This paper proposes a controller design for the electric pump of a deep-throttling rocket engine. The nonlinearity of the system is taken into consideration by analyzing the gap metric. Then, proportionalintegral-derivative controller and gain-scheduling linear quadratic regulator are designed. Analyzing the amplitude-and phase-frequency characteristics as well as the pole-zero distribution of the system, the results show that the designed controllers can stabilize the linearized equations in incremental form at different operating points. This indicates that these two controllers are available for the original system in the whole range of working conditions and this is verified in the simulation. Meanwhile, the comparison between proportional-integral-derivative controller and gain-scheduling linear quadratic regulator is presented. It demonstrates that the proportional-integral-derivative controller is better at tracking both step and ramp signals but with worse control signals. It means that the proportionalintegral-derivative controller seems less suitable for real use due to severe oscillations. Meanwhile, the parameter tuning of a proportional-integral-derivative controller depends on more extensive manual tuning. Therefore, the gain-scheduling linear quadratic regulator is preferred.

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The bolted flange joint is a kind of widely used joint structure in the rotor system. Its discontinuous mechanical characteristics result from the existing of the contact surface, which will slide and deform when the spool deforms. As a consequence, the joint’s stiffness is always smaller than that of fixed configuration, which affects rotor’s stiffness distribution and the rotor’s dynamics further. The objective of this study is to investigate the mechanical characteristics of the bolted flange joint, the affecting factors and the influence on rotor’s dynamics. According to the characteristics of structure and mechanical state, using the existing equivalent axial spring-bending beam model to describe the tension and compression stiffness of bolted flange joint section, then the bending stiffness model of whole bolted flange joint is established based on that. The results show that there is a significant effect of the bolted flange joint on the local stiffness of the rotor, the loss of local bending stiffness reach a high level when the number of bolts is few. The mathematical description between stiffness loss and structure size, load and assembling condition is obtained through the analytical results. A bolted flange joint simulation model, taking the characteristics of the contact into account, is built by the nonlinear finite element method. The trends of numerical results agree with the analytical conclusion, and show the stiffness of bolted flange joint is smaller than that of the fixed configuration. The stiffness of bolted flange joint decreases a small amount with the increasing moment. When the number and the pretension force increases, the stiffness increases nonlinearly. Based on the mechanism of stiffness loss, the equivalent stiffness is used to replace the fixed configuration stiffness on the location of bolts in finite element model of high pressure rotor system. The results of dynamic analysis shows that the stiffness loss has a greater impact on bending modes than the rigid modes while the static analysis shows that the stiffness loss has a small negatively effect on clearances. The study shows that, the stiffness loss of bolted flange joint has a close relationship with the load and assembling conditions. The results show the effectiveness in controlling the mechanical and dynamic properties of the rotor with bolted flange joints by careful adjusting of structural parameter, load parameter and assembling parameter during designing.

Liquid rocket engines (LREs) are the main propulsive devices of launch vehicles. Due to the complex structures and extreme working environments, LREs are also the components prone to failure. It is of great engineering significance to develop fault detection technologies which can detect fault symptoms in time and provide criteria for further fault diagnosis and control measures to avoid serious consequences during both the ground tests and flight missions. This paper presents a novel fault detection method based on convolutional auto-encoder (CAE) and one-class support vector machine (OCSVM) for the steady-state process of LREs. We train the CAEs by normal ground hot-fire test data of a certain type of large LRE for automatic feature extraction. Then the obtained features are used to train the OCSVMs to accomplish the fault detection task. The results demonstrate that the proposed method outperforms traditional redline system (RS), adaptive threshold algorithm (ATA), and back-propagation neural network (BPNN). We also study the effect of sample sizes and domain knowledge on the performance of the proposed method. The results suggest that appropriate measures that enrich the effective information content in the training data, such as increasing sample size and introducing domain knowledge, can further improve the performance of the proposed fault detection method.

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