Fuzzy stochastic damage mechanics (FSDM) based on fuzzy auto-adaptive control theory
马普所间隙高熵合金变形机制 acta
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Fuzzy support vector machines
Fuzzy Support Vector Machines Chun-Fu Lin and Sheng-De WangAbstract—A support vector machine(SVM)learns the decision surface from two distinct classes of the input points.In many appli-cations,each input point may not be fully assigned to one of these two classes.In this paper,we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different constributions to the learning of deci-sion surface.We call the proposed method fuzzy SVMs(FSVMs). Index Terms—Classification,fuzzy membership,quadratic pro-gramming,support vector machines(SVMs).I.I NTRODUCTIONT HE theory of support vector machines(SVMs)is a new classification technique and has drawn much attention on this topic in recent years[1]–[5].The theory of SVM is based on the idea of structural risk minimization(SRM)[3].In many ap-plications,SVM has been shown to provide higher performance than traditional learning machines[1]and has been introduced as powerful tools for solving classification problems.An SVM first maps the input points into a high-dimensional feature space and finds a separating hyperplane that maximizes the margin between two classes in this space.Maximizing the margin is a quadratic programming(QP)problem and can be solved from its dual problem by introducing Lagrangian multi-pliers.Without any knowledge of the mapping,the SVM finds the optimal hyperplane by using the dot product functions in feature space that are called kernels.The solution of the optimal hyperplane can be written as a combination of a few input points that are called support vectors.There are more and more applications using the SVM tech-niques.However,in many applications,some input points may not be exactly assigned to one of these two classes.Some are more important to be fully assinged to one class so that SVM can seperate these points more correctly.Some data points cor-rupted by noises are less meaningful and the machine should better to discard them.SVM lacks this kind of ability.In this paper,we apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM(FSVM) such that different input points can make different constribu-tions to the learning of decision surface.The proposed method enhances the SVM in reducing the effect of outliers and noises in data points.FSVM is suitable for applications in which data points have unmodeled characteristics.The rest of this paper is organized as follows.A brief review of the theory of SVM will be described in Section II.The FSVMManuscript received January25,2001;revised August27,2001.C.-F.Lin is with the Department of Electrical Engineering,National Taiwan University,Taiwan(e-mail:genelin@.tw).S.-D.Wang is with the Department of Electrical Engineering,National Taiwan University,Taiwan(e-mail:sdwang@.tw).Publisher Item Identifier S1045-9227(02)01807-6.will be derived in Section III.Three experiments are presented in Section IV.Some concluding remarks are given in Section V.II.SVMsIn this section we briefly review the basis of the theory of SVM in classification problems[2]–[4].Suppose we are given asetThe optimal hyperplane problem is then regraded as the so-lution to theproblem(6)where can be regarded as aregularization parameter.This is the only free parameter in theSVM formulation.Tuning this parameter can make balance be-tween margin maximization and classification violation.Detaildiscussions can be found in[4],[6].Searching the optimal hyperplane in(6)is a QP problem,which can be solved by constructing a Lagrangian and trans-formed into thedual(7)where is the vector of nonnegative Lagrangemultipliers associated with the constraints(5).The Kuhn–Tucker theorem plays an important role in thetheory of SVM.According to this theorem,thesolution(8)in(8)are those for