【作者】 赵洪雷；

【导师】 韩万金； 王松涛；

【作者基本信息】 哈尔滨工业大学 ， 动力机械及工程， 2007， 博士

【摘要】 涡轮作为航空发动机的重要部件,其性能的改进对发动机整体性能的提高有着至关重要的影响。气动设计是涡轮设计的核心,没有高水平的气动设计就没有高水平的涡轮性能。过去大量采用传统的准三维设计方法,随着数值模拟技术的不断成熟,近年来,基于优化理论的气动设计方法逐渐兴起,并且吸引了国内外同行学者的研究兴趣。但是将现代优化设计与准三维设计这两种方法联合起来对涡轮进行气动优化设计的研究,目前还很少见到。本文的主要工作就是研究联合应用准三维设计和现代自动优化设计对多级涡轮进行气动优化的设计方法。本文首先通过比较包括现代自动优化设计方法的涡轮气动现代设计体系的优缺点,提出将现代自动优化设计方法与传统设计方法有机结合能有效地进行涡轮叶型的气动优化设计。采用四级轴流试验涡轮的试验数据,验证了应用Numeca商用软件模拟不同工况下多级涡轮内部流场的数值结果的可靠性。为了实现准三维设计与自动优化设计的有机结合,本文编制了多级轴流涡轮气动优化设计流程,此流程包括准三维设计过程和多级局部优化设计过程。准三维设计主要是S2流面正问题计算,通过准三维设计进行初步设计,初步提高性能,确定总体参数,为下一步的优化设计打下基础。然后采用多级局部优化设计,通过提高局部性能来提高总体性能。局部优化过程联合采用人工神经网络和遗传算法,流场计算采用全三维粘性流N-S方程求解。局部优化设计有三个特点:(1).针对每列叶栅的气动特性进行局部优化;(2).各列叶栅反复多次优化;(3).粗细网格交替使用进行流场计算。总的来说,此设计流程可以发挥两种设计方法的优点,在不需要较多人力和时间的情况下完成设计。应用此设计流程分别对气冷涡轮级、三级涡轮和四级高负荷低压涡轮进行气动优化设计,总体性能均获得了不同程度的提高。通过分析近年来的典型涡轮损失模型,找到了影响各种损失主要因素的相关参数。在优化设计时,合理调整这些参数能够较大幅度地降低总流动损失,在此基础上,本文提出了局部优化的组合优化参数群的概念。组合优化参数群分别由叶型优化参数组、子午流道优化参数组、积叠规律优化参数组以及优化参数组的组合构成。本文给出了各参数组的建立方法、适用条件以及如何通过流场诊断采用组合优化参数群进行局部优化设计。最后,本文分析了优化设计中的两个难点问题,它们分别是高负荷低压涡轮的优化设计和多工况优化设计。在进行高负荷低压涡轮优化设计时要使用适当细的网格。此外,调整各级的功率、级负荷系数以及各级的静、动叶切向升力系数,使它们在各级中的分布尽量均匀,有利于提高涡轮的总体性能。多工况优化设计是多目标优化设计,在涡轮设计中是否采用,应该根据对具体情况的分析而定。对于各工况下性能相似的涡轮,应采用单工况为主的设计方法。对于各工况下性能差别较大的涡轮,必须采用多工况优化设计。更多还原

【Abstract】 Turbine is an important component of aero engine. The improvements of its performance are of significant meaning to enhance the whole performance level of engine. Aerodynamic design plays a core role in the design of turbine since no high level performance can be achieved without high level aerodynamic design. In past, traditional quasi-three dimensional design (Q3D) methods were widely adopted. However, in recent years, with the incessant maturity of numerical simulation technology, aerodynamic design method based on optimization theory has gradually become prevailing and attracts great interest of researchers in this field of the whole world. However, few studies can be found at present in relation to combining modern optimization design methods and Q3D methods to perform the aerodynamic optimization design of turbine. Based on this point, the main task of this article is to study the design method of combining modern automatic optimization design methods and Q3D methods so as to achieve aerodynamic optimization of the multistage turbine.First of all, through analysis of the advantages and shortcomings of the modern aerodynamic design system of turbine (including modern automatic optimization design methods), the paper brings forward that the aerodynamic optimization design of turbine blade profile can be effectively achieved by organically combining modern automatic optimization design methods and traditional design methods. The reliability of applying commercial software of NUMECA to simulate the internal flow field of multistage turbine at different work conditions is validated by adopting the test data of four-stage axial test turbine.In this paper, an aerodynamic optimization design process of multistage axial turbine is compiled to implement the organic combination of Q3D and automatic optimization design. The design process is composed of Q3D design course and multistage local optimization design course. Q3D methods mainly refer to S2 stream surface direct problem calculation. Applying Q3D to conduct preliminary design can elementarily improve the total performance and fix on the total parameter so as to establish a base for the optimization design in the next step. Then adopting multistage local optimization design further improves the total performance through improving local performance. Local optimization course jointly applies genetic algorithm and artificial neural network. Flow computation applies three-dimensional viscosity Navier–Stokes equation solver. Such local optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid to compute flow field. In general, the design process may make full use of these two design methods and complete design in the condition of without huge expenditure of manpower and calculation time. Such process was applied to the aerodynamic optimization design of an air-cooled turbine stage, a three-stage turbine and a four-stage high load low pressure turbine. The total performance of these three turbines is improved in some degree.Through analysis of the typical turbine loss model of recent years, correlative parameters that influence the main factors of all sorts of losses have been found out. In optimization design, the total flow loss can be reduced in a great extent by reasonably adjusting these parameters. On such basis, this paper brings forward the concept of combination optimization parameter group of local optimization. Combination optimization parameter group are composed of blade profile optimization parameter team, meridional channel optimization parameter team, stacking law optimization parameter team and the combination of optimization parameter team. The setting up methods, application condition of every parameter team and how to implement local optimization design by adopting parameter team through flow field diagnosis are presented in this paper. Finally, two difficult problems in optimization design are analyzed in this paper. The two problems are the optimization design of high load low pressure turbine and the optimization design under multiple work conditions. The optimization design of high load low pressure turbine should apply proper fine calculation grid. Furthermore, adjusting the power and the stage load coefficient of every stage and the tangential lift coefficient of every stator and rotor so as to distribute more uniform in every stage is beneficial to improve total performance of turbine. The optimization design under multiple work conditions is multi-objective optimization design. Whether or not applying optimization design under multiple work conditions in turbine design should be decided according to the concrete conditions. The turbine with similar performance under every work condition should adopt the design method of mainly focusing on single work condition while the turbine with very different performance under every work condition should be designed by adopting optimization design under multiple work conditions.更多还原