To investigate the performance potential of the aero-propulsion system and the problem of control mode conversion, this paper takes the inlet/engine integrated component-level model as the research object, and a performance-seeking control (PSC) scheme based on the neighborhood-based speciation differential evolution–grey wolf optimization (NSDE-GWO) algorithm is designed and combined with an active disturbance rejection control (ADRC) to establish a multivariable fusion closed-loop control system. The analysis reveals that the NSDE-GWO hybrid algorithm, which takes advantages of the two algorithms, significantly improves the computing efficiency and optimization accuracy, achieving better optimization solutions in three different modes. The intelligent fusion controller is able to achieve a smooth transition of performance modes to ensure that the engine is provided with a stable thrust during operation under supersonic conditions, and the potential for performance optimization is maintained at a reasonable level. Maximum installed thrust mode capable of achieving no thrust loss and a maximum fluctuation rate within 2000 N/s, with the largest variation in thrust during the conversion being less than 0.9% under the minimum turbine temperature mode and the minimum specific fuel consumption mode. This study presents a theoretical foundation and engineering applications for the design of supersonic propulsion system controllers.

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Advanced aero-engine component-level models are characterized by strong nonlinearity and multivariate, and traditional iterative algorithms cannot meet the requirements of convergence, real-time, and accuracy at the same time. To improve the convergence and alleviate the initial value dependence, a hybrid damped Newton algorithm based on the neighborhood based speciation differential evolution (NSDE) is proposed in this paper for solving the aero-engine component-level model. The computational efficiency and convergence of the hybrid damped Newton algorithm and NSDE hybrid damped Newton algorithm under four typical steady-state operating point conditions are analyzed, and then, the accuracy of the model is verified. It is demonstrated that the hybrid damped Newton method has the advantage of low initial value sensitivity and high computational efficiency under large deviation conditions. The hybrid damped Newton method is more efficient than the Broyden algorithm in terms of iterative efficiency, faster than the traditional N-R algorithm in terms of computation speed, and has the highest computational convergence rate under the four typical operating conditions, but it cannot eliminate the initial value dependence. The NSDE hybrid damped Newton method offers high simulation accuracy and greatly increases computational real-time performance under large deviation conditions, and the maximum error between the numerical simulation results and the experimental reference value is 8.1%. This study provides advanced theoretical support for component-level modelling and has certain engineering application value.

With the advancement of the supersonic aero propulsion system, optimizing the combined performance of inlet/engine integration has become increasingly crucial. To solve the coupling inlet/engine problem, a quasi-one-dimensional inlet modeling and drag calculation method are proposed, integrated performance seeking control (PSC) based on the neighborhood-based speciation differential evolution-grey wolf optimizer (NSDE-GWO) is presented and quantitatively analyses the influence of variable geometry inlet regulation on performance. The results reveal that the optimization effect of the ramp angle adjustment is generally better than that of the bleed adjustment, and the NSDE-GWO hybrid algorithm achieves remarkable optimization solutions in all three different modes. The PSC with variable geometry inlet adjustment provides more additional potential for optimization compared with fixed geometry inlet, and the performance can be maximized by adjusting both the bleed adjustment and the ramp angle. This study maximizes the exploitation of potential and has theoretical guidance and practical engineering significance.