which the constraints(5)are satisfied with theequality sign.Thepointandis classified correctly and clearlyaway the decision margin.To construct the optimalhyperplane,it followsthat,the computationof problem(7)and(11)is impossible.There is a good propertyof SVM that it is not necessary to knowaboutcalled kernel that can compute the dotproduct of the data points in featurespace(14)and the decision functionis(15)III.FSVMsIn this section,we make a detail description about the ideaand formulations of FSVMs.A.Fuzzy Property of InputSVM is a powerful tool for solving classification problems[1],but there are still some limitataions of this theory.From thetraining set(1)and formulations discussed above,each trainingpoint belongs to either one class or the other.For each class,wecan easily check that all training points of this class are treateduniformly in the theory of SVM.In many real-world applications,the effects of the trainingpoints are different.It is often that some training points are moreimportant than others in the classificaiton problem.We wouldrequire that the meaningful training points must be classifiedcorrectly and would not care about some training points likenoises whether or not they are misclassified.That is,each training point no more exactly belongs to oneof the two classes.It may90%belong to one class and10%be meaningless,and it may20%belong to one class and80%be meaningless.In other words,there is a fuzzymembershipcan be regarded as the attitude ofmeaningless.We extend the concept of SVM with fuzzy mem-bership and make it an FSVM.B.Reformulate SVM Suppose we are given aset,and sufficientsmall denote the corresponding featurespace vector with amapping.Since the fuzzymembership is the attitude of the corre-spondingpoint(17)where(19)(22)and the Kuhn–Tucker conditions are definedas(23)is misclassi-fied.An important difference between SVM and FSVM is thatthe points with the same value ofin SVM controls the tradeoff be-tween the maximization of margin and the amount of misclassi-fications.Alargermakes SVMignore more training points and get wider margin.In FSVM,we canset(25)where,and make the firstpoint.If we want to make fuzzy membershipbe a linear function of the time,we canselect(26)By applying the boundary conditions,we cangetFig.1.The result of SVM learning for data with time property. By applying the boundary conditions,we canget(30)where(31)suchthat1,it may belongs to this class with lower accuracy or reallybelongs to another class.For this purpose,we can select thefuzzy membership as a function of respective class.Suppose we are given a sequence of trainingpointsifFig.2.The result of FSVM learning for data with time property.Fig.3shows the result of the SVM and Fig.4shows the result of FSVM bysettingis indicated as square.In Fig.3,the SVM finds theoptimal hyperplane with errors appearing in each class.In Fig.4,we apply different fuzzy memberships to different classes,theFSVM finds the optimal hyperplane with errors appearing onlyin one class.We can easily check that the FSVM classify theclass of cross with high accuracy and the class of square withlow accuracy,while the SVM does not.e Class Center to Reduce the Effects of OutliersMany research results have shown that the SVM is very sen-sitive to noises and outliners[8],[9].The FSVM can also applyto reduce to effects of outliers.We propose a model by settingthe fuzzy membership as a function of the distance between thepoint and its class center.This setting of the membership couldnot be the best way to solve the problem of outliers.We just pro-pose a way to solve this problem.It may be better to choose a dif-ferent model of fuzzy membership function in different trainingset.Suppose we are given a sequence of trainingpoints1(37)and the radius ofclassif(39)whereis indicated as square.In Fig.5,theSVM finds the optimal hyperplane with the effect of outliers,for