g.deforestation)because they do not address competition between land-use types explicitly.The CLUE Modeling FrameworkThe Conversion of Land Use and its Effects(CLUE) modeling framework(Veldkamp and Fresco1996,Ver-burg and others1999a)was developed to simulate land-use change using empirically quantified relations be-tween land use and its driving factors in combination with dynamic modeling.In contrast to most empirical models,it is possible to simulate multiple land-use types simultaneously through the dynamic simulation of competition between land-use types.This model was developed for the national and con-tinental level,applications are available for Central America(Kok and Winograd2001),Ecuador(de Kon-ing and others1999),China(Verburg and others 2000),and Java,Indonesia(Verburg and others 1999b).For study areas with such a large extent the spatial resolution of analysis was coarse(pixel size vary-ing between7ϫ7and32ϫ32km).This is a conse-quence of the impossibility to acquire data for land use and all driving factors atfiner spatial resolutions.A coarse spatial resolution requires a different data rep-resentation than the common representation for data with afine spatial resolution.Infine resolution grid-based approaches land use is defined by the most dom-inant land-use type within the pixel.However,such a data representation would lead to large biases in the land-use distribution as some class proportions will di-minish and other will increase with scale depending on the spatial and probability distributions of the cover types(Moody and Woodcock1994).In the applications of the CLUE model at the national or continental level we have,therefore,represented land use by designating the relative cover of each land-use type in each pixel, e.g.a pixel can contain30%cultivated land,40%grass-land,and30%forest.This data representation is di-rectly related to the information contained in the cen-sus data that underlie the applications.For each administrative unit,census data denote the number of hectares devoted to different land-use types.When studying areas with a relatively small spatial ex-tent,we often base our land-use data on land-use maps or remote sensing images that denote land-use types respec-tively by homogeneous polygons or classified pixels. When converted to a raster format this results in only one, dominant,land-use type occupying one unit of analysis. The validity of this data representation depends on the patchiness of the landscape and the pixel size chosen. Most sub-national land use studies use this representation of land use with pixel sizes varying between a few meters up to about1ϫ1km.The two different data represen-tations are shown in Figure1.Because of the differences in data representation and other features that are typical for regional appli-cations,the CLUE model can not directly be applied at the regional scale.This paper describes the mod-ified modeling approach for regional applications of the model,now called CLUE-S(the Conversion of Land Use and its Effects at Small regional extent). The next section describes the theories underlying the development of the model after which it is de-scribed how these concepts are incorporated in the simulation model.The functioning of the model is illustrated for two case-studies and is followed by a general discussion.392P.H.Verburg and othersCharacteristics of Land-Use SystemsThis section lists the main concepts and theories that are prevalent for describing the dynamics of land-use change being relevant for the development of land-use change models.Land-use systems are complex and operate at the interface of multiple social and ecological systems.The similarities between land use,social,and ecological systems allow us to use concepts that have proven to be useful for studying and simulating ecological systems in our analysis of land-use change (Loucks 1977,Adger 1999,Holling and Sanderson 1996).Among those con-cepts,connectivity is important.The concept of con-nectivity acknowledges that locations that are at a cer-tain distance are related to each other (Green 1994).Connectivity can be a direct result of biophysical pro-cesses,e.g.,sedimentation in the lowlands is a direct result of erosion in the uplands,but more often it is due to the movement of species or humans through the nd degradation at a certain location will trigger farmers to clear land at a new location.Thus,changes in land use at this new location are related to the land-use conditions in the other location.In other instances more complex relations exist that are rooted in the social and economic organization of the system.The hierarchical structure of social organization causes some lower level processes to be constrained by higher level dynamics,e.g.,the establishments of a new fruit-tree plantation in an area near to the market might in fluence prices in such a way that it is no longer pro fitable for farmers to produce fruits in more distant areas.For studying this situation an-other concept from ecology,hierarchy theory,is use-ful (Allen and Starr 1982,O ’Neill and others 1986).This theory states that higher level processes con-strain lower level processes whereas the higher level processes might emerge from lower level dynamics.This makes the analysis of the land-use system at different levels of analysis necessary.Connectivity implies that we cannot understand land use at a certain location by solely studying the site characteristics of that location.The situation atneigh-Figure 1.Data representation and land-use model used for respectively case-studies with a