Transient Analysis for Hypersonic Inlet Accelerative Restarting Process

Su, Weiyi; Hu, Zhiwei; Tang, Piao-Ping; Chen, Yun

doi:10.2514/1.a33601

Cited by 11 publications

(1 citation statement)

References 24 publications

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“…As the key component, the inlet directly affects the engine operating conditions. It is well known that the unstart phenomenon is one of the most important issues of the hypersonic inlet, which should be prevented [10] or taken effective measures to realize the restart when falling into the unstart state [11][12][13]. However, once the inlet unstart occurs during flight, it will increase the working burden of the hypersonic engine, and the hysteresis phenomena in the restarting process of the hypersonic inlet are widespread [14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Backpressure Unstart Prediction and Warning Method for Combined Cycle Engine Hypersonic Inlet-Oriented Wide Speed Range

Min,

Hong,

Cai

et al. 2024

International Journal of Aerospace Engineering

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Inlet unstart prediction and warning are strictly crucial to the operation of hypersonic engines, especially for combined cycle engines where implementation across a wide speed range poses significant challenges. This paper proposes a realization method that involves constructing the conditions of critical backpressure ratios for the inlet unstart and unstart warning states within a wide speed range and establishing the backpressure prediction models for each engine mode. The detection of the unstart and unstart warning states is achieved by predicting the backpressure ratio at the exit of the isolator and comparing it to the critical backpressure ratios. To achieve this, numerical simulations for a three-dimensional inward-turning multiducted hypersonic combined inlet at various Mach numbers and backpressure ratios are carried out to obtain the dataset of surface pressure. A 10-fold cross-validation support vector machine (10-CV SVM) is used to solve the unstart boundary of surface pressure, and an unstart margin is set to determine the unstart warning boundary. A back propagation (BP) neural network is constructed to estimate the critical backpressure ratios at each working point within a wide speed range. The data information of surface pressure on the boundaries is used as the input for the predictions. The overall average regression correlation coefficient approaches 0.99 on the test dataset at each working point. The backpressure prediction models are established by the one-dimensional convolutional neural network (1D-CNN). Only 2 to 4 measurement points of surface pressure are considered for cross-validation evaluation, and the mean absolute percentage error is between 4% and 8% with the average prediction time not exceeding 2 ms. Finally, the proposed method and prediction models are validated by wind tunnel experimental data.

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