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XX设计(XX)中期报告题目LWZ150双头螺杆型干式无油真空泵设计系 别 专 业 班 级 姓 名 学 号 导 师 20XX年 03月17 日1.设计（论文）进展状况 1.1双头螺杆真空泵设计方案 确定螺杆型线(即阴阳转子型线) 确定螺杆基本尺寸 1.2.转子的型线选择及其参数 1.2.1转子型线及其要素 螺杆泵最关键的是一对相互啮合的转子。转子的齿面与转子轴线垂直面的截交 线称为转子型线。 对于螺杆泵转子型线的要求，主要是在齿间容积之间有优越的密封性能，因为这些齿间容积是实现气体压缩的工作腔。对螺杆泵性能有重大影响的转子型线要素有接触线、泄漏三角形、封闭容积和齿间面积等。 接触线 螺杆泵的阴、阳转子啮合时，两转子齿面相互接触而形成的空间曲线称为接触线。如果转子齿面间的接触连续，则处在高压力区内的气体将通过接触线中断缺口，向低压力区泄漏。阴、阳转子型线啮合时的啮合点轨迹，称为啮合线。啮合线实质是接触线在转子端面上的投影。显然接触线连续，意味着啮合线应该是一条连续的封闭曲线。 泄漏三角形 在接触线顶点和机壳的转子气缸孔之间，会形成一个空间曲边三角形，称为泄漏三角形。若啮合线顶点距阴、阳转子齿顶圆的交点较远，则说明泄漏三角形面积较大。 封闭容积 如果在齿间容积开始扩大时，不能立即开始吸气过程，就会产生吸气封闭容积。吸气封闭容积的存在，影响了齿间容积的正常充气。从转子型线可定性看出封闭容积的大小。 齿间面积 它是齿间容积在转子端面上的投影。转子型线的齿间面积越大，转子的齿间容积就越大。 1.2.2 坐标系建立 为了用数学方程描述螺杆型线中各段组成齿曲线，建立如图1所示的四个坐标系：图1 坐标系关系图(1) 固结在阳转子的动坐标系(2) 固结在阴转子的动坐标系。(3) 阳转子的静坐标系。(4) 阴转子的静坐标系。由于螺杆泵的阴、阳转子之间是定传动比啮合，故有:（1） 式中，2、1为阴、阳转子转角；n2、n1为阴、阳转子转速；2、1 为阴、阳转子角速度；R2t、R1t为阴、阳转子节圆半径；z2、z1为阴、阳转子齿数；i为传动比； A为阴、阳转子中心距。 1.2.3 坐标变换螺杆泵转子型线上的每一点，都可以表示在上述四个坐标系中，这些坐标之间的变换关系式如下：a 动坐标系与静坐标系的变换 （2）b 动坐标系与静坐标系的变换 （3）c 静坐标系与静坐标系的变换（4）d 动坐标系与动坐标系的变换（5）e 动坐标系与动坐标系的变换（6） 1.2.4 转子型线设计原则 满足啮合要求。螺杆泵的阴、阳转子型线必须是满足啮合定律的共轭型线。 形成长度较短的连续接触线。为了尽可能减少气体通过间隙带的泄漏，要求设法缩短转子间的接触线长度。 应形成较小面积的泄漏三角形。 应使封闭容积较小。吸气封闭容积导致泵功耗增加、效率降低、噪声增大。所以转子型线应使封闭容积尽可能小地。 齿间面积尽量大。较大的齿间面积使泄漏量占的份额相对减少，效率得到提高。 1.3 双螺杆空气泵螺杆尺寸的确定 1.3.1双螺杆泵螺杆尺寸按以下的关系式确定：阳转子节圆直径 d1=D1/（1+h1）阴转子节圆直径 d2=d1/（z2/z1）阳转子根圆直径 Di1=d1/（1-h2）阴转子顶圆直径 De2=d1/（i+h2）阴转子根圆直径 Di2=d1/（i-h1）转子螺杆长度 L=（L/De1）De1中心距 A=0.5（d1+d2）阴转子扭转角 2=1/i阳转子的导程 b1=360L/1阴转子的导程 b2=360L/2阳转子的转速（r/min） n1=60u1/3.14De1阴转子的转速（r/min） n2=n1/i节圆螺旋角 =arctg（b1/2r1）= arctg（b2/2r2） 1.3.2 本设计中泵转子螺杆部分的几何尺寸选用标准系列。取阳转子圆周速度u1=10m/s，则阳转子转速n1=60u1/(3.14D1)=6010/(3.140.102)=1873.3608r/min.阴转子转速n2=n1/I=1873.3608/(0.6667)= 2809.9007 r/min. 1.3.3 由于所涉及的螺杆式无油真空泵的参数要达到抽速为150L/S,极限真空度达到15Pa，则转子参数如下： 中心距：80 ；阳转子齿数:：4 ；阴转子齿数:：6 ； 阴转子外径：R48 ；阳转子外径: R51 1.4 转子型线的绘制 图2（阴阳转子啮合的左视图）图3 （阴阳转子啮合） 1.5 外文翻译的完成 1.5.1 所选择的外文文献： Research of an unattended intelligentized control system of air compressor for supplying constant-pressure air 1.5.2 翻译：一个用来提供恒定空气压力的无人值守的智能化控制系统的空气压缩机的研究2.存在问题及解决措施 本设计对于螺杆干式无油真空泵的要点在阴阳转子型线的选择和设计上，转子的工作状况决定真空泵的工作情况，达到一定的真空效率和输出功率。 2.1 在使用方面考虑泵的载荷的大小所选择的合适的真空泵，考虑使用环境，外界条件以为维修保养的方便。 2.2 泵使用后的清洗和降温，使用冷却吹扫法给泵腔内降温，将我使用N或蒸汽。 2.3考虑进气量的不足，由于会出现粉尘或者颗粒的缘故，过滤器阻塞，阴阳转子间隙大，需清洗过滤器，调整阴阳转子 之间的间隙。3.后期工作安排 14-16周，完成转子的结构设计，绘制整体转配图。 17-18周，整理设计资料，撰写毕业设计论文，准备答辩。 指导教师签字： 年 月 日一个用来提供恒定空气压力的无人值守的智能化控制系统的空气压缩机的研究Lingen Chen Jun Luo Fengrui Sun Chih Wu摘要 对多级压缩机的优化设计模型，本文假设固定的流道形状以入口和出口的动叶绝对角度，静叶的绝对角度和静叶及每一级的入口和出口的相对气体密度作为设计变量，得到压缩机基元级的基本方程和多级压缩机的解析关系。用数值实例来说明多级压缩机的各种参数对最优性能的影响。关键词 轴流压缩机 效率 分析关系 优化 1 引言轴流式压缩机的设计是工艺技术的一部分，如果缺乏准确的预测将影响设计过程。至今还没有公认的方法可使新的设计参数达到一个足够精确的值，通过应用一些已经取得新进展的数值优化技术，以完成单级和多级轴流式压缩机的设计。计算流体动力学（CFD）和许多更准确的方法特别是发展计算的CFD技术，已经应用到许多轴流式压缩机的平面和三维优化设计。它仍然是使用一维流体力学理论用数值实例来计算压缩机的最佳设计。Boiko通过以下假设提出了详细的数学模型用以优化设计单级和多级轴流涡轮：（1）固定的轴向均匀速度分布（2）固定流动路径的形状分布，并获得了理想的优化结果。陈林根等人也采用了类似的想法，通过假设一个固定的轴向速度分布的优化设计提出了设计单级轴流式压缩机一种数学模型。