national/continental extent and local/regional extent.Modeling Regional Land-Use Change393boring or even more distant locations can be as impor-tant as the conditions at the location itself.Land-use and land-cover change are the result of many interacting processes.Each of these processes operates over a range of scales in space and time.These processes are driven by one or more of these variables that influence the actions of the agents of land-use and cover change involved.These variables are often re-ferred to as underlying driving forces which underpin the proximate causes of land-use change,such as wood extraction or agricultural expansion(Geist and Lambin 2001).These driving factors include demographic fac-tors(e.g.,population pressure),economic factors(e.g., economic growth),technological factors,policy and institutional factors,cultural factors,and biophysical factors(Turner and others1995,Kaimowitz and An-gelsen1998).These factors influence land-use change in different ways.Some of these factors directly influ-ence the rate and quantity of land-use change,e.g.the amount of forest cleared by new incoming migrants. Other factors determine the location of land-use change,e.g.the suitability of the soils for agricultural land use.Especially the biophysical factors do pose constraints to land-use change at certain locations, leading to spatially differentiated pathways of change.It is not possible to classify all factors in groups that either influence the rate or location of land-use change.In some cases the same driving factor has both an influ-ence on the quantity of land-use change as well as on the location of land-use change.Population pressure is often an important driving factor of land-use conver-sions(Rudel and Roper1997).At the same time it is the relative population pressure that determines which land-use changes are taking place at a certain location. Intensively cultivated arable lands are commonly situ-ated at a limited distance from the villages while more extensively managed grasslands are often found at a larger distance from population concentrations,a rela-tion that can be explained by labor intensity,transport costs,and the quality of the products(Von Thu¨nen 1966).The determination of the driving factors of land use changes is often problematic and an issue of dis-cussion(Lambin and others2001).There is no unify-ing theory that includes all processes relevant to land-use change.Reviews of case studies show that it is not possible to simply relate land-use change to population growth,poverty,and infrastructure.Rather,the inter-play of several proximate as well as underlying factors drive land-use change in a synergetic way with large variations caused by location specific conditions (Lambin and others2001,Geist and Lambin2001).In regional modeling we often need to rely on poor data describing this complexity.Instead of using the under-lying driving factors it is needed to use proximate vari-ables that can represent the underlying driving factors. Especially for factors that are important in determining the location of change it is essential that the factor can be mapped quantitatively,representing its spatial vari-ation.The causality between the underlying driving factors and the(proximate)factors used in modeling (in this paper,also referred to as“driving factors”) should be certified.Other system properties that are relevant for land-use systems are stability and resilience,concepts often used to describe ecological systems and,to some extent, social systems(Adger2000,Holling1973,Levin and others1998).Resilience refers to the buffer capacity or the ability of the ecosystem or society to absorb pertur-bations,or the magnitude of disturbance that can be absorbed before a system changes its structure by changing the variables and processes that control be-havior(Holling1992).Stability and resilience are con-cepts that can also be used to describe the dynamics of land-use systems,that inherit these characteristics from both ecological and social systems.Due to stability and resilience of the system disturbances and external in-fluences will,mostly,not directly change the landscape structure(Conway1985).After a natural disaster lands might be abandoned and the population might tempo-rally migrate.However,people will in most cases return after some time and continue land-use management practices as before,recovering the land-use structure (Kok and others2002).Stability in the land-use struc-ture is also a result of the social,economic,and insti-tutional structure.Instead of a direct change in the land-use structure upon a fall in prices of a certain product,farmers will wait a few years,depending on the investments made,before they change their cropping system.These characteristics of land-use systems provide a number requirements for the modelling of land-use change that have been used in the development of the CLUE-S model,including:●Models should not analyze land use at a single scale,but rather include multiple,interconnected spatial scales because of the hierarchical organization of land-use systems.●Special attention should be given to the drivingfactors of land-use change,distinguishing drivers that determine the quantity of change from drivers of the location of change.