example,a square at( 2.2).InFig.6,the distance of the above two outliers to its correspondingmean is equal to the radius.Since the fuzzy membership is afunction of the mean and radius of each class,these two pointsare regarded as less important in FSVM training such that thereis a big difference between the hyperplanes found by SVM andFSVM.Fig.3.The result of SVM learning for data sets with different weighting.Fig.4.The result of FSVM learning for data sets with different weighting.Fig.5.The result of SVM learning for data sets with outliers.Fig.6.The result of FSVM learning for data sets with outliers.V.C ONCLUSIONIn this paper,we proposed the FSVM that imposes a fuzzy membership to each input point such that different input points can make different constributions to the learning of decision sur-face.By setting different types of fuzzy membership,we can easily apply FSVM to solve different kinds of problems.This extends the application horizon of the SVM.There remains some future work to be done.One is to select a proper fuzzy membership function to a problem.The goal is to automatically or adaptively determine a suitable model of fuzzy membership function that can reduce the effect of noises and outliers for a class of problems.R EFERENCES[1] C.Burges,“A tutorial on support vector machines for pattern recogni-tion,”Data Mining and Knowledge Discovery,vol.2,no.2,1998.[2] C.Cortes and V.N.Vapnik,“Support vector networks,”MachineLearning,vol.20,pp.273–297,1995.[3]V.N.Vapnik,The Nature of Statistical Learning Theory.New York:Springer-Verlag,1995.[4],Statistical Learning Theory.New York:Wiley,1998.[5] B.Schölkopf,C.Burges,and A.Smola,Advances in Kernel Methods:Support Vector Learning.Cambridge,MA:MIT Press,1999.[6]M.Pontil and A.Verri,Massachusetts Inst.Technol.,1997,AI Memono.1612.Properties of support vector machines.[7]N.de Freitas,o,P.Clarkson,M.Niranjan,and A.Gee,“Sequen-tial support vector machines,”Proc.IEEE NNSP’99,pp.31–40,1999.[8]I.Guyon,N.Matic´,and V.N.Vapnik,Discovering Information Patternsand Data Cleaning.Cambridge,MA:MIT Press,1996,pp.181–203.[9]X.Zhang,“Using class-center vectors to build support vector machines,”in Proc.IEEE NNSP’99,1999,pp.3–11.。
欧盟残余应力标准EN153 05中文翻译稿
EUROPEAN COMMITTEE FOR STANDARDIZATION COMITE EUROEEN DE NORMALISATION EUROPAISCHES KOMITEE FUR NORMUNG
爱派克测试技术(上海)有限公司组织翻译
2
EUROPEAN STANDARD
EN 15305‐2008
EUROPEAN STANDARD
EN 15305‐2008
August 2008
欧盟X射线衍射残余应力测定标准EN 15305-2008中文翻译稿
Non-destructive Testing - Test Method for Residual Stress analysis by X-ray Diffraction
爱派克测试技术(上海)有限公司组织翻译
1
EUROPEAN STANDARD
EN 15305‐2008
August 2008
ICS 19.100
无损检测–用X射线衍射进行残余应力分析 的测试方法
本欧洲标准于2008年7月4日经欧盟标准委员会(CEN)批准。
欧盟标准委员会成员国有义务按照欧盟标准无变更的给予标准国家地位。关于本 标准的最新目录和题录都可从欧盟标准委员会管理中心和欧盟成员国那里得到。
6.2.3 χ 法....................................................... 22
6.2.4 修改的 χ 方法............................................... 23
6.2.5 6.3 6.4 6.5
其它衍射几何............................................... 23 辐射的选择 ................................................. 23 探测器的选择 ............................................... 26 设备的性能 ................................................. 26
湿热环境下复材修理压缩解析模型及极限载荷的二分法分析
析ꎮ Ｂｅｎｋｈｅｄｄａ 等 [２１] 研究一个近似模型来评估复合
郝建滨等 [１０] 建立胶接修理结构中胶层剪应力分布
的解析模型ꎬ分析了胶层剪应力分布随补片长度、厚
度和胶层厚度的变化ꎮ 李振凯
[１１]
层压板在吸湿、加热过程中的湿热弹性应力ꎬ同时考
虑到温度和湿度变化引起的机械特性的变化ꎮ
建立了复合材料
生的破坏模式( 见图 １) 包括母板压缩破坏、补片压
材料双面贴补修理拉伸解析分析模型ꎬ模型考虑了
缩破坏、补片压缩破坏和胶层剪切破坏三种ꎬ并采用
母板补片的破坏ꎬ也分析了胶层的剪切破坏ꎬ并考虑
首层破坏原则ꎬ修理结构中任何一种破坏模式先发
了搭接区阶梯末截面积的变化细节ꎮ 文献中建立的
生ꎬ对应的载荷即为极限载荷ꎮ 模型引入二分法求
模型大多针对拉伸性能ꎬ压缩性能
２００２４０)
摘要: 解析模型具有计算速度快、直观反映各参数对力学性能的影响等优点ꎮ 建立了湿热环境下复材修理压缩解析模
型ꎮ 模型中考虑了湿热对复合材料力学性能的影响ꎬ以及湿热所产生的湿热应力ꎮ 模型假设修理结构受压后的破坏模式分为
母板压缩破坏、补片压缩破坏和胶层剪切破坏三种ꎬ任何一种破坏模式先发生ꎬ对应的载荷即为极限载荷ꎮ 模型先直接求得湿
ｐｒｉｎｃｉｐｌｅ ｃｏｏｒｄｉｎａｔｅ ｓｙｓｔｅｍ
理后ꎬ该修理结构在湿热环境下的压缩性能的解析
预测方法ꎮ 本文的解析模型基于以下假设:①对胶
层采用了线弹性模型和最大应力准则ꎻ②只考虑了
胶层的剪切变形影响ꎬ忽略母板和补片的剪切变形ꎻ
③认为修理结构在承载时始终处于小变形、线弹性
的状态ꎻ④层合板失效判断采用首层破坏准则和最
料在不同湿热环境下的吸水率、巴氏硬度和弯曲强
D-S证据理论