在本文中为优化设计多级轴流压缩机的模型，提出了假设一个固定的流道形状，以入口和出口的动叶绝对角度，静叶的绝对角度和静叶及每一级的入口和出口的相对气体密度作为设计变量，分析压缩机的每个阶段之间的关系，用数值实例来说明多级压缩机的各种参数对最优性能的影响。2 基元级的基本方程考虑图1所示由n级组成的轴流压缩机, 其某一压缩过程焓熵图和中间级的速度三角形见图2和图3，相应的中间级的具体焓熵图如图4，按一维理论作级的性能计算。按一般情况列出轴流压缩机中气体流动的能量方程和连续方程,工作流体和叶轮的速度。在不同级的轴向流速不为常数，即考虑, () 时的能量和流量方程。在下列假定下分析轴流压缩机的工作: 相对于稳定回转的动叶、静叶和导向叶片机构, 气体流动是稳定的； 流体是可压缩、无黏性和不导热的； 通过级的流体质量流量为定值；在实际工质的情况下, 压缩过程是均匀的；本级出口绝对气流角为下一级进口角绝对气流角；忽略进出口管道的影响。 在每一级的具体焓如下： （1） （2）第阶段的动叶和静叶的焓值损失总额计算如下： （3） （4）其中是第阶段动叶叶片轮廓总损失系数，是第阶段静叶叶片轮廓总损失的系数。 图1 n级轴流式压缩机的流量路径。叶片轮廓损失系数和是工作流体和叶片的几何功能参数。它们可以使用各种方法及视作常量来计算。当和看做工作流体和叶片的几何功能参数时，可以使用Ref迭代的方法来计算损失系数。使用迭代方法解决计算损失系数：（1）选择和初始值，然后计算各级的参数。（2）计算的，值，重复第一步，直到计算值和原值之间的差异足够小。第阶段理论所需计算得： （5）第阶段实际所需计算得： 图2 n级压缩机的焓熵图 图3 中间级的速度三角形 图4 中间级的焓熵图 （6）基元级反应度定义为。因此有： （7） 在这里，视作速度系数,它们的计算为:和 （8） （9）3 级组的数学模型压缩机各级的比压缩功为则总的比耗功为， 各级的滞止等熵能量头为，则级组各级滞止等熵比压缩功总和为，级组等熵比压缩功为, 则为压缩机的重热系数。根据定义,多级压缩机通流部分滞止等熵效率为： 求解确定各级能量头的分配： （11）方程式（11）同样可以写作：. (12)出于方便，一些参数简化约束计算做了如下定义： （13） （14） （15） （16）这里 是气动力函数，在这里的是滞止声速相对应的，且 是相对面积，是相对密度，是叶片高 是流量系数。通过Boiko的论文引入等熵线系数，一个是： （17）这里 （18）因此约束条件也可写作 （19） （20） （21）在这里多级轴流式压缩机滞止等熵线的效率计算如下： （22）这里是多级压缩机的等熵工作系数，每一级的等熵工作系数是。现在的优化问题是寻找和的最佳值，来找出在方程（1921)约束下的目标函数的最大值。4 结论 一旦这些系统和定义的常数按目标实现自己系统功能，在他最理想的环境下达到预计函数最大的程度。其呈现的并非是一个线性的而是一阶梯函数。本优化模型是（2n +1）约束功能和一个n级轴流压缩机（4n + 1）变量的非线性规划程序。例如改善外部法或SUMT法，对于这样的问题Powell采用在无约束极小化技术与一维最小的抛物线插值方法。人们已经发现是非常有作用的。表1 各级相对面积级 () 1 2 3 4 5 6 7相对面积 10.9360.8860.8090.7290.7010.647表2 原始数据和设计计划参数上限下限原始数据最佳数据=0.732=0.732=0.732=0.6=0.59=0.59=0.49=0.59549080.589172.685874.911666.5570359049.5045.0045.0045.00549084.133876.343177.5568.2003359049.5045.0045.0045.00549066.41159.708069.058255.7046359049.541845.0045.0046.6157549089.9990.0090.9989.6147031.0891.04591.09131.093031.1481.14741.15491.0798031.4241.39701.39001.2624031.4241.41171,。41981.2624031.5651.53721.60911.3345031.6181.63381.66711.44500.90200.90500.90740.89555 数值计算例子在计算中，做，,则为0.04, 为0.025和为0.02的设置。表1列出了在每个级的相对面积。应当指出会有一些优化目标的关系与这些量纲的影响是工作流体参数的功能和流动路径的几何参数设置。然而，得到的关系不会改变流体性质。对于3级压缩机中，有13个设计变量和7个约束条件。此外，较低上限约束的13个设计变量的值也应考虑在计算中。优化变量的上限和下限，原来的设计方案中优化不同流量系数和工作系数的结果列于表2。由此可以看出，优化程序是有效和实用的。计算结果表明，最佳停滞等熵效率是随工作系数和流量系数的递减而递减的函数。工作系数影响最佳停滞等熵效率的作用大于流量系数。各值流量系数和工作系数，最优的最后一级输出绝对角度总是接近。6 结论在本文中在研究固定流形的多级轴流压缩机的效率优化中使用一维流体理论研究。根据压缩机普遍特性和特征间关系。由展示的数值量其结果可以为多级压缩机的性能分析和优化提供一些指导。这是一个初步的研究将其不可避免的使用多目标数值优化技术和人工神经网络算法用于分析压缩机优化。参考文献（见原文）术语 声音速度 (m/s) c 绝对速度 (m/s)F 过流面积 f 相对面积G 空气质量流量 h 焓i 焓比 k 速度系数l 叶片升度 n 级数p 压力 R 理想气体常数s 特定熵 T 温度u 轮线速度 W 相对速度y 相对密度希腊符号 绝对气流角, 相对气流解, 气动力系数 效率 流量系数 热率参数 量纲速度 气体密度, 反动度 气动力系数 能量头系数 损失系数下标 轴向 重热系数 临界 第级 第阶段 理想的 动叶 静叶 等熵过程 切向速度1 动叶入口点 2 动叶的出口点3 静叶出口点 * 滞止参数Research of an unattended intelligentized control system of air compressor for supplying constant-pressure airLingen Chen , Jun Luo , Fengrui Sun , Chih Wu Postgraduate School, Naval University of Engineering, Wuhan, 430033, PR China Mechanical Engineering Department, US Naval Academy, Annapolis MN21402, USAAvailable online 28 November 2007AbstractA model for the optimal design of a multi-stage compressor, assuming a fixed configuration