●Sudden changes in driving factors should not di-rectly change the structure of the land-use system asa consequence of the resilience and stability of theland-use system.394P.H.Verburg and others●The model structure should allow spatial interac-tions between locations and feedbacks from higher levels of organization.Model DescriptionModel StructureThe model is sub-divided into two distinct modules,namely a non-spatial demand module and a spatially explicit allocation procedure (Figure 2).The non-spa-tial module calculates the area change for all land-use types at the aggregate level.Within the second part of the model these demands are translated into land-use changes at different locations within the study region using a raster-based system.For the land-use demand module,different alterna-tive model speci fications are possible,ranging from simple trend extrapolations to complex economic mod-els.The choice for a speci fic model is very much de-pendent on the nature of the most important land-use conversions taking place within the study area and the scenarios that need to be considered.Therefore,the demand calculations will differ between applications and scenarios and need to be decided by the user for the speci fic situation.The results from the demandmodule need to specify,on a yearly basis,the area covered by the different land-use types,which is a direct input for the allocation module.The rest of this paper focuses on the procedure to allocate these demands to land-use conversions at speci fic locations within the study area.The allocation is based upon a combination of em-pirical,spatial analysis,and dynamic modelling.Figure 3gives an overview of the procedure.The empirical analysis unravels the relations between the spatial dis-tribution of land use and a series of factors that are drivers and constraints of land use.The results of this empirical analysis are used within the model when sim-ulating the competition between land-use types for a speci fic location.In addition,a set of decision rules is speci fied by the user to restrict the conversions that can take place based on the actual land-use pattern.The different components of the procedure are now dis-cussed in more detail.Spatial AnalysisThe pattern of land use,as it can be observed from an airplane window or through remotely sensed im-ages,reveals the spatial organization of land use in relation to the underlying biophysical andsocio-eco-Figure 2.Overview of the modelingprocedure.Figure 3.Schematic represen-tation of the procedure to allo-cate changes in land use to a raster based map.Modeling Regional Land-Use Change395nomic conditions.These observations can be formal-ized by overlaying this land-use pattern with maps de-picting the variability in biophysical and socio-economic conditions.Geographical Information Systems(GIS)are used to process all spatial data and convert these into a regular grid.Apart from land use, data are gathered that represent the assumed driving forces of land use in the study area.The list of assumed driving forces is based on prevalent theories on driving factors of land-use change(Lambin and others2001, Kaimowitz and Angelsen1998,Turner and others 1993)and knowledge of the conditions in the study area.Data can originate from remote sensing(e.g., land use),secondary statistics(e.g.,population distri-bution),maps(e.g.,soil),and other sources.To allow a straightforward analysis,the data are converted into a grid based system with a cell size that depends on the resolution of the available data.This often involves the aggregation of one or more layers of thematic data,e.g. it does not make sense to use a30-m resolution if that is available for land-use data only,while the digital elevation model has a resolution of500m.Therefore, all data are aggregated to the same resolution that best represents the quality and resolution of the data.The relations between land use and its driving fac-tors are thereafter evaluated using stepwise logistic re-gression.Logistic regression is an often used method-ology in land-use change research(Geoghegan and others2001,Serneels and Lambin2001).In this study we use logistic regression to indicate the probability of a certain grid cell to be devoted to a land-use type given a set of driving factors following:LogͩP i1ϪP i ͪϭ␤0ϩ␤1X1,iϩ␤2X2,i......ϩ␤n X n,iwhere P i is the probability of a grid cell for the occur-rence of the considered land-use type and the X’s are the driving factors.The stepwise procedure is used to help us select the relevant driving factors from a larger set of factors that are assumed to influence the land-use pattern.Variables that have no significant contribution to the explanation of the land-use pattern are excluded from thefinal regression equation.Where in ordinal least squares regression the R2 gives a measure of modelfit,there is no equivalent for logistic regression.Instead,the goodness offit can be evaluated with the ROC method(Pontius and Schnei-der2000,Swets1986)which evaluates the predicted probabilities by comparing them with the observed val-ues over the whole domain of predicted probabilities instead of only evaluating the percentage of correctly classified observations at afixed cut-off value.This is an appropriate methodology for our application,because we will use a wide range of probabilities within the model calculations.The influence of spatial autocorrelation on the re-gression results can be minimized by only performing the regression on a random sample of pixels at a certain minimum distance from one another.Such a selection method is adopted in order to maximize the distance between the selected pixels to attenuate the problem associated with spatial autocorrelation.For case-studies where autocorrelation has an important influence on the land-use structure it is possible to further exploit it by incorporating an autoregressive term in the regres-sion equation(Overmars