[13] Tessem, B. Approximations for efficient computation in the theory of evidence. Artificial Intelligence, 1993, 61:315-329. 【注：文献10-12均为证 据理论近似计算方法】
[29] 孙全 等. 一种新的基于证据理论的合成公式. 电子学报, 2000, 28(8): 117-119.
[30] 曾成, 赵保军, 何佩昆. 不完备框架下的证据组合方法. 电子与信息 学报, 2005, 27(7): 1043-1046.
[31] 王永庆. 人工智能原理与方法. 西安交通大学出版社, 1998. pp. 185-197. （第5章第5.5节 “证据理论”）
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证据理论的发展概况（续1）
本章的主要参考文献（续1）
[5] Zadeh, L. A. Review of Shafer’s a mathematical theory of evidence. AI Magazine, 1984, 5:81-83. 【对证据理论进行质疑的经典文献之一】
[6] Shafer, G. Perspectives on the theory and practice of belief functions. International Journal of Approximate Reasoning, 1990, 4: 323-362.
F-X域复数经验模态分解去噪方法（英文）
F-X域复数经验模态分解去噪方法（英文）
马彦彦;李国发;王钧;周辉;张保江
【期刊名称】《应用地球物理：英文版》
【年(卷),期】2015(12)1
【摘要】F-X域经验模态分解去噪方法在处理非稳态地震数据时存在两个局限,一是单纯剔除第一个固有模态分量将导致有效信号缺失及去噪能力偏弱问题,二是分解复信号时对实部和虚部分别分解存在分解数目不一致的风险。
本文对上述两个方面进行了改进,提出了一种新的F-X域投影法复数经验模态分解预测滤波方法,首先采用基于空间投影的复数经验模态分解将F-X域地震数据直接分解为不同的复固有模态分量,然后再对这些分量分别进行F-X域预测滤波。
合成记录及实际资料测试表明,本文的新方法能更好地衰减随机噪声,更有效地保持地震信号。
【总页数】9页(P47-54)
【关键词】复数经验模态分解;复固有模态函数;F-X域预测滤波;随机噪声衰减【作者】马彦彦;李国发;王钧;周辉;张保江
【作者单位】中国石油大学(北京)油气资源与探测国家重点实验室;中国石油大学(北京)CNPC物探重点实验室;中国石化石油勘探开发研究院
【正文语种】中文
【中图分类】P
【相关文献】
1.基于经验模态分解的f-x域面波压制方法 [J], 董烈乾;李振春;杨少春;李凤磊;王娇
2.f-x经验模式分解迭后去噪方法与应用 [J], 刘保童
3.基于FSVM的二维经验模态分解域图像去噪 [J], 吴昌健;陈茹静
4.基于复数据经验模态分解的噪声辅助信号分解方法 [J], 曲建岭;王小飞;高峰;周玉平;张翔宇
5.二维经验模态分解域的新型HMT模型图像去噪 [J], 吴昌健
因版权原因，仅展示原文概要，查看原文内容请购买。
一种仿人智能Fuzzy控制方法
一种仿人智能Fuzzy控制方法
梅胜松;方康玲
【期刊名称】《武汉冶金科技大学学报》
【年(卷),期】1997(020)001
【摘要】首先介绍了Ｆｕｚｚｙ控制的基本原理，然后针对经典模型稳态精度不高的弱点，在Ｆｕｚｚｙ控制器中引入一种仿人智能调节器，提出一种仿人智能Ｆｕｚｚｙ控制方法。
给出了仿人智能Ｆｕｚｚｙ控制方法的具体算法及相应的应用示例。
【总页数】4页(P100-103)
【作者】梅胜松;方康玲
【作者单位】自动化教研室;自动化教研室
【正文语种】中文
【中图分类】TP18
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3.一种适用于连续温度控制的准二维Fuzzy-PID控制方法 [J], 吴江涛;刘志刚;陈圣坤
4.基于仿人智能积分的Fuzzy-PD控制器性能优化研究 [J], 杨智博;孙和平
5.一种仿人智能的智能控制方法 [J], 王东云
因版权原因，仅展示原文概要，查看原文内容请购买。
原子力显微镜摩擦力标定的改进楔形法_陈天星
αW′0
=cos2
μ θ-μ2 sin2 θ
(1) (2)
由(1)(2)式 可以 看 出 Oglet ree 楔 形法(简 称 Ogletree 法)并 没 有考 虑 黏着 力 的影 响 。 实 际上当载荷 较小时 , 黏 着力对 摩擦力 的影响 不 可忽 略 。 摩 擦 力与 载荷 之 间已 不 再满 足 Coulomb 定理[ 9] 。
第 4 期
陈天星等 :原子力显微镜摩擦力标定的改进楔形法
71
力曲线线性部分斜率的不同计 算摩擦因数的方 法[ 2-3] 。 以上两种方法避免了直接对针尖横向灵 敏度的标定 , 操作简便 , 但是需要对悬 臂梁几何 尺寸进行精确测量 。杠 杆法是 将一根 粘有硅 球 的玻璃纤维 粘在一 根没有 针尖的 悬臂梁 上 , 通 过测量力曲 线 , 可 以计 算出在 垂直于 悬臂梁 轴 向平面内的 扭矩 , 从而 计算出 横向灵 敏度的 方 法[ 4] , 这种方法操 作较为困难 、费 时 。 还 有一些 方法 , 需要利用一些辅助设备 , 如 横向电纳米天 平[ 5] 、悬浮热解石墨片[ 6] 等 , 这些方法实 现起来 较为困难 。
2 改进的楔形标定法模型的建立
在 Oglet ree 法的基础上进行了改进 , 考虑了 粘着力 A 的影响 , 进行建模分析 。 改进后的楔形 法的原理如图 3 所示 。
图 1 楔形法摩擦回路曲线 Fig .1 Schematic f rictio n loo p o n flat and slo p
图 3 改进的楔形标定法 的原理图 Fig .3 Schematic diag ram of impr oved wedge me thod
当针尖沿斜坡向上运动时 , 针尖的力平衡方 程和力矩平衡方程分别为 :
冶金专业英语词汇(F)
冶金专业英语词汇(F)冶金专业英语词汇(F) face centered cubic lattice 面心立方晶格face centered cubic structure 面心立方结构face centered lattice 面心晶格face hardening 外表硬化face length 辊身长度face of weld 焊接面face shield 手持焊接面罩facing sand 面砂factor of safety 平安系数factory test 工厂试验fahlerz 铜矿failure 破坏fall of temperature 温度下降fall rate 沉降速度falling tup 碎铁落锤false equilibrium 假平衡false part 对型famatinite 脆硫锑铜矿family of crystal planes 晶面族fan 送风机fan cooling 风扇冷却fancy alloy 装饰合金fanning 慢风操作fansteel process 范史提尔生产法faraday's law 法拉第定律fastening by pinning 销钉紧固fat coal 肥煤fat sand 肥砂fatigue 疲劳fatigue bending machine 弯曲疲劳试验机fatigue breakdown 疲劳断裂fatigue corrosion 疲劳腐蚀fatigue crack 疲劳裂痕fatigue crescents 贝壳状纹理fatigue curve 疲劳曲线fatigue failure 疲劳断裂fatigue fracture 疲劳断口fatigue limit 疲劳极限fatigue ratio 疲劳比fatigue strength 疲劳强度fatigue stress 疲劳应力fatigue test 疲劳试验fatigue tester 疲劳试验机fatigue testing machine 疲劳试验机faulty casting 有缺陷铸件fayalite 铁橄榄石faying surface 搭接面feed 给料feed angle 供应角feed gearbox 给料机械feed hopper 加料漏斗feed reel 开卷机feed roller 送料辊feed rolls 送料辊feed shoe 给料刮板feed table 给料辊道feeder 冒口feeder head 冒口feeding 给料feeding pound 冒口发热剂feeding device 装料装置feldspar 长石felt 毛毡ferberite 钨铁矿fergusonite 褐钇钽矿fermium 镄fern leaf crystal 锯齿状晶体ferric chloride 氯化铁ferric pound 正铁化合物ferric hydroxide 