积垢的形成机理及其对轴流式压气机性能的影响研究

发布时间：2018-03-28 16:43

本文选题：积垢　切入点：轴流式压气机　出处：《华北电力大学》2014年博士论文

【摘要】：轴流式压气机是燃气轮机发电机组中重要的组成部分之一,由于机组功率的50%-60%由压气机消耗,因此压气机性能的高低直接影响发电机组的效率。在压气机的众多失效模式中,积垢造成的压气机性能退化达到70%-85%,因此研究压气机积垢的形成机理,分析积垢对轴流式压气机性能的影响,讨论压气机积垢的监测与控制策略,对于燃气轮机机组的安全、高效和健康运行具有非常重要的现实意义。本文取得的主要研究成果如下： (1)从微观角度对颗粒物在压气机内部的输送过程和沉积过程进行理论和数值仿真分析,建立了颗粒物输送机理与叶片不同位置之间的对应关系,所得到的结论是压力面积垢是由于较大颗粒物的惯性作用的结果,而吸力面积垢的主要原因是因为小颗粒物的湍流扩散造成的。然后,分别研究了颗粒物大小、颗粒物浓度、入口温度、初始速度、旋转速度、表面粗糙度和湿度等因素对积垢形成的影响。 (2)对单级轴流式压气机积垢行为建模方法进行分析,对过去采用表面粗糙度以及厚度等参数的设定表征积垢行为存在的缺陷进行讨论,提出利用压气机叶片型面参数的变化,将逆向工程中的测量技术与传统的CFD分析方法相结合,获取积垢叶片的准确几何模型,真实反映叶片积垢的实际状态,提高性能仿真的精度。 (3)提出了多级轴流式压气机性能预测的新方法,改变原有方法只能针对某一指定时刻积垢状态进行分析的缺点,将积垢的多级轴流式压气机划分为两部分,分别选择合理的计算方法。对于积垢部分的性能计算,采用级堆叠方法、相似理论和线性退化模型相结合,充分考虑级间积垢分布的差异及其它影响因素。对于无积垢级的性能计算,采用平均微元级计算其性能。 (4)提出了压气机积垢状态监测的优化方案,改变过去仅仅依靠某一性能参数进行监测所存在的缺陷。在运行的初期,主要基于热力学性能参数的变化进行判断,当性能参数下降到一定程度的时候,再结合对叶片型面参数的监测,对压气机积垢的状态进行准确评估。 (5)对压气机清洗时间间隔进行分析,获得优化数学模型,为压气机积垢的控制提供支持。
[Abstract]:Axial flow compressor is one of the important parts of gas turbine generator set. Because 50% to 60% of unit power is consumed by compressor, the performance of compressor directly affects the efficiency of generator set. The performance degradation of compressor caused by fouling has reached 70-85.Therefore, the formation mechanism of compressor fouling is studied, the influence of fouling on the performance of axial flow compressor is analyzed, and the monitoring and control strategy of compressor fouling is discussed, which is safe for gas turbine units. Efficient and healthy operation is of great practical significance. The main research results obtained in this paper are as follows:. 1) from the microscopic point of view, the transport process and deposition process of particulate matter in the compressor are analyzed theoretically and numerically, and the corresponding relationship between the conveying mechanism of particulate matter and the different position of the blade is established. The conclusion is that the pressure area scale is the result of the inertial action of the larger particles, and the main reason of the suction area scale is the turbulent diffusion of the small particles. The influence of inlet temperature, initial velocity, rotation speed, surface roughness and humidity on fouling formation. This paper analyzes the modeling method of scaling behavior of single stage axial flow compressor, discusses the defects of setting parameters such as surface roughness and thickness to characterize the scaling behavior in the past, and puts forward the change of profile parameters of compressor blade. By combining the measurement technology in reverse engineering with the traditional CFD analysis method, the accurate geometric model of the scalded blade is obtained, which reflects the actual state of the blade fouling and improves the accuracy of performance simulation. A new method for predicting the performance of multistage axial flow compressor is proposed. The original method can only be used to analyze the fouling state at a given time, and the multi-stage axial flow compressor is divided into two parts. For the performance calculation of the fouling part, the hierarchical stacking method, the similarity theory and the linear degradation model are adopted. Considering the difference of scale distribution among stages and other influencing factors, the average microelement level is used to calculate the performance of scale free grade. In this paper, the optimization scheme of compressor fouling condition monitoring is put forward, which changes the defects existing in monitoring only one performance parameter in the past. In the initial stage of operation, it is mainly judged by the variation of thermodynamic performance parameters. When the performance parameters are reduced to a certain extent, combined with the monitoring of blade profile parameters, the compressor fouling state is accurately evaluated. 5) the cleaning time interval of compressor is analyzed, and the optimized mathematical model is obtained, which provides support for the control of compressor fouling.
【学位授予单位】：华北电力大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM31

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