盘式永磁调速器的建模与磁场分析【说明书论文开题报告外文翻译】
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毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的：通过毕业设计，使学生受到电气工程师所必备的综合训练，在不同程度上提高各种设计及应用能力，具体包括以下几方面： 1. 调查研究、中外文献检索与阅读的能力。 2. 综合运用专业理论、知识分析解决实际问题的能力。 3. 定性与定量相结合的独立研究与论证的能力。 4. 仿真实验的测试、采集与分析处理的能力。 5. 设计、计算与绘图的能力，包括使用计算机的能力。 6. 逻辑思维与形象思维相结合的文字及口头表达的能力。 2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）：1.本课题要求对盘式永磁调速器的研究现状进行调研，掌握永磁调速器的基本结构特点与工作原理，掌握 ANSYS 仿真分析软件前处理和后处理模块的功能，结合分析对象进行几何建模，网格划分、载荷和边界条件加载、求解选项设置以及结果后处理等仿真分析流程。选取合适单元对盘式永磁调速器进行二维和三维磁场的仿真分析，并分析盘式永磁调速器在不同气隙下的磁场分布和转矩输出，为永磁调速器的磁场研究提供理论依据。 2.设计装置有限元分析过程的程序，包括详细的二维模型和三维模型的搭建，网格划分情况，边界条件等详细步骤； 3.最终完成一篇符合金陵科技学院毕业论文规范的系统文档，包括各类技术资料，程序等； 4.文中要有仿真结果的展示，并对仿真数据进行分析； 5.能够完成各项任务，参加最后的毕业设计答辩。 毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括图表、实物等硬件要求： 1.按期完成一篇符合金陵科技学院论文规范的毕业设计说明书（毕业论文） ； 2. 有结构完整，合理可靠的技术方案； 3. 能详细说明装置的建模过程； 4.有相应仿真分析数据和结果说明。 5.要求在有限元仿真软件 ANSYS 上建模成功，并在答辩时进行展示。 4主要参考文献： 1王玉良，周福昌.永磁传动器设计制造中的几个问题J.磁材料及器件，2000 2谢丹，梅顺齐.基于 ANSYS 软件的磁力驱动机构的磁场分析J.轻工机械，2009 3丁国平.磁力轴承电磁场的相关理论和实验研究D.武汉理工大学，2008 4王新伟，刘文胜，王一.ANSYS 软件在无轴承异步电机磁场分析中的应用J.电机技术，2008 5郭荣文.磁场的有限元模拟与延拓研究D.中南大学，2007 6高秀芬.永磁联轴器工作特性研究D.吉林大学，2004 7赵家文.磁力传动联轴器及其应用J.机械设计与制造工程，2002 8赵韩，王勇，田杰.磁力机械研究综述J.机械工程学报，2003 9苏彦宏，张泽慧.磁传动技术的发展和应用J.甘肃科技，2004 10顾庆昌.永磁-电磁离合器磁场分析及特性研究D.合肥大学，2008 11苏洪伟.永磁涡流联轴器性能分析D.长春：吉林大学，2013. 12刘宏宇.永磁调速系统与节能J.上海电力，2008，(3)：257-260. 13段晓伟，王向东.大功率风机水泵调速节能方法对比分析J.节能，2012，31(5)：28-31. 14张泽东.永磁磁力耦合器设计与关键技术研究D.沈阳：沈阳工业大学，2012. 毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：2015.11.04-2015.11.28 在毕业设计管理系统里选题 2015.11.29-2015.12.16 与指导教师共同确定毕业设计课题 2015.12.17-2016.01.10 查阅指导教师下发的任务书，准备开题报告 2016.02.25-2016.03.09 提交开题报告、外文参考资料及译文、论文大纲 2016.03.09-2016.04.28 进行毕业设计（论文） ，填写中期检查表，提交论文草稿等 2016.04.29-2016.05.09 按照要求完成论文或设计说明书等材料, 提交论文定稿 2016.05.09-2016.05.13 教师评阅学生毕业设计；学生准备毕业设计答辩 2016.05.14-2016.05.21 参加毕业设计答辩，整理各项毕业设计材料并归档 所在专业审查意见：通过 负责人： 2016 年 1 月 12 日 毕 业 设 计（论文） 开 题 报 告 1结合毕业设计（论文）课题情况，根据所查阅的文献资料，每人撰写不少于1000 字左右的文献综述： 在当今能源短缺的世界，我们人类越来越关注节能、环保、高效率的生活方式及生产技术，都是因为现在科学技术的快速发展和现在文明的进步与提升。作为一个新生事物，永磁调速器从发明时间到目前为止不过 15 年时间。关于永磁调速器的相关技术也是新时代刚兴起的一种新兴技术。一、永磁调速器永磁调速器又叫永磁磁力耦合调速驱动器，它通过调节铜导体和永磁体之间的气隙实现由电动机到负载的转矩传输的一种调速装置，可实现电动机和负载间无机械链接的传动方式。在旋转机械中，设备的驱动侧（电动机）和被驱动侧（负载）完全没有机械联接，转矩的产生是由于一侧的稀土强永磁体与另一侧的感应磁场的相互作用，通过调节永磁体和铜导体之间的气隙可以精确控制传递的转矩，从而实现负载速度调节。由此可见，永磁调速器主要是利用永磁驱动技术来实现驱动与调速目的，其能实现无极平滑调速和高效节能，并且结构简单可靠，维护成本低，无需再供电源，能适应各种恶劣的环境，并且对电网无污染，且工作时可以实现带缓冲的软启动，具有过载保护功能等优点。尤其对于旋转机械设备，如何将电机的输出动率充分传递给负载，使电机与负载之间达到最优的能源匹配，并且在对负载调速场合能充分发挥机械设备的优势，使节能环保技术在旋转机械中得到应用，是很多机械设备需要解决的问题。当然，在这个过程中，很多科研人员做出了很大的努力，相应的产品也无不推动着社会及工业技术的发展。调速被国际上公认为电机最佳的节能方，在此最大的突破就是液力耦合器和变频器的产生及应用，其大大提高了生产率及能源的优化利用，使节能环保这一理念在工程机械的应用当中得到了充分体现。永磁调速器是一种利用简单地机械结构（高强度导体转子与永磁转子相互作用） ，实现电机与负载之间通过气隙进行扭矩传递。根据现有永磁调速器的结构大体可分为两种，一种为圆盘式，一种为筒式。永磁调速器主要由导体转子、永磁转子和控制器三部分组成，导体转子固定在电机输出轴上，永磁转子固定在负载转轴输入端，导体转子和永磁转子之间有间隙（称为气隙） 。这样电动机和负载由原来的硬件（机械）联接转变为软（磁）联接，通过调节永磁体和导体之间的气隙就可以实现负载轴上的输出转矩变化，从而实现负载转速变化。从调速器的结构可以看出，机构中的磁场主要由永磁体激发，若要进行磁场分析计算，首先要建立永磁体的数学模型，然后通过磁场数值的计算方法得出永磁空间磁场的解析表达式，最后通过确定具体参数计算出永磁体外部空气气隙处的磁场强度，为盘式永磁调速器磁场分析计算出一定的理论依据及参考。二、有限元分析近年来，随着有限元法的极大发展及广泛应用，依托计算机技术的迅猛发展，基于有限元法分析法的计算机应用软件也应运而生，最广泛的就是美国 ANSYS 公司研制的大型通用有限元分析（FEA）软件 ANSYS，它能够进行包括结构、热、声、流体以及电磁场等学科的研究，在核工业、石油化工、航空航天、机械制造、土木工程、生物医学等方面有着广泛的应用。ANSYS 的功能强大，可以灵活地解决电磁方面的问题。 ANSYS 电磁场分析是以麦克斯韦方程组作为出发点，利用有限元法先将要处理的对象划分成若干个微小单元，当然这些微小单元中也可包含若干节点，然后根据电磁场偏微分方程按照有限元法进行数值求解，解得磁势和电势的场分布值，在经过转化可得电磁场的各种物理量，如磁感应强度、磁场强度及磁力线分布等。毕 业 设 计（论文） 开 题 报 告 2本课题要研究或解决的问题和拟采用的研究手段（途径）： 本课题要研究或解决的问题是：1.了解盘式永磁调速器的结构、技术特点以及它的工作原理；2.掌握运用 ANSYS 仿真软件，在此进行建模仿真与建模并对不同气隙下的以及磁场情况进行分析；3.在完成上述两个步骤后，整理出具体方案思路及论文大纲。研究手段（途径）：1.去图书馆查阅相关资料，经过汇总，作为参考资料；2.充分利用网络资源，进行相关信息的搜索；3.和相关课题的同学进行讨论研究；4.理论联系实际，在 ANSYS 仿真软件中进行模拟仿真。