of the flow-path, is presented.The absolute inlet and exit angles of the rotor, the absolute exit angle of the stator, and the relative gas densities at the inlet and exit stations of the stator, of every stage, are taken as the design variables. Analytical relations of the compressor elemental stage and the multi-stage compressor are obtained. Numerical examples are provided to illustrate the effects of various parameters on the optimal performance of the multi-stage compressor. 2007 Elsevier Ltd. All rights reserved.Keywords: Multi-stage axial-flow compressor; Efficiency; Analytical relation; Optimization1. IntroductionThe design of the axial-flow compressor is partially an art. The lack of accurate prediction influences the design process. Until today, there are no methods currently available that permit the prediction of the values of these quantities to a sufficient accuracy for a new design. Some progresses has been achieved via the application of numerical optimization techniques to single- and multi-stage axial-flow compressor design 122.Especially with the development of computational fluid-dynamics (CFD), many more accurate methods of calculating have been presented in many references in which the techniques of CFD have been applied to two- and three-dimensional optimal designs of axial-flow compressors 1720. However, it is still of worthwhile significance to calculate, using one-dimensional flow-theory, the optimal design of compressors. Boiko 23 presented a detailed mathematical model for the optimal design of single- and multi-stage axial-flow turbines by assuming (i) a fixed distribution of axial velocities or (ii) a fixed flow-path shape, and obtained the corresponding optimized results. Using a similar idea, Chen et al. 22 presented a mathematical model for the optimal design of a single-stage axial-flow compressor by assuming a fixed distribution of axial velocities.In this paper, a model for the optimal design of a multi-stage axial-flow compressor, by assuming a fixed flow path shape, is presented. The absolute inlet and exit angles of the rotor, the absolute exit angle of the stator, and the relative gas densities at the inlet and exit stations of the stator, of each stage, are taken as the design variables. Analytical relations of the compressor stage are obtained. Numerical examples are provided to illustrate the effects of various parameters on the optimal performance of the multi-stage compressor 2. Fundamental equations for elemental-stage compressor Consider a n-stage axial-flow compressor see Fig. 1. Fig. 2 shows the specific enthalpyspecific entropy diagram of this compressor. For a n-stage axial-flow compressor, there are (2n + 1) section stations. The stage velocity triangle of an intermediate stage (i.e. jth stage) is shown in Fig. 3. The corresponding specific enthalpyspecific entropy diagram is shown in Fig. 4. The performance calculation of multi-stage compressor is performed