and others2002).Based upon the regression results a probability map can be calculated for each land-use type.A new probabil-ity map is calculated every year with updated values for the driving factors that are projected to change in time,such as the population distribution or accessibility.Decision RulesLand-use type or location specific decision rules can be specified by the user.Location specific decision rules include the delineation of protected areas such as nature reserves.If a protected area is specified,no changes are allowed within this area.For each land-use type decision rules determine the conditions under which the land-use type is allowed to change in the next time step.These decision rules are implemented to give certain land-use types a certain resistance to change in order to generate the stability in the land-use structure that is typical for many landscapes.Three different situations can be distinguished and for each land-use type the user should specify which situation is most relevant for that land-use type:1.For some land-use types it is very unlikely that theyare converted into another land-use type after their first conversion;as soon as an agricultural area is urbanized it is not expected to return to agriculture or to be converted into forest cover.Unless a de-crease in area demand for this land-use type occurs the locations covered by this land use are no longer evaluated for potential land-use changes.If this situation is selected it also holds that if the demand for this land-use type decreases,there is no possi-bility for expansion in other areas.In other words, when this setting is applied to forest cover and deforestation needs to be allocated,it is impossible to reforest other areas at the same time.2.Other land-use types are converted more easily.Aswidden agriculture system is most likely to be con-verted into another land-use type soon after its396P.H.Verburg and othersinitial conversion.When this situation is selected for a land-use type no restrictions to change are considered in the allocation module.3.There is also a number of land-use types that oper-ate in between these two extremes.Permanent ag-riculture and plantations require an investment for their establishment.It is therefore not very likely that they will be converted very soon after into another land-use type.However,in the end,when another land-use type becomes more pro fitable,a conversion is possible.This situation is dealt with by de fining the relative elasticity for change (ELAS u )for the land-use type into any other land use type.The relative elasticity ranges between 0(similar to Situation 2)and 1(similar to Situation 1).The higher the de fined elasticity,the more dif ficult it gets to convert this land-use type.The elasticity should be de fined based on the user ’s knowledge of the situation,but can also be tuned during the calibration of the petition and Actual Allocation of Change Allocation of land-use change is made in an iterative procedure given the probability maps,the decision rules in combination with the actual land-use map,and the demand for the different land-use types (Figure 4).The following steps are followed in the calculation:1.The first step includes the determination of all grid cells that are allowed to change.Grid cells that are either part of a protected area or under a land-use type that is not allowed to change (Situation 1,above)are excluded from further calculation.2.For each grid cell i the total probability (TPROP i,u )is calculated for each of the land-use types u accord-ing to:TPROP i,u ϭP i,u ϩELAS u ϩITER u ,where ITER u is an iteration variable that is speci fic to the land use.ELAS u is the relative elasticity for change speci fied in the decision rules (Situation 3de-scribed above)and is only given a value if grid-cell i is already under land use type u in the year considered.ELAS u equals zero if all changes are allowed (Situation 2).3.A preliminary allocation is made with an equalvalue of the iteration variable (ITER u )for all land-use types by allocating the land-use type with the highest total probability for the considered grid cell.This will cause a number of grid cells to change land use.4.The total allocated area of each land use is nowcompared to the demand.For land-use types where the allocated area is smaller than the demanded area the value of the iteration variable is increased.For land-use types for which too much is allocated the value is decreased.5.Steps 2to 4are repeated as long as the demandsare not correctly allocated.When allocation equals demand the final map is saved and the calculations can continue for the next yearly timestep.Figure 5shows the development of the iteration parameter ITER u for different land-use types during asimulation.Figure 4.Representation of the iterative procedure for land-use changeallocation.Figure 5.Change in the iteration parameter (ITER u )during the simulation within one time-step.The different lines rep-resent the iteration parameter for different land-use types.The parameter is changed for all land-use types synchronously until the allocated land use equals the demand.Modeling Regional Land-Use Change397Multi-Scale CharacteristicsOne of the requirements for land-use change mod-els are multi-scale characteristics.The above described model structure incorporates different types of scale interactions.Within the iterative procedure there is a continuous interaction between macro-scale demands and local land-use suitability as determined by the re-gression equations.When the demand changes,the iterative procedure