氢氧化铁ferric oxide 三氧化二铁ferric salt 正铁盐ferric sulfate 硫酸铁ferric sulfide 硫化亚铁ferrite 铁素体;亚铁酸盐ferrite former 铁素体形成元素ferrite work 网状铁素体ferritic cast iron 铁素体铸铁ferritic chrome steel 铁素体类型的铬钢ferritic electrode 铁素体钢焊条ferritic phase 铁素体相ferritic stainless steel 铁素体不锈钢ferritic steel 铁素体钢ferritic structure 铁素体组织ferritic transformation 铁素体转变ferro cerium 铈铁ferro vanadium 钒铁ferroalloy 铁合金ferroalloy furnace 铁合金炉ferroaluminum 铝铁ferroboron 硼铁ferrochrome 铬铁ferrochrome silicon 铬硅铁ferrocobalt 钴铁ferrocoke 铁焦ferrocolumbite 铁铌矿ferrocolumbium 铌铁ferromagic alloy 铁磁性合金ferromagic material 强磁性质ferromagic substance 强磁性质ferromagism 铁磁性ferromanganese 锰铁ferromolybdenum 钼铁ferronickel 镍铁ferroniobium 铌铁ferrophosphorus 磷铁ferrosilicium 硅铁ferrosilicoaluminum 硅铝铁ferrosilicon 硅铁ferrostatic pressure 铁水静压力ferrosteel roll 半钢轧辊ferrotitanium 钛铁ferrotungsten 钨铁ferrouranium 铀铁ferrous alloy 铁基合金ferrous chloride 氯化亚铁ferrous coke 铁焦ferrous pound 亚铁化合物ferrous hydroxide 氢氧化亚铁ferrous manganese ore 锰铁矿ferrous metal 黑色金属ferrous metallurgy 黑色冶金ferrous oxide 氧化亚铁ferrous salt 亚铁盐ferrous silicate 硅酸亚铁ferrous sulfate 硫酸亚铁ferrous sulfide 硫化亚铁ferrovanadium 钒铁ferroxcube 立方结构的铁氧体ferroxyl indicator 铁锈指示剂ferrozirconium 锆铁ferruginous deposits 铁矿床fery pyrometer 费里高温计fettler 清理工fettling 炉底修补fettling room 修整工段fiber 纤维fiber posite material 纤维复合材料fiber diagram 纤维绕射图fiber direction 纤维方向fiber fracture 纤维状断口fiber metallurgy 纤维冶金fiber reinforced posite 纤维强化复合材料fiber reinforced material 纤维增强材料fiber reinforcement 纤维增强fiber stress 纤维应力fiber structure 纤维状组织fiber texture 纤维织构fibering 纤维性fibrous fracture 纤维状断口fibrous material 纤维材料fibrous powder 纤维状粉fibrous structure 纤维状组织field 场field density 磁通量密度field intensity 场强度field ion microscope 电场离子显微镜field ion microscopy 电场离子显微镜检查法field test 现场试验field welding 工地焊接figure of merit 质量指数file hardness testing 锉喜度试验filiform corrosion 丝状腐蚀fill factor 充填系数filled die 满料压模filler material 填充材料filler metal 填充金属filler rod 焊条芯filler sand 填充砂fillet 角焊缝fillet joint 角焊缝接头fillet welding 角焊filling 孔型填充filling amount 装粉量filling condition 装粉状态filling volume 装粉体积filling weight 装粉重量film 膜film lubrication bearing 铃动力轴承filter 过滤机filter bag 过滤袋filter bed 过滤层filter cake 滤渣filter cell 过滤槽filter cloth 滤布filter core 撇渣泥芯filter crucible 过滤坩埚filter frame 滤框filter gate 过滤网浇口filter plate 滤板filter press 压滤机filter pump 滤泵filterability 过滤性filtering apparatus 过滤装置filtering layer 过滤层filtering surface 过滤面filtrate 滤液filtration 过滤filtration residue 滤渣fin 毛边fin cutting 去除毛刺final annealing 最终退火final liquor 废液final pass 精轧道次final product 最终产物final sintering 最终烧结final slag 最终炉渣final yield 最后收获率fine adjustment 精调fine crusher 细碎机fine crushing 细碎fine drawing 细丝拉拔fine gage sheet scrap 薄钢板废料fine grain 细晶粒fine grain annealing 细颗粒退火fine grained fracture 细晶断口fine grained steel 细晶粒钢fine grained structural steel 细晶粒结构钢fine grained structure 细粒组织fine granular fracture 细晶断口fine grinding 细碎fine mesh sieve 细筛fine metal 纯金属fine ore 粉矿fine powder 细粉fine silver 纯银fine solder 优级软焊料fine structure 精细结构fine wire 细金属丝fine wire drawing machine 细拉丝机fineness 细度finery 熟铁精炼炉fines 细粉finest wire 极细钢丝fingernailing 半月形焊接变形fining process 精炼法finish rolling 精轧finished sheet 精整薄板finisher 成品轧机finishing 最后加工;精炼finishing die 精拉拔模finishing drawing 精拉拔finishing equipment 精整设备finishing groove 精轧孔型finishing line 精整椎线finishing mill 精轧机finishing mill group 精轧机组finishing pass 精轧道次finishing roll 精轧辊finishing stand 完成机座finishing temperature 终温度finishing train 精轧机组finishings 最终合金添加剂finite element method 有限元素法finned tube 加筋管finning 筋条加强fire bar 炉篚fire brick 耐火砖fire bridge 火桥fire cement 耐火水泥fire cracker welding 躺焊fire damp 爆炸性矿井瓦斯fire end 燃烧端fire gilding 火法镀金fire grate 炉篦fire hole 火口fire refined copper 火法精炼铜fire refining 火法精炼fire resistance 耐火性fire resisting casting 耐热铸件fire waste 熔炼损失fireclay 耐火粘土fireclay brick 耐火粘土砖fireclay refractory 粘土质耐火材料fired magnesite 烧成菱镁矿fireproof casting 耐热铸件firing 点火;烧成firing shrinkage 烧成收缩first matte 初次锍first order reaction 