using one-dimensional flow theory. The analysis begins with the energy and continuity equations, and the axial-flow velocities of the working fluid and wheel velocities at the different stations in the compressor are not considered as constant, that is, , (), where i denotes the ith station and j denotes the jth stage. The major assumptions made in the method are as follows The working fluid flows stably relative to the vanes, stators and rotors, which rotate at a fixed speed. The working fluid is compressible, non-viscous and adiabatic. The mass-flow rate of the working fluid is constant. The compression process is homogeneous in the working fluid. The absolute outlet angle of the working fluid, in jth stage, is equal to the absolute inlet angle of the working fluid in (j+1)th stage. The effects of intake and outlet piping are neglected.The specific enthalpies at every station are as follows （1） （2）The total profile losses of the jth stage rotor and the stator are calculated as follows: （3） （4）Whereis the total profile loss coefficient of jth stage rotor-blade and is that of jth stage-stator blade.Fig. 1. Flow-path of a n-stage axial-flow compressorFig. 2. Enthalpyentropy diagram of a n-stage compressorFig. 3. Velocity triangle of an intermediate stageFig. 4. Enthalpyentropy diagram of an intermediate stage.The blade profile loss-coefficients and are functions of parameters of the working fluid and blade geometry. They can be calculated using various methods and are considered to be constants. When and are functions of the parameters of the working fluid and blade geometry, the loss coefficients can be calculated using the method of Ref. 24, which was employed and described in Ref. 21. The optimization problem can be solved using the iterative method:(1) First, select the original values of and and then calculate the parameters of the stage.(2) Secondly, calculate the values of and , and repeat the first step until the differences between the calculated values and the original ones are small enough.The work required by the jth stage is （5）The work required by the jth rotor is: （6）The degree of reaction of the jth stage compressor is defined as . Hence, one has （7）Where， are the velocity coefficients, and they are defined as: andThe constraint conditions can be obtained from the energy-balance equation for the one-dimensional flow （8） （9）3. Mathematical model for the behaviour of the multi-stage compressorThe compression work required by each stage is. The total compression work required by the multi-stage compressor is . The stagnation isentropic enthalpy rise of every stage is . The sum of the stagnation isentropic enthalpy rise of each stage is, while the stagnation isentropic enthalpy rise of the multi-stage compressor is . One has,The stagnation isentropic efficiency of the multi-stage axial-flow compressor is （10）The total energy-balance of a n-stage compressor gives: （11）Eq. (11) can be rewritten as. (12)For