will cause the land-use types for which demand increased to have a higher competitive capacity (higher value for ITER u )to ensure enough allocation of this land-use type.Instead of only being determined by the local conditions,captured by the logistic regressions,it is also the regional demand that affects the actually allocated changes.This allows the model to “overrule ”the local suitability,it is not always the land-use type with the highest probability according to the logistic regression equation (P i,u )that the grid cell is allocated to.Apart from these two distinct levels of analysis there are also driving forces that operate over a certain dis-tance instead of being locally important.Applying a neighborhood function that is able to represent the regional in fluence of the data incorporates this type of variable.Population pressure is an example of such a variable:often the in fluence of population acts over a certain distance.Therefore,it is not the exact location of peoples houses that determines the land-use pattern.The average population density over a larger area is often a more appropriate variable.Such a population density surface can be created by a neighborhood func-tion using detailed spatial data.The data generated this way can be included in the spatial analysis as anotherindependent factor.In the application of the model in the Philippines,described hereafter,we applied a 5ϫ5focal filter to the population map to generate a map representing the general population pressure.Instead of using these variables,generated by neighborhood analysis,it is also possible to use the more advanced technique of multi-level statistics (Goldstein 1995),which enable a model to include higher-level variables in a straightforward manner within the regression equa-tion (Polsky and Easterling 2001).Application of the ModelIn this paper,two examples of applications of the model are provided to illustrate its function.TheseTable nd-use classes and driving factors evaluated for Sibuyan IslandLand-use classes Driving factors (location)Forest Altitude (m)GrasslandSlope Coconut plantation AspectRice fieldsDistance to town Others (incl.mangrove and settlements)Distance to stream Distance to road Distance to coast Distance to port Erosion vulnerability GeologyPopulation density(neighborhood 5ϫ5)Figure 6.Location of the case-study areas.398P.H.Verburg and others。

5052铝合金冲压成形过程中韧性断裂的仿真研究余海燕;王友【摘要】对5052铝合金进行单向拉伸试验，使用试验曲线拟合Voc e模型参数。

观察拉伸试样断口形貌，并使用光学显微镜测量拉伸试样断口的最小厚度。

结合单向拉伸仿真和试验结果，求解得到Cockcroft-Latham 韧性断裂准则中的材料参数。

将Voce 模型和Cockcroft-Latham 韧性断裂准则引入球头胀形仿真，并进行试验对比。

结果表明：采用该拟合的Voce 模型和Cockcroft-Latham韧性断裂准则预测所得零件开裂位置和裂口形状与试验结果吻合，采用的基于有限元仿真与简单试验相结合的材料参数反求方法具有求解方便、计算精度高的优点。

%The uniaxial tensile tests were conducted on 5052 aluminum alloy and Voce model parameters were determined by fitting with the experimental curves. The fracture surface was observed and the minimum thickness of it was measured with optical microscope. The material parameter of Cockcroft-Latham ductile damage criteria was achieved through uniaxial tensile simulation and test results. Voce model and Cockcroft-Latham ductile damage criterion were introduced into the numerical simulation of spherical bulging and simulation approach was employed to compare with the experimental results. The results show that the position and shape of the fracture surface simulated with Voce model and Cockcroft-Latham ductile damage criteria are in good agreement with the experimental ones. The method of material parameters identification based on finite element simulation and simple tests has high accuracy and can be applied conveniently.【期刊名称】《中国有色金属学报》【年(卷),期】2015(000)011【总页数】7页(P2975-2981)【关键词】铝合金;球头胀形;韧性断裂;数值模拟【作者】余海燕;王友【作者单位】同济大学汽车学院，上海 201804;同济大学汽车学院，上海 201804【正文语种】中文【中图分类】TG389铝合金由于具有密度低、耐腐蚀高、比强度高等特点，近来作为重要的轻质材料在汽车制造中被广泛使用[1-3]。

bight翻译bight翻译：海湾bight n.海湾双语例句：1，Electric control of production equipment for no joint wire rope bight无接头钢丝绳绳圈生产设备的电气控制2，Study on ecological monitoring using meiobenthos in Southern Bight of North Sea小型底栖生物在北海南部湾生态监测中的应用研究3，Preliminary Study on Increasing Effect of Biocontrol Against Rice Sheath Bight by Mixed Bacteria Culture混合菌培养提高水稻纹枯病生防效果的研究4，So bight dog eyes, so lovely, right?多么明亮的狗眼！好可爱，是吗？5，Numerical Modeling Experiment of the Tidal Current in the Bight of Fengcheng Harbour防城港湾潮流数值模拟试验6，A block that can be opened to receive the bight of a rope.打开后能够获得绳的曲线的滑轮。

7，I am seeing the sun, it is very bight.我看着太阳，它非常的明亮刺眼。

8，Grab the tip of the bight and pull it through the loop.抓住绳圈的顶部，并把它穿过绳圈。

9，The hall was so bight and dark, so grave and gay.大厅既光彩夺目，又朦胧黑暗，即庄严肃穆，又是轻松愉快。

10，Recognition of these characteristics is beneficial to exploring the law of bight r education认识中国近代高等教育演变的双重特征，有助于探索高等教育发展规律，为当今高等教育改革提供有益的历史借鉴。