毕 业 设 计（论文） 开 题 报 告 指导教师意见：1对“文献综述”的评语：综述内容较为丰富，参考文献合理，概括了永磁调速器及其分析方法等研究内容的相关背景、基础知识、历史发展等，同时还对本课题所研究的任务进行了一定的阐述，对本课题的研究有一定的指导意义。2对本课题的深度、广度及工作量的意见和对设计（论文）结果的预测：本课题研究的任务是盘式永磁调速器的磁场仿真，深度中等，涉及到的知识面广，例如盘式永磁调速器的磁场和有限元仿真等技术，学生可以通过实例调研，查阅专业资料，进行仿真建模分析，来实现最终的设计任务和结果，并对自己的专业应用能力是一个非常大的提高。3.是否同意开题： 同意 不同意指导教师： 2016 年 03 月 03 日所在专业审查意见：同意 负责人： 2016 年 03 月 08 日1译文题目： Disc permanent magnet based on ANSYS modeling and magnetic field analysis of speed governor学生姓名： 陆海燕 学 号： 1205202030 EDDY CURRENT COUPLER OPTIMIZATIONA. Canova, F. Ereschi, M. Repetto, B. VusiniPolitecnico di Torino, corso Duca degli Abruzzi 24, 10129 Torino - ItalyCorresponding author: Aldo Canova, e-mail . ,aldoeddy currents are forced due relative speed b etween this region and permanent magnets;Region 3: airgap;Region 4: stator permanent magnets;Region 5: stator back ironThe field equations are expressed in terms of magnetic vector potential (A) in a cylindrical coordinate system.The field equations had to be written in five regions as described below.Region 1, 3 and 5. In airgap and in the back iron the conductivity is zero, considering a laminated core, and thefield equations are: Region 2. This is the only region made of conductive material, the magnetic equation is:3where 2 is the relative permeability of RCS (in our case, having copper material, 2= l), w is the angular speed,a2 is the conductivity of RCS and 0 is the angular coordinate. Region 4. Air and permanent magnet constitute region 4. It is possible under certain hypotheses to write a uniquefield equation. In fact, by assuming the same relative permeability both in magnet and in air the magnetic fluxdensity components can be written in the form:where BO, He, B, and H, a,re the angular and radial components of magnetic flux density and field intensity respectively. RESB is the residual induction of the magnets which has a rectangular waveform along the 0 coordinate inside the magnet region and it is equal to zero outside the magnets. This rectangular waveform can he expressed as Fourier distribution:where i is the imaginary unit, n is the order of harmonic arid T is the pole pitch:and n,Lou is the number of pair poles. By using the VSM, equation (2) can be split in:By solving equation (6) for the n-order harmonic we obtain:where )(2nCarid )(2nDdepend on the boundary conditions. From the field solution it is possible to calcu1at.e the eddy currents induced in the RCS:and the eddy current losses:4Considering the power contribution to the n-order harmonic the torque relation for the solution is:where H is the axial lenght of the coupler. The total torque can be computed as sum of the n-order harmoniccomponents. This calculation can be performed (due the particular form of J , in which only odd harmonics arepresent, as stated by equation (4). By evaluating the sum of the square of J , the mutual components cancel out andequation (12) can be written:3. Genetic AlgorithmMany optimization strategies are classified under the name Genetic Algorithm (GA) and thus this term docs not identify precisely an algorithm hut rather a wide class of stochastic zero-th order search methods for scalar ob jective functions, which share some common points 7, 6. The main issues characterizing an algorithm belonging to GA are: Natural selection: all algorithms recall the mechanism of Darwinian selection. Each point in the con-figuration space is considered as an individual and it has associated a value of the Objective Function (OF). In the general assumption, the parameter values in design space are considered similar to the Genetic Outfit (GO) of the “individual“ (genotype) and its OF value is considered as a measure of its fitness to stand living in the environment (phenotype), that is if a maximum of the function is looked for an individual the greater is the value of O F and the larger is possibility t o survive. Individuals are then selected by the principle of the survival of best fit.Genetic Outfit: each point in the space of the degrees of freedom of the problem defines a GO. This GO can he coded as a binary string, as it was in the original definition of GA, or LLS a real valued vector which is often preferred in problems with continuous variables.Generation: search is performed through several evaluations of the individuals. All individuals arc then processed by the algorithm its a generation in the evolution of the species. The algorithm evaluates all OF values relative to 5the current generation (population) before applying the natural selection process which gives birth to the new one. The first generation is usu. ally defined in a random way inside the design space. This phase of the algorithm can be parallelized.Combination of GO: new individuals are generated throughout the search by combining genotypes of individuals by means of mechanisms mutuated from nature: cross-over involves the creation of a new individual by merging together two genetic outfits of individuals considered as parents; mutation concerns a random alteration of the genetic outfit of an individual by means of some change in its genotype; reproduction usually configures the possibility of passing a genetic outfit inaltered to the next generation. Each of these mechanisms gives rise to a share of individuals in the next generation.Roulette wheel selection: the natural selection process is not applied in a deterministic way but, more properly, as a biased stochastic process where fitter individuals have a greater chance to pass part of their GO to the next generation. This usually helps the method to Escape local extrema of the OF.Selective pressure: as it is defined GA tends to find out the fittest individual in the design variable space eliminating from the search all local extrema of the function. Even if this global optimization property is one of the reason of success of GA, often it implies neglecting multiple extrema present in multimodal functions. Several modifications have been proposed to overcome this limitation SI. Most of these methods, when evaluating the chance of survival of a configuration, take into account not only phenotype but also individual genotypes.The present implementation of GA is based on a continu-ous variable genotype representation, thus each individual is a vector of variables corresponding to a point in design variables space. Cross-over mechanism is introduced by means of a weighted sum between two individuals of the previous generation, while mutation is obtained by adding a random vector to an existing one. The main features of the algorithm are presented in l.In order to restrain the selective pressure of the method, a clustering process of the individuals in a generation is performed. This process contrasts the natural tendency of the method to create new individuals very close to the hest ones of the previous -generation. Allowing- onlv. good individuals with differ-ent genotypes to enter the ”mating pool”, that is the set of individuals able to reproduce, helps keeping diversity among the population 3, 2.At the end of the GA, a refinement of the solution is done by a deterministic algorithm in order to improove the quality of the OF.4. Multiobjective Optimization and Fuzzy LogicIn