convenience, in order to make the constraints dimensionless, some parameters are defined: （13） （14） （15） （16）Where are the aerodynamic functions, and , where is the stagnation sound velocity and ，is the relative area, is the relative density, where l is the height of the blade, and is flow coefficient. Introducing the isentropic coefficient used by Boiko 23, one has （17）Where （18）Therefore, the constraint conditions can be rewritten as： （19） （20） （21）and the stagnation isentropic efficiency of the multi-stage axial-flow compressor can be rewritten as （22）Where is isentropic work coefficient of the multi-stage. The isentropic work coefficient of each stage is defined as .Now the optimization problem is to search the optimal values of and for finding the maximum value of the objective function under the constraints of Eqs. (19)(21).4. Solution procedureOnce the system variables, the objective function, and the constraints are defined, a suitable method has to be adopted to determine the values of the design variables that maximize the objective function while satisfying the given constraints. The present optimization model is a non-linear programming procedure withTable 1Relative areas for the stationsStation ()1234567Relative area 10.9360.8860.8090.7290.7010.647Table 2Original and optimal design plans参数上限下限原始数据最佳数据=0.732=0.732=0.732=0.6=0.59=0.59=0.49=0.59549080.589172.685874.911666.5570359049.5045.0045.0045.00549084.133876.343177.5568.2003359049.5045.0045.0045.00549066.41159.708069.058255.7046359049.541845.0045.0046.6157549089.9990.0090.9989.6147031.0891.04591.09131.093031.1481.14741.15491.0798031.4241.39701.39001.2624031.4241.41171,。41981.2624031.5651.53721.60911.3345031.6181.63381.66711.44500.90200.90500.90740.89555. Numerical exampleIn the calculations, , , , n = 3, R = 286.96 J/(kgK), , and are set. The relative areas at every station are listed in Table 1. It should be pointed out that there will be some influence on the relation of the optimization objective with these dimensionless parameters if are functions of the working fluid parameters and geometry parameters of the flow-path configuration. However, the relation obtained will not change qualitatively. For a 3-stage compressor, there are 13 design variables and 7 constraint conditions. Besides, the lower and upper limit value constraints of the 13 design variables should also be considered in the calculations. The lower and upper limits of the optimization variables, the original design plan, and the optimization results for different flow coefficients and work coefficients are listed in Table 2. It can be seen that the optimization procedure is effective and practical. The calculations show that the optimal stagnation isentropic efficiency is an increasing function of the work coefficient and a decreasing function of the flow coefficient. The effect of the work coefficient on the optimal stagnation isentropic-efficiency is larger than that of the flow