一级反响firth hardometer 富氏硬度计fish eye 鱼眼状裂纹fish scale 铁鳞fish scale fracture 鱼鳞状断口fishhooks v 形外表缺陷fishplate 鱼尾板fishtail 轧材 v 形尾端fission 分裂fissionable metal 裂变金属fissure 裂纹fissuring 开裂fit 配合fitness to brazing 钎焊性fittings 配件five stand mill 五机架轧机fixed bed 固定床fixed electron 束缚电子fixed furnace 固定炉fixed nitrogen 化合氮fixed open hearth furnace 固定式平炉fixed oxygen 化合氧fixed plug 固定芯棒fixed plug drawing 固定芯棒拔制flake 薄片;白点flake graphite 片状石墨flake powder 片状粉flake susceptibility 白点感受性flaking 剥落flame 火焰flame analysis 火焰分析flame annealing 火焰退火flame brazing 火焰钎焊flame chipping 火焰清理flame cleaning 火焰清理flame core 焰心flame cutting machine 火焰切割机flame furnace 反射炉flame gunning 火焰喷补flame hardening 火焰淬火flame heating 火焰加热flame plating 火焰喷涂flame scarfing 火焰清理flame shaping 火焰异形加工flame softening 火焰软化flame spraying 火焰喷射flame temperature 火焰温度flame test 焰色试验flange 凸缘flange beam 工字钢flange forming 凸缘成形法flange rail 槽形轨flange test 弯边试验flanging 折边flanging test 弯边试验flash 焊瘤flash annealing 快速退火flash butt welding machine 闪光电焊机flash plating 闪熔镀覆flash removal 去毛头flash roaster 悬浮焙烧炉flash roasting 悬浮焙烧flash roasting furnace 悬浮焙烧炉flash smelting furnace 闪速熔炼炉flash welder 闪光电焊机flash welding 闪光对焊flashback 回火flashing loss 闪熔收缩损失flask 型箱;烧瓶flask bar 箱带flask drag 下砂箱flask molding 砂箱造型flask pin 砂箱导销flat bars 扁钢flat billet 扁坯flat bottom furnace 平底炉flat bulb steel 球扁钢flat pass 框形孔型flat position 平焊位置flat rammer 平捣锤flat roll 平面轧辊flat rolled products 扁材flat rolling 扁平孔型轧制flat steel 扁钢flat stock 扁材flat tip 平头电极flat welding 平焊flat wire 扁钢丝flatness 平直度flats 扁钢flattener 矫直机flattening 矫平flattening test 压扁试验flatting mill 轧扁机flaw 缺陷flaw detection 探伤flaw detector 探伤器flaw echo 缺陷的回波信号flection 弯曲flexibility 挠性flexible coupling 弹性联轴节flexible dummy bar 可挠引锭杆flexural rigidity 抗挠刚度flexural strain 挠曲应变flexural strength 挠曲强度flexural stress 挠曲应力flexure 挠曲floating die 浮沉模floating hot top 浮游式保温帽floating mandrel 游动芯棒floating plug 浮动塞棒floating plug drawing 游动顶头拔制floating zone melting 悬浮区域熔炼floulant 凝聚剂floulation 凝聚floulation agent 凝聚剂flood basin 浇口杯floor 平台floor mold 地坑铸型floss hole 烟道清灰口flotation 浮选flotation agent 浮选剂flotation cell 浮选槽flotation concentrate 浮选精矿flotation machine 浮选机flotation process 浮选法flotation pulp 浮选矿桨flotation tailings 浮选尾矿flow chart 撂图flow lines 吕德斯线flow structure 塑变组织flow welding 浇焊flowability 怜性fluctuation 波动flue 烟道flue dust 炉顶灰flue gas 烟气flue gas path 烟道fluid bed 怜床fluid bed smelting 怜床溶炼fluid extrusion 静液力挤压fluid film lubrication 液模润滑fluid friction 铃摩擦fluidity 怜性fluidity spiral 旋涡怜试样fluidity test piece 旋涡怜试样fluidization 连化fluidized bed patenting 怜体淬火fluidized bed process 怜床法fluidized bed reduction 连化床复原fluidized bed roaster 连化床焙烧炉fluidized bed roasting 怜床焙烧fluorescent flaw detection 荧光探伤fluorescent perant inspection 荧光渗透检验fluorescent x ray spectroscopy x 射线荧光光谱分析fluorhydric acid 氟酸fluoride 氟化物fluorination 氟化fluorine 氟fluorite 萤石fluorspar 萤石fluosolid roaster 连化床焙烧炉fluosolid roasting 怜床焙烧flush off 排渣flush weld 辖补强焊缝flushing 出渣fluted sheet 波纹钢板fluted tube 沟槽管fluting 开槽flux 熔剂flux backing 焊药垫flux cored electrode 含熔剂芯焊条flux cored wire 含熔剂芯焊条flux cutting 氧熔剂切割flux density 磁通量密度flux oxygen cutting 氧熔剂切割fluxed briquette 自熔性团块fluxed charge 助熔炉料fluxed pellets 自熔性球团fluxible anode 助熔性阳极fluxing agent 熔剂fluxmeter 磁通计fly ash 飘尘flying saw 飞锯flying shears 飞剪flyspun electrode 绕丝焊条flywheel 飞轮foam 泡沫foam flotation 泡沫浮选foamed slag 泡沫渣foamer 起泡剂foaming 起泡foaming agent 起泡剂foamy slag 泡沫渣focused collision 聚焦碰撞focuson 聚焦碰撞fog quenching 喷雾淬火foil 箔foil metal 金属箔foil rolling mill 箔材轧机fold 压折follower plate 挤压垫forced aerated cooling bed 强制通风冷却床forced circulation 强制循环forced convection 强制对流forced cooling 强制冷却forced draught 强制通风fore pump 预真空泵forehand welding 左向焊forehearth 前炉foreign atom 杂质原子foreign nucleus 外来核foreplate 导卫板forest of dislocations 位错林forge 锻造车间forge furnace 锻造炉forge hammer 锻锤forge hearth 锻工炉forge iron 搅炼生铁forge pig 搅炼生铁forge rolling machine 轧锻机forge scale 锻造氧化皮forge welding 锻焊forgeability 可锻性forged roll 锻造辊forged