分析了主动间隙控制活门漏油、高压涡轮叶片榫头裂纹、高压涡轮叶片腐蚀和高压涡轮冷却部件堵塞等四个典型故障的解决方法,总结出高压涡轮主动间隙控制系统、高压涡轮叶片、高压涡轮导向器及高压涡轮罩环的设计改进措施,为我国设计出维修性好和性能高的大涵道比涡扇发动机的高压涡轮提供了参考。

First of all ,the paper elaborates on solutions to the four typical failures of active clearance con-trol valve leakage ,HPT blade joint crack ,HPT blade corrosion and HPT cooling components jam .Sec-ondly ,it sums up the measures of design improvement of HPT active clearance control system , HPT blade ,HPT nozzle and HPT shroud .Finally ,it provides the reference for the development of high pres-sure turbine of high bypass ratio turbofan engine for Chinese independent intellectual property rights with good maintainability and high performance .

气膜冷却是保证航空发动机涡轮导向叶片正常工作的重要方法之一。为研究在较高涡轮前温度条件下气膜孔布置对叶片前缘降温的效果，采用ANSYS CFX的RNG k‐ε模型对选定叶片进行了叶片前缘气膜冷却仿真。根据驻点、流动分离点等条件确定了气膜孔位置，并根据仿真效果对气膜孔布置进行调整。通过仿真结果表明，调整好位置的气膜孔对叶片前缘的冷却效果比较明显。

Film cooling is one of the important methods to ensure the turbine guide blade of aircraft engine working normally .In order to research the cooling effect that the gas film holes have on the leading edge of the blade under a higher turbine inlet temperature ,this paper adopts ANSYS CFX''s RNG k‐εturbulence mode to simulate the film cooling effect of the leading edge of the blade and to determine and adjust the po‐sition of the gas film hole according to the stagnation point and the flow separation point .Simulation re‐sults show that the gas film with a well‐adjusted position has a better cooling effect on the leading edge of the blade .

对一高压涡轮导向器扇形叶栅进行试验,发现相邻测试叶片流场的周期性较差,给导向器气动性能试验评估带来极大困难。对试验件的数值模拟亦给出了相同结果。为提高试验评估精度,采用几何设计和数值模拟迭代的方法,对试验件进行了改进设计。对改进试验件进行的试验表明,高压涡轮导向器扇形叶栅通道内的周期性得到明显改善,该试验结果可较为准确地评估导向器的气动性能。

An experiment of the sector cascades of high pressure turbine nozzle without expected periodici-ty of flow fields brings difficulty to evaluate the aerodynamic performance of guide vane. The simulation re-sults verified this characteristic of nonperiodicity. In order to improve the accuracy of experiment assess-ment, the improved design of the test section was carried out by means of iteration between geometry design and numerical simulation. The experiment results of improved specimen indicate that the periodicity is obvi-ously improved in the sector cascade channels of high pressure turbine guide vane. The experiment results can be used to evaluate the aerodynamic performance of guide vane.

介绍了采用金属注射成形(MIM)技术制造涡轮增压器HK30耐热不锈钢零件的工艺,包括喂料制备、注射、脱脂和烧结工艺。对采用不同工艺参数制备的HK30材料进行了力学性能测试,对材料的微观组织进行了分析,并将此工艺成功应用于可变截面涡轮增压器喷嘴环用耐热不锈钢导向叶片的制造,对批量生产状况下的工艺稳定性进行了研究。

The manufacturing process of HK30 heat-resisting stainless steel turbocharger with the metal injection molding (MIM) technology was introduced including feedstock preparation ,injection molding ,debinding and sintering .The mechanical properties of HK30 specimens prepared according to different technology parameters were tested and the corresponding micro-structures were analyzed .The developed MIM technology was successfully applied to the guide vane manufacturing of nozzle ring for the variable geometry turbocharger (VGT) .The stability of MIM technology in mass production was further re-searched .

目前航空发动机维修正从原来的定时维修向视情维修转换，而视情维修的基础则是准确预测发动机部件或整机的损伤。传统基于有限元理论的损伤预测仅能对标称条件下部件损伤进行精确预测，在因外部环境改变、噪声等因素引起的非标称条件下，则难以保证部件损伤预测精度，同时其分析过程复杂、工作量大，不利于机载实时运行。以某型涡扇发动机涡轮导向叶片的热机械疲劳损伤为例，建立发动机运行条件和叶片损伤之间的神经网络预测模型，并利用Monte Carlo仿真提高模型的预测精度。仿真结果显示，根据下一循环的飞行条件，叶片损伤预测结果相对误差在0.4%以下，且该模型可以应用于机载实时预测。

Aircraft engine maintenance is changing from original timing maintenance to condition-based maintenance, which is based on accurate estimate of the damage to the engine parts or the whole engine. Traditional damage prediction based on finite element theory only accurately predicts damage of the parts under nominal conditions, but it is difficult to guarantee the prediction accuracy of the component damage under other non-nominal conditions caused due to the change of external environment, noise. And its complex analysis process is not conducive to real-time operation of airborne. The paper takes thermo-mechanical damage of turbofan engine guide vane for example, establishes the neural network predic-tion model between the engine operating condition and vane damage, and then improves the prediction accuracy of the model using Monte Carlo simulation. The simulation results show that the error of predicted TMF damage is bellow 0.4%. And the model can be used to real-time predict