堵塞条件下发动机高空试验推力差异分析

堵塞技术是降低发动机高空模拟试验成本和解决设备抽气能力不足的可行手段。在不同喷管排气环境压力条件下,某型带拉瓦尔喷管涡扇发动机完成了四组堵塞对推力影响的试验,发动机总推力试验值最大差异为6.2%。为揭示试验推力差异产生的原因,提高堵塞条件下发动机高空试验推力评估的准确性,开展了相关的理论分析和一维仿真计算,提出了主要影响量估算方法。结果表明：推力差异主要与发动机转速、结果修正方法和喷管扩张段流场特性有关;按文中提出的主要影响量估算方法计算了这些因素对推力的影响量,扣除影响量后总推力的最大差异降至0.8%;相同转速下,总推力相似换算值随喷管排气压力的升高呈增大趋势,最大差异达到3.3%。     

带增压压气机的小型脉冲爆震涡轴发动机性能分析

为了解决小型涡轴发动机采用脉冲爆震燃烧室代替主燃烧室时,由于脉冲爆震燃烧室的增压作用,无法从压气机出口引气对燃气涡轮冷却、封严的问题,提出了一种带增压压气机的脉冲爆震涡轴发动机(PDTSE)构型,并建立了相应的性能分析方法。对常规涡轴发动机与带增压压气机的PDTSE性能进行对比研究,计算结果表明：与常规涡轴发动机相比,带增压压气机PDTSE性能具有较大优势;在整个设计域内,综合考虑性能指标和设计难度,在循环最优处,带增压压气机的PDTSE相比普通涡轴的热循环效率高14.5%,耗油率低9.7%,单位功率高27%。     

Experimental study of a controlled variable double-baffle distortion generator engine test rig

In order to explore the total-pressure distortion test assessment method for a turbofan engine, a Controlled Variable Double-Baffle Distortion Generator(CVDBDG) with a horizontal symmetry moving form was developed, which can adjust the steady-state and time–variant distortion separately in real time. The inlet total-pressure distortion test was conducted on an afterburner turbofan engine. The distortion parameters of CVDBDG and the instability characteristics of the engine were measured. The experimental data were modeled and analyzed by using back propagation artificial neural networks, and the work envelope of CVDBDG was obtained. Based on the analysis of the data on the engine's instability, the properties of CVDBDG used for the stability assessment were preliminarily evaluated. The results show that CVDBDG can simulate both steady-state and time–variant distortions simultaneously in a range determined by three envelopes.Under the condition of symmetric double baffles, a critical depth of insertion exists, beyond which the symmetric baffles will generate an asymmetric flow field. In the case of double baffles, compared to a single baffle, the engine exhibited different instability characteristics. Based on CVDBDG, it is expected that more efficient engine stability and durability assessment methods can be developed.     

涡扇发动机低压部件通流耦合计算

为了研究各部件匹配与过渡段几何参数对发动机总体性能的影响,针对某涡扇发动机风扇、低压涡轮、内外涵道、掺混段和尾喷管等部件,基于周向平均Navier-St okes通流控制方程,采用时间推进有限体积法进行了部件耦合计算求解,将沿程各部件性能计算参数与试验做了对比,结果表明:在设计状态下发动机沿程各截面计算特征参数与试验值最大误差不超过5.0%,说明该通流耦合计算方法能够快速预估发动机总体性能,完善各部件匹配关系,在发动机总体设计初期有一定工程应用价值。     

基于主成分分析法的地磁导航适配区域选择

地磁场信息量越大的区域,导航性能越好。借用数理统计和信息论中的概念,设定了多个评价地磁场信息特点的指标,并通过主成分分析法,排除了相关指标,得到综合指标值,客观、定量地分析了适配区的导航性能,从而选择最佳导航适配区。仿真表明,本文方法是一种选择地磁导航工作区的有效方法,选择的适配区导航误差小。     

基于正激波位置计算的进气道/发动机系统实时仿真模型

研究了超声速条件下,基于进气道内正激波位置计算的进气道/发动机系统综合仿真问题。提出了将超声速进气道分为内、外两部分独立计算的方案,外部通过斜激波计算公式获得进气道内部的边界条件,对进气道内部采用准一维CFD模型模拟背压对正激波位置的影响。相比二维CFD计算,在保证参数精度要求的前提下有效缩短了计算时间,提高了仿真实时性。进一步将进气道模型与发动机部件级模型匹配运行,构建了进气道与发动机综合实时仿真模型。进气道/发动机综合模型的仿真表明,其能够准确模拟进气道正激波位置对涡扇发动机的动稳态影响,当来流发生扰动时,通过喷管喉道面积和压缩部件导叶角的快速调节,能够抑制正激波位置的变化,并使推力下降量减少50%。      

基于mSDTFT的无速度传感器异步电机转速估计

采用Teager-Kaiser能量算子对定子电流进行解调,有效消除了基频频谱泄露对提取转子速度谐波的影响,然后分析了SDTFT对转速估计精度的影响,在此基础上提出一种新的mSDTFT频谱分析方法估计转速。该方法有效降低了计算开销,特别是可以只针对某些需要的谱线进行计算,提高了谱分析的灵活性和高效性。试验结果表明,所提方法在不同负载条件下能够有效提取转子速度谐波,且具有较高的转速估计精度。     

基于气路参数融合的涡扇发动机性能退化预测

针对单参数驱动的涡扇发动机性能退化预测精度不高的问题,提出了一种基于气路参数融合的涡扇发动机性能退化预测的方法。通过监测发动机性能退化过程中多源参数,采用专家经验和核主成分分析相结合的方法,进行发动机性能参数的选择和融合,从而构建健康参数。基于非线性Wiener过程构建涡扇发动机退化模型,采用极大似然方法求得发动机退化模型的离线参数估计值;由于不同发动机性能退化的差异性,基于贝叶斯更新理念对随机参数进行实时更新,可以实现对单台发动机的性能退化实时预测。通过实例验证,采用此方法在预测末端方均根误差为0.028 3,整体预测精度提升了54.5%,可以辅助指导维修决策。      

Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

In the manufacturing of thin wall components for aerospace industry, apart from the side wall contour error, the Remaining Bottom Thickness Error (RBTE) for the thin-wall pocket component (e.g. rocket shell) is of the same importance but overlooked in current research. If the RBTE reduces by 30%, the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component. Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation. This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing. In this paper, the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components, which enables accurate prediction and compensation of RBTE value for the thin wall components. The physics based model considers the primary root cause, in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness (AMRT) variation, for the RBTE formation. And to incorporate the dynamic and inherent coupling of the complicated manufacturing system, the multi-feature fusion and machine learning algorithm, i.e. kernel Principal Component Analysis (kPCA) and kernel Support Vector Regression (kSVR), are incorporated with the physics based model. Therefore, the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy. The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling, and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling, respectively.