steel 锻钢forging 锻造;锻件forging brass 可锻黄铜forging die 锻模forging furnace 锻造炉forging hammer 锻锤forging ingot 锻造用钢锭forging machine 锻造机forging manipulator 锻造操纵机forging press 锻压机forging property 可锻性forging ratio 锻造比forging rolls 轧锻机forging steel 锻造用钢forging stock 锻坯forging tool 锻造用工具fork truck 叉式自动装卸车form drawing 成形拉伸form factor 形状系数form of groove 孔型外形formability 可成形性formed coke 型焦formed section 冷弯型钢former pass 预轧孔型forming 成形forming mill 成形机forming roll 成形辊forming stand 成形机座forsterite 镁橄榄石forsterite refractory 镁橄榄石耐火材料forvacuum 预抽真空forward extrusion 正挤压forward pull 前张力forward slip 前滑forward slip zone 前滑区forward welding 左向焊fosse 铸坑fossil fuel 化石燃料foucault currents 涡流foul electrolyte 废电解液founding 铸造;熔解foundry alloy 中间合金foundry blacking 铸模涂料foundry coke 铸造焦炭foundry iron 铸造用生铁foundry ladle 浇桶foundry pattern 铸造模型foundry pit 铸坑foundry sand 型砂foundry scrap 铸造废钢foundry stove 铸型枯燥炉four ponent alloy 四元合金four ponent system 四元系four high cold mill 四辊式冷轧机four high finishing stand 四辊精轧机座four high hot mill 四辊式热轧机four high mill 四辊式轧机four high stand 四辊式机座four part alloy 四元合金four plate mold 组合式结晶器four roll pass 四辊孔型four stand mill 四机架轧机fraction 局部fractional column 分馏塔fractional condensation 分级凝缩fractional crystallization 分步结晶fractional dissolution 分步熔解fractional distillation 分级蒸馏fractional extraction 分步萃取fractional precipitation 分级沉淀fractional sublimation 分级升华fractionating column 分馏塔fractionation 分级fractography 断口金相学fracture 断口fracture criterion 断裂指标fracture load 破坏载荷fracture mechanics 断裂力学fracture mechanism 断裂机理fracture nucleation 断裂成核fracture speed 裂纹扩展速度fracture stress 破裂应力fracture test 断口试验fracture toughness 破裂韧性fragility 脆性fragment 碎片fragmentation of grains 细晶化fragmented powder 碎片状粉fragmentized scrap 碎裂废金属frame filter press 框式压滤机francium 钫frank dislocation 弗兰克位错frank read source 弗兰克瑞德源fraze 毛边freckle type segregation 斑点状偏析free carbon 游离碳free cementite 自由渗碳体free convection 自然对流free cutting alloy 易切舷金free cutting brass 易掀铜free cutting property 易切显能free cutting steel 易切钢free cyanide 游离氰化物free energy 自由能free energy of mixing 混合自由能free enthalpy 自由焓free ferrite 自由铁素体free forging 自由锻造free gold 自然金free moisture 游离水分free settling 自由沉降free spread 自由宽展freeze drying 冷冻枯燥freezing 冻结freieslebenite 柱硫锑铅银矿frenkel defect 夫伦克耳缺陷frenkel vacancy 夫伦克耳空位frequency 频率frequency curve 频率曲线frequency polygon 频度多边形fretting corrosion 摩擦腐蚀friction 摩擦friction angle 摩擦角friction coefficient 摩擦系数friction drive 摩擦传动friction factor 摩擦系数friction heat 摩擦热friction losses 摩损friction press 摩擦压机friction resistance 摩擦阻力friction saw 摩擦锯friction sawing 摩擦锯切断friction surface 摩擦面friction transmission 摩擦传动friction welding 摩擦焊接frictional force 摩擦力frictional wear 摩擦磨损frictional work 摩擦功fritted hearth 烧结炉床fritting 烧结front mill table 输入辊道front pull 前张力front side 进料侧front wall 前墙frosting 磨砂froth 泡沫froth flotation 泡沫浮选frother 起泡剂frothing 起泡frothing agent 起泡剂fuel 燃料fuel burner 燃料燃烧器fuel cell 燃料电池fuel gas 燃气fuel oil 燃料油fuel oil injection 燃油喷吹fuel rate 燃料比fuel rod 燃料棒fuel utilization factor 燃料利用系数fugacity 逸散能fugitiveness 逸散能full aging 完全时效full fusion thermit welding 熔化铝热焊full hardening 穿透硬化full length bore 穿孔full pattern 整体铸型full peration 全熔透fuller's earth 漂白土fullering 压槽锤击fulminating gold 雷酸金fulminating mercury 雷汞fulminating platinum 雷酸铂fulminating silver 雷酸银fume 烟fuming furnace 烟化炉fuming process 烟化法function 函数funnel 漏斗;浇口杯furnace 炉furnace aisle 炉子跨furnace atmosphere 炉气氛furnace bay 炉子跨furnace black 炉黑furnace body 炉体furnace bottom 炉底furnace brazing 炉内钎焊furnace burdening 配炉料furnace campaign 炉子寿命furnace capacity 炉子容量furnace charge 炉料furnace coiler 炉用卷取机furnace coke 高炉焦炭furnace cooling 炉冷furnace end 炉头furnace gas 炉气furnace grate 炉篦furnace life 炉龄furnace lines 炉子内型furnace lining 炉衬furnace platform 炉台furnace roof 炉顶拱furnace run 冶炼进程furnace scale 炉内氧化皮furnace shaft 炉身furnace stack 炉身furnace top 炉顶furnace top platform 炉顶平台furnace transformer 电炉变压器fused cast basic brick 熔铸碱性砖fused electrolyte 熔融电解质fused flux 熔融态熔剂fused refractory 熔融耐火材料fused salt 熔融盐fused salt electrolysis 熔盐电解fusibility 可熔性fusible alloy 易熔合金fusible cone 测温锥fusing agent 熔剂fusion 熔化fusion cast brick 熔铸碱性砖fusion curve 熔化曲线fusion cutting 熔切fusion heat 熔化热fusion line 熔合线fusion peration 焊透;焊透深度fusion point 熔点fusion temperature 熔化温度fusion thermit welding 铝热熔焊fusion welding 熔焊fusion zone 熔化带。