Qualitative ResearchAn Introduction Associate Professor Dr Kamal KennyLearning Objectives Understand . . .•How qualitative methodologies differ from quantitative methodologies.•The controversy surrounding qualitative research.•The types of decisions that use qualitative methodologies.•The different qualitative research methodologies.7-2Qualitative Research•Qualitative research is an interdisciplinary, transdisciplinary, and sometimes counterdisciplinary field.It crosses the humanities and the social and physical sciences. Qualitative research is many things at the same time. It is multiparadigmatic in focus. Its practitioners are sensitive to the value of the multimethod approach. They are committed to the naturalistic perspective, and to the interpretative understanding of human experience. At the same time, the field is inherently political and shaped by multiple ethical and political positions.•Nelson et al’s (1992, p4)Qualitative Research•‘Qualitative Research…involves finding out what people think, and how they feel -or at any rate, what they say they think and how they say theyfeel. This kind of information is subjective. Itinvolves feelings and impressions, rather thannumbers’•Bellenger, Bernhardt and Goldstucker, Qualitative Research inMarketing, American Marketing AssociationQualitative Research•Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.•Qualitative Researchers study “things” (people and their thoughts) in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.Qualitative Research•Qualitative research involves the studied use and collection of a variety of empirical materials -case study, personal experience, introspective, life story, interview,observational, historical, interactional, and visual texts-that describe routine and problematic moments and meanings in individuals lives.•Deploy a wide range of interconnected methods, hoping always to get a better fix on the subject matter at hand.Bricoleur•Bricoleur• A ‘Jack of all trades or kind of professional DIY person’•Produces a bricolage, that is a pieced together, close-knit set of practices that provide solutions to a problem in aconcrete situation•The solution which is a result of the bricoleurs method is an emergent construction that changes and takes newforms as different tools, methods and techniques are added to the puzzle.Bricoleur•The Qualitative Researcher as Bricoleur uses the tools of his methodological trade . The choice of research practices depends upon the questions that are asked, and thequestions depend on their context, what is available in the context, and what the researcher can do in that setting.•The Bricoleur is adept at performing a large number of diverse tasks ranging from interviewing to observing, to interpreting personal and historical documents, to intensive self-reflection and introspection.Bricoleur•The bricoleur understands that research is an interactive process shaped by his own personal history, biography,gender, social class, race, and ethnicity and those of thepeople in the setting.•The product of the bricoleur’s labour is a bricolage, a complex, dense, reflexive, collage-like creation thatrepresents the researchers images, understanding andinterpretations of the world or phenomenon under analysis.•The bricolage will connect the parts to the whole, stressing the meaningful relationships that operate in the situations and social worlds studied.Positivist Paradigm•Emphasises that human reason is supreme and that there is a single objective truth that can be discovered by science•Encourages us to stress the function of objects, celebrate technology and to regard the world as a rational, ordered place with a clearly defined past, present and futureNon-Positivist Paradigm•Questions the assumptions of the positivist paradigm •Argues that our society places too much emphasis on science and technology•Argues that this ordered, rational view of consumers denies the complexity of the social and cultural world we live in •Stresses the importance of symbolic, subjective experienceThe Five moments of QualitativeResearchTraditional Period:1900’s-World War II •Wrote objective colonising accounts of field experiences that were reflective of the positivist scientist paradigm•Concerned with offering valid, reliable, and objective interpretations in their writings.•The ‘subject’ who was studied was alien, foreign, and strange.The Modernist PhasePost war-1970’s•The modernist ethnographer and sociological participant observer attempted rigorous, qualitative studies of important social processes, including social control in the classroom and society •Researchers were drawn to qualitative research because it allowed them to give a voice to society’s ‘underclass’Blurred Genres1970-1986•Researchers had a full complement of paradigms, methods and strategies•Applied qualitative research was gaining in stature•Research strategies ranged from grounded theory to the case study methodology•Methods included qualitative interviewing and observational, visual, personal and documentary methods.•Computers were becoming more prevalent•Boundaries between the social sciences and humanities had become blurred•Social science was borrowing models, theories and methods of analysis from the humanities•Researcher acknowledged as being part of the research processPopularity of QualitativeResearch1Usually much cheaper than quantitative research2No better way than qualitative research to understand in-depth the motivations and feelings of consumers3Qualitative research can improve the efficiency and effectiveness of quantitative researchLimitations of QualitativeResearch1Marketing successes and failures are based on small differences in the marketing mix.Qualitative research doesn’t distinguish these differences as well as quantitative research can.2Not representative of the population that is of interest to the researcher3The multitude of individuals who, without formal training, profess to be experts in the fieldQualitative Research as a Process•Theory•Method•Analysis•All three interconnect to define the qualitative research processTheoretical ApproachDeductive•Deductive Theoretical Approach•Seek to use existing theory to shape the approach which you adopt to the qualitative research process and to aspects of data analysis •Analytical Procedures•Pattern Matching•Involves predicting a pattern of outcomes based on theoretical propositions to explain what you