coefficient. Also for various values你of the flow coefficients and work coefficients, the optimal absolute exit-angle of the last stage always approaches .6. ConclusionIn this paper, the efficiency optimization of a multi-stage axial-flow compressor for a fixed flow shape has been studied using one-dimensional flow-theory. The universal characteristic relation of the compressor be haviour is obtained. Numerical examples are presented. The results can provide some guidance as to the performance analysis and optimization of the multi-stage compressor. This is a preliminary study. It will be necessary to use multi-objective numerical optimization techniques 1113,20,21,2529 and artificial neural network algorithms 10,19,30,31 for practical compressor optimization.References1 Wall RA. Axial-flow compressor performance prediction. AGARD-LS-83 1976(June): Gu C, Miao Y. Blade design of axial-flow compressors by the method of optimal control theory. Trans ASME, J Turbomach1987;109(1):99107.3 Hearsey RM. Numerical optimization of axial compressor design. ASME paper No. 89-GT-14.4 Tuccille R. A proposal for optimized design of multi-stage compressors. ASME paper No. 89-GT-34.5 Lim JS, Chung MK. Design-point optimization of an axial-flow compressor stage. Int J Heat Fluid Flow 1989;10(1):4858.6 Massardo A, Statta A. Axial-flow compressor design optimization: Part I-pitchline analysis and multi-variable objective functioninfluence. Trans ASME, J Turbomach 1990;112(2):339404.7 Massardo A, Statta A, Marini M. Axial-flow compressor design optimization: Part II-throughflow analysis. Trans ASME, JTurbomach 1990;112(2):40511.8 Egorov IN, Fomin VN. Numerical method of optimization of a multi-stage axial compressor. Experimental and ComputationalAerothermodynamics of Internal Flows. World Publishing Corporation; 1990, p. 495503.9 Tuccille R. Optimal design of axial-flow compressor. ASME IGTI 1990;5:22733.10 Geoge H, Stuart B. Preliminary design of axial compressors using artificial intelligence and numerical-optimization techniques.ASME paper No. 91-GT-334.11 Chen L. A brief introduction of multi-objective optimization for an axial-flow compressor-stage. Gas Turbine Technol 1992;5(1):113in Chinese.12 Egorov IN, Krekinin GV. Multi-criterion stochastic optimization of an axial compressor. ASME IGTI 1992;7:56370.13 Egorov IN. Optimization of multi-stage axial compressor in a gas-turbine engine system. ASME paper, 92-GT-424 1992.14 Chen L. Some new developments on the optimal design of turbomachinery during the past decade. J Eng Thermal Energy Power1992;7(4):21421 in Chinese.15 Egorov IN. Deterministic and stochastic optimization of a variable axial-compressor. ASME paper No. 93-GT-397.16 Sun J, Elder RL. Numerical optimization of a stator vane setting in multi-stage axial-flow compressors. Proc Inst Mech Eng1998;212(A4):24759.17 Calvert WJ, Ginder RB. Transonic fan and compressor design. Proc Inst Mech Eng 1999;213(C5):41936.18 Gallimore SJ. A