Fluid-Structure Interaction
Fluid-Structure Interaction Fluid-structure interaction (FSI) is a complex and challenging problem in engineering and physics, involving the interaction between a fluid and a solid structure. This interaction occurs in many real-world scenarios, such as the flow of air over an aircraft wing, the behavior of blood flow in arteries, and the movement of water around a marine structure. Understanding and accurately modeling FSI is crucial for designing and optimizing engineering systems, as well as for predicting and preventing potential failures. One of the key challenges in FSI is the need to accurately capture the two-way coupling between the fluid and the structure. This requires sophisticated numerical methods and computational tools that can solve the governing equations for both the fluid and the solid, while also accounting for their mutual interactions. Traditional approaches to FSI modeling often involve simplifications and assumptions that can limit the accuracy and applicability of the results. As a result, there is a growing demand for advanced FSI simulation techniques that can provide more realistic and reliable predictions. From a fluid dynamics perspective, FSI is important because it can significantly affect the behavior of the fluid flow. The presence of a solid structure can alter the flow patterns, create turbulence, and induce pressure fluctuations, which in turn can have a feedback effect on the structure itself. This mutual influence between the fluid and the structure can lead to complex and non-linear phenomena, such as vortex shedding, flow-induced vibrations, and fluid-elastic instabilities. Understanding these effects is essential for optimizing the performance and safety of engineering systems, such as aircraft, bridges, and offshore platforms. On the other hand, from a structural mechanics perspective, FSI is important because it can introduce additional loads and stresses on the solid structure. The forces exerted by the fluid, such as drag, lift, and added mass, can cause deformation, fatigue, and ultimately structural failure. Moreover, the dynamic response of the structure to the fluid-induced forces can lead to resonance, fatigue, and other forms of structural damage. Therefore, accurately predicting the FSI effects is crucial for ensuring the structural integrity and longevity of engineering systems. In the field of biomedical engineering, FSI plays a critical role in understanding the behavior of biological fluids, such asblood, within the human body. For example, the interaction between blood flow and the arterial walls is a key factor in the development of cardiovascular diseases, such as atherosclerosis and aneurysms. By accurately modeling FSI in blood vessels, researchers and clinicians can gain insights into the underlying mechanisms ofthese diseases, as well as develop more effective diagnostic and treatment methods. In conclusion, fluid-structure interaction is a multifaceted problem that requiresa holistic understanding of fluid dynamics, structural mechanics, and their mutual interactions. Addressing the challenges of FSI requires the development ofadvanced numerical methods, high-performance computing tools, and experimental techniques that can capture the complex and non-linear behavior of fluid-structure systems. By advancing our understanding of FSI, we can not only improve the design and performance of engineering systems, but also deepen our insights into the behavior of biological fluids in the human body.。