design problems there are several objectives to be optimized at the same time. These problems are referred to as Multiobjective Optimization Problems ( sMOP) or Vector Optimization Problems ( sVOP). In scalar optimization the definition of the optimal point is univocal: in maximization problems, for example, a greater value of the objective function is preferred and the search of maximum should be in that direction. In MOPs it is of-ten difficult to compare different points because objectives can be in conflict. The search for Pareto 6boundary can be used directly in a GA procedure lo;alternatively, as in the present case a scalarization procedure can be applied to the objective functions.Many techniques can be used to solve sMOP, in order to translate thevectorial problem into a scalar problem. The technique adopted in this work uses the fuzzy logic theory9. Classical logic is a binary one in which a statement can be true (1) or false (0). Fuzzy Logic is a multivalue logic where a statement can he simultaneously partially true and false. Each statement can be translated by a Membership Function (MF) p into a number which states its truthfulness within the range 0, l (p = 0, completely false, p = 1 completely true). In optimization problems sFare used to translate the value of the i - t h OF f, into a normalized value p( if） which indicates the degree of satisfaction of the i- t h objective. The set of the MF contains two types of function: sigmoidal and gaussian. The sigmoidal function (Fig. 2(a) can be used to translate statements such as the power is acceptable if it is greater then 50 kW and unacceptable if it is lower then 10 kW. It is characterized by two values: the lower bound rej and the upper bound acf . The values assumed by the MF are respectively 0.1 and 0.9. The gaussian function (Fig. 2(b) can be used to represent statement such as the energy of the system must be about 100 k J . This function. is univocally identified giving the mean value meanf in which the MF is 1, and the standard deviation U . In Fuzzy approach to MOP, each objective can be evaluated by itself, the global fuzzy objective function is generated by 5MOP becomes a scalar problem; the goal is to determine the set Of parameter which maximize equation (I3).5. ResultsThe procedure has been applied to a radial coupler for two different RCS materials: copper and aluminium. The values of materials characteristic, (conductivity U , density 6, cost) are reported in Tab. 1. For each of these materials, two objectives have been optimized: torque, which expression is given by equation (12), and the total momentum7Figure 2: Set of MF for Fuzzy OptimizationTable 1: Materials Parametersof inertia, given by the sum of the single contributions of the rotating parts:where Mi is the mass of the thi material. These two parameters define the model from the mechanical point of view. Moreover, a constrain on the maximum current density value limJ, in the RCS has been applied in order to limit the RCS temperature. From equation (8), it is known that the current density has a nonuniform distribution along the r coordinate of the RCS and can be rep-resented by a proper troncated Fourier series expansion along the 0 coordinate. For this reason, limJ, is the aver-age value (along T ) of the RMS current density waveform. A 8rasonable value of limJ = 50 A/mm2 for the copper and of limJ, = 40 A/mni2 for the aluminium has been chosen. From the geometrical data of the coupler shownFigure 3: Section of the couplerin Fig. 1, we have identified five optimizat
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