expect to find•Explanation Building•Involves attempting to build an explanation while collecting and analysing the data, rather than testing a predicted explanation as in pattern matchingInductive Approach•Inductive Theoretical Approach•Seek to build up a theory which is adequately grounded in a number of relevant cases. Referred to as Interpretative and Grounded Theory•Art of Interpretation•Field Text:Consists of field notes and documents from the field •Research Text:Notes and interpretations based on the filed text •Working interpretative document: Writers initial attempt to make sense out of what he has learned•Public Text:The final tale of the FieldQualitative Data CollectionTechniques•In depth Interviewing•Focus Groups•Participant Observations •Ethnographic Studies•Projective TechniquesAnalysis Qualitative Data:An Approach•Categorisation•Unitising data•Recognising relationships and developing the categories you are using to facilitate this •Developing and testing hypotheses to reach conclusionInteractive Nature of theQualitative Process•Data collection, data analysis and the development and verification of relationships and conclusion are all interrelated and interactive set of processes •Allows researcher to recognise important themes, patterns and relationships as you collect data •Allows you to re-categorise existing data to see whether themes and patterns and relationships exist in the data already collected•Allows you to adjust your future data collection approach to see whether they exist in other casesTools for helping the AnalyticalProcess•Summaries•Should contain the key points that emerge from undertaking the specific activity•Self Memos•Allow you to make a record of the ideas which occur to you about any aspect of your research,asyou think of them•Researcher DiaryData Collection TechniquesAction Research Grounded TheoryOther TechniquesQualitative Research in Business•Job Analysis •Advertising Concept Development •Productivity Enhancement •New Product Development •Benefits Management •Retail Design •Process Understanding •Union Representation •Market Segmentation •Sales AnalysisQualitative•Understanding •InterpretationFocus of ResearchQuantitative •Description •ExplanationResearcher InvolvementQualitative•High•Participation-basedQuantitative•Limited•ControlledResearch DesignQualitative•Longitudinal•Multi-methodQuantitative•Cross-sectional orlongitudinal•Single methodSample Design and Size Qualitative•Non-probability•Purposive•Small sampleQuantitative•Probability•Large sampleData Type and PreparationQualitative•Verbal or pictorial•Reduced to verbalcodesQuantitative•Verbal descriptions•Reduced to numericcodesTurnaround Qualitative•Shorter turnaround possible •Insight developmentongoingQuantitative•May be time-consuming •Insight developmentfollows data entryQualitative •Nonquantitative; human •Judgment mixed with fact •Emphasis on themesQuantitative •Computerized analysis •Facts distinguished •Emphasis on countsQualitative Research and the Research ProcessPretasking Activities Use product in homeBring visual stimuliCreate collageKeep diariesFormulating the Qualitative Research QuestionFactors TopicsQualitative SamplingGeneral sampling rule:You should keep conducting interviews until no new insights are gained.The Interview QuestionHierarchyInterviewer Responsibilities•Recommends topics and questions •Controls interview •Plans location and facilities •Proposes criteria for drawing sample •Writes screener •Recruits participants •Develops pretasking activities •Prepares research tools •Supervises transcription •Helps analyze data •Draws insights •Writes reportElements of a RecruitmentScreener•Heading •Screening requirements •Identity information •Introduction •Security questions •Demographic questions •Behavior questions •Lifestyle questions •Attitudinal and knowledge questions •Articulation and creative questions •Offer/ Termination。

Review Cohesive Zone Modeling andIts ApplicationsABSTRACT:This term paper reviews the papers which are about the cohesive process zone model, a general model that deal with the nonlinear zone ahead of crack tip because of plasticity or microcracking. The cohesive zone model could predict the behavior of uncracked structures. Linear elastic fracture mechanics (LEFM) can be used to solve fracture problems provided a crack-like notch or flaw exists in the body and the nonlinear zone ahead of the crack tip is negligible, but for most of cases, for instance, ductile metals or cementitious materials, the size of nonlinear zone is not negligible. Therefore, people use cohesive process zone model to solve fracture problems above.Keywords: Cohesive zone; Cohesive process zone modelINTRODUCTION:Perhaps it is one of the greatest achievements of continuum mechanics in the 20th century that researchers can predict when a crack will grow in many media [22]. Due to the fact that Griffith’s work was proposed for an ideal elastic material, alternative approaches have been formulated more recently, such as the cohesive zone models proposed by Dugdale and Barenblatt. Linear elastic fracture mechanics (LEFM) tells that the stress at the tip of a crack in a brittle material is singular and infinite [10], which is known as physically unrealistic. Dugdale and Barenblatt found that there is a cohesive zone ahead of the crack tip, which limits the magnitude of stress at the crack tip to physically meaningful levels. It is Barenblatt [13] who first describe fracture as a material separation across as surface. It appears by different names, such as cohesive process zone model, cohesive zone