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Detection and Transient Dynamics Modeling of Experimental Hypersonic Inlet Unstart

机译:实验超音速入口Unstart检测和瞬态动力学建模

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During unstart, the rapid upstream propagation of a hypersonic engine's inlet-isolator shock system can be readily detected through pressure measurements. Specifically, the magnitude of the pressure readings suddenly and dramatically increases as soon as the leading edge of the shock system passes the measurement location. An online change detection algorithm can monitor the pressure time history at a given sensing location and determine when an abrupt pressure rise occurs. If this kind of information can be obtained at various sensing locations distributed throughout the inlet then a feedback control scheme has an improved basis upon which to make actuation decisions for preventing unstart. In this paper, a variety of change detection algorithms have been implemented and tested on multiple sources of experimental high-speed pressure transducer data. The performance of these algorithms is compared, and the suitability of each algorithm for the general unstart problem is discussed. Attempts to model the transient dynamics governing the unstart process have also been made through the use of system identification techniques. The result of these system identification efforts is a partially nonlinear dynamic model that describes shock motion through pressure signals. The process reveals the possibility of partitioning the nonlinear behaviors from the linear dynamics with relative ease. Related attempts are then made to create a model where the nonlinear portion has been pre-specified leaving only the linear portion to be determined by system identification. The modeling and identification process specific to the unstart data used is discussed and successful models are presented for both the full system identification and the partitioned model cases.
机译:在未启动期间,通过压力测量,可以容易地检测超音发动机的入口隔离器冲击系统的快速上游传播。具体地,一旦冲击系统的前沿通过测量位置,就突然跳过的压力读数的大小突然增加。在线更改检测算法可以监视给定感测位置处的压力时间历史,并确定何时发生突然的压力。如果可以在分布在整个入口处的各种感测位置可以获得这种信息,则反馈控制方案具有改进的基础,用于制作用于防止unstart的致动决策。在本文中,已经在实验高速压力传感器数据的多个来源上实施和测试了各种变化检测算法。比较了这些算法的性能,并讨论了每种算法对一般unstart问题的适用性。还通过使用系统识别技术来模拟管理UNSTART过程的瞬态动态的尝试。这些系统识别力的结果是部分非线性动态模型,其通过压力信号描述冲击运动。该过程揭示了与相对容易的线性动力学划分非线性行为的可能性。然后使相关的尝试创建一个模型,其中已经预先指定了非线性部分仅留下线性部分以通过系统识别确定。讨论了所使用的UNSTART数据的建模和识别过程,并为完整的系统识别和分区模型案例呈现成功模型。

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