基于改进无模型自适应算法的涡扇发动机限制保护控制方法

针对发动机限制保护控制问题,提出了一种基于数据驱动的改进无模型自适应限制保护控制方法。以主回路闭环系统为设计对象,通过在常规无模型自适应控制律的准则函数中增加误差变化率,使超限保护系统具有更快的响应速度,给出了改进后系统的稳定性条件,并采用临界比例度法进行了控制器参数整定。仿真结果表明:在不同工作点,所设计的改进无模型自适应限制保护控制器相比常规无模型自适应限制保护控制器,最大超限平均减小2.97%,响应时间平均缩短2.30s,实现了发动机超限后的迅速回调,保证了发动机系统的安全运行。     

基于高速慢车的航空发动机快速响应控制

针对传统航空发动机响应速度慢,难以在紧急事件中用于控制受损飞机完成起降过程的问题,采用高速慢车控制模式来提升发动机加速性能,通过增加发动机在慢车时高压压气机转速,为加速前期提供更大的燃油流量,从而缩短发动机从慢车至最大状态的加速时间。为保证慢车时高压转子转速提高的同时发动机推力和稳定裕度不变,通过修改高压压气机可调导叶控制计划来调整高压转子工作点。仿真结果显示,与原有控制相比,采用高速慢车快速响应控制模式的发动机加速上升时间从原来的2.00s缩短至1.86s,而高压压气机最小喘振裕度仅由16.01%下降至14.81%,同时慢车推力基本保持不变。     

Fault diagnosis based on measurement reconstruction of HPT exit pressure for turbofan engine

Aero-engine gas path health monitoring plays a critical role in Engine Health Management(EHM). To achieve unbiased estimation, traditional filtering methods have strict requirements on measurement parameters which sometimes cannot be measured in engineering. The most typical one is the High-Pressure Turbine(HPT) exit pressure, which is vital to distinguishing failure modes between different turbines. For the case of an abrupt failure occurring in a single turbine component, a model-based sensor measurement reconstruction method is proposed in this paper. First,to estimate the missing measurements, the forward algorithm and the backward algorithm are developed based on corresponding component models according to the failure hypotheses. Then,a new fault diagnosis logic is designed and the traditional nonlinear filter is improved by adding the measurement estimation module and the health parameter correction module, which uses the reconstructed measurement to complete the health parameters estimation. Simulation results show that the proposed method can well restore the desired measurement and the estimated measurement can be used in the turbofan engine gas path diagnosis. Compared with the diagnosis under the condition of missing sensors, this method can distinguish between different failure modes, quantify the variations of health parameters, and achieve good performance at multiple operating points in the flight envelope.     

涡扇发动机全包线加速控制计划改进方法研究

加速控制计划直接影响了发动机的响应速度以及运行安全。为了提高发动机响应能力,提出了一种基于等温度线的发动机全包线加速控制计划。分别针对稳态和动态过程开展相似换算误差分析,证明并验证了关键参数在等风扇进口温度时,具有较高相似换算精度的规律。基于此换算误差理论,提出全包线加速控制计划改进方法,该方法在不同等风扇进口温度下设计多条加速控制计划,再通过线性插值得到包线内不同等温线下的加速控制计划。结果表明,改进后的加速控制计划相比于传统单点优化得到的加速控制计划,发动机加速至最大转速的98%所需的时间减少了7.2%,最大转速提升了1%,且风扇、压气机喘振裕度和涡轮前温度等均未超出限制值。因此,该方法相比于传统单点优化方法既提升了在包线内获取的加速控制计划的精度,又确保了发动机在包线内安全稳定工作的前提下更好的发挥加速性能。     

基于显式预测控制的涡扇发动机控制器设计

模型预测控制因其能够较好地处理涡扇发动机约束问题且实现多变量控制而具有较大的应用潜力。为了解决传统模型预测控制算法在实际应用中存在运算量大、实时性较差的问题,基于显式模型预测设计了航空发动机多变量控制器。在控制结构上采用多速率双闭环系统,保证了控制精度;在计算上通过多参数规划将在线优化问题转化为线性函数计算问题,大幅度减少了计算量;在发动机从开环切闭环的过程提出一种增量式切换方法,实现无扰切换。数值仿真和硬件在环仿真结果表明,转速和压比稳态误差分别不超过±0.25%和±1%,控制器在25ms控制周期内能完成计算,满足嵌入式系统实时性要求。     

涡扇发动机气路故障定量诊断的BP网络研究

为了克服ＢＰ算法收敛速度慢的问题,提出了一种基于混合学习规则的ＢＰ算法,并采用模归一化方法,成功地定量组织了故障的学习样本,建立了能够定量分析发动机气路部件故障的人工神经网络（ＢＰＮ）。通过分析测量系统随机误差的影响和实际试车数据的效验结果,表明该网络具有较强的推广能力及适应性,能基本满足故障定量诊断的要求,并具有较好的工程实用性。     

求解涡扇发动机数学模型的有限域搜索方法

提出一种基于N-R迭代方法的有限域优化搜索法,用于求解涡扇发动机数学模型。该方法不仅具有N-R优化迭代求解特色,还具有将初猜解附近划定域进行泛搜能力,使迭代运算具有强收敛性和平稳性。该方法所定义的迭代因子可通过编程自动调整,进而可以确定最小迭代次数及此意义下的最优迭代因子。      

涡扇发动机内外函回热分析

对吴仲华教授提出的航空涡扇发动机改为陆用输出轴功的一种最安全可靠办法-将外函空气由内函动力透平排气回热后也输出轴功,进行了基本的热力循环分析。按初步分析可知,此法宜用于函道比接近于1的总压比不太高的机组。按目前技术水平,采用此措施可以达到比“切顶”相对提高效率与功率10%左右。在我国有一定的应用前景。      

某型涡扇发动机试车台测试系统研制

结合某型高推重比涡扇发动机试车合测试系统的研制,介绍了该系统的技术指标、数据采集技术、显示技术和试车仿真原理。为提高系统测量精度和使用性能,提出并应用了测量参数非线性修正、多模式显示、实时发动机参数整理等技术。     

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.