model, etc. In recent years, the cohesive zone modeling has become one of the most popular tools to simulate fracture in materials and structures. The cohesive zone model, which is originally applied to concrete and cementitious composites and interface fracture (see, for example, [5]). It is assumed that ahead of the physical crack tip, there is a cohesive zone which consists of upper and lower surfaces held by the cohesive traction. Thecohesive traction is related to the separation displacement between the two surfaces. The relationship of cohesive traction and the separation displacement can be called as “cohesive law”or ”Traction-separation law”, When applied loads to the models, the upper and lower surfaces separate gradually, after the separation of these surfaces at edges of the cohesive zone model reaches a critical value, the separation of the two surfaces leads to the crack growth. Although the cohesive zone model was originally proposed for model I fracture [9] for the purpose of removing the crack tip stress singularity, it also can be applied in model III fracture model [10].It is said that the necessary condition to eliminate stress singularity at the tip is that the cohesive traction must be a nonzero value at initial vanishing separation displacement [11]. Additional fracture energy dissipation mechanism is needed besides the fracture process in cohesive zone when the stress singularity exists at the cohesive zone tip. Common wise consider the fracture energy in the cohesive zone model is the critical energy release rate in LEFM. This is true only the cohesive zone is vanishingly small [12].However, more complicated cohesive zone models which can accurately simulate real material behavior make problem solutions more difficult. There is one shortcoming [23] when using cohesive zone models, and that is one needs to predict the direction that cracks prefer to grow, such as occurs when cracks grow at material interfaces.Cohesive zone models recently could be founded by using the finite element code ABAQUS to predict crack growth. However, to master cohesive zone models, it still needs a period time. LITERATURE SURVEY:Fundamental theory of Cohesive Elements Model in interfaceBroberg [14] depicts the appearance of the process zone in a cross-section normal to the crack edge by decomposing it into cells. The behavior of one single cell is defined by relationships between boundary loads and displacements conditions. If the cells are assumed as cubic and be put along the crack zone, this could be considered as a finite element in computations. Researches constructed cohesive models as that: tractions increase until reach a maximum, and then approach zero when the separation displacement increases. The thickness of the interface in theunloaded state is considered as zero. Tvergaard and Hutchinson (1993) introduced traction-separation relation: let δn and δt be the normal and tangential components of the relative displacement of the respective faces across the interface in the zone [7]:The tractions are supposed to be zero when λ=1.The potential is:The normal and tangential components of the traction are given byIf the tangential component of the traction is zero, the traction-separation law is a purely normal separation. The peak normal traction under purely normal separation is termed the interface strength. The work of separation per unit area of interface is given byThe stress-strain relationship of the film material:Studies in interface by applying cohesive zone model always contain the following parameters [7]:Generally, for model I cohesive zone models it only contains opening mode fracture, the relationship between the cohesive traction and the separation displacement could be expressed as:In this equation, σc is the peak traction,δc is a characteristic separation displacement, f is a dimensionless function which relate to the shape of the cohesive traction-separation displacement curve (like figure 1).Review of mixed mode cohesive zone modelFor mixed model fracture, for instance, model I and model II, both separation displacements and cohesive surface tractions have normal and tangent components. The general mixed mode cohesive zone model could be the form:The character “n” represents “normal”, and “s” represents “shear”. To obtain better functional forms of f n and f s, a cohesive energy potential is often used. Ortiz and Pandolfi [15] introduced an effective separation and effective traction:In the equation “η” is a coefficient which could be changed according to different weights to the model I and model II. Under loading conditions, the effective traction can be derived from a cohesive energy potential by:And the cohesive tractions can be obtained by:Tvergaard and Hutchinson [16] used a different form of the shape function and the traction-separation relations are similar. However, Needleman [17] and Xu and Needleman [18] didn’t use effective quantities and considered that the cohesive potential is a direct function of two separations [17]:And their relationship traction and separation displacement:Wei Zhang, Xiao min Deng [10] provided an effective approach to simulate Model III crack. It is interesting that they found that the von Mises effective stress in cohesive zone is constant. The cohesive zone is a traction region that the surface traction smoothly changes from zero at the crack tip to a certain magnitude at the cohesive zone tip. It is said that the cohesive zone is a mathematical extension of the crack and physical fracture process zone. Traction-separation relation takes the form [10]:。