Multi-Objective Optimization Design of Adaptive Cycle Engine with Serpentine 2-D Exhaust System Based on Infrared Stealth

Abstract

In the overall design process of the turbofan engine, it has become crucial to address the challenge of selecting design parameters that not only meet the flight thrust demand but also enhance engine economy. As the demand for stealth performance in future fighter aircraft increases, it becomes imperative to consider infrared stealth indicators during the design process. The adaptive cycle engine possesses an adjustable thermal cycle, necessitating careful attention to the selection of design parameters to fulfill the requirements. Therefore, this paper proposes a multi-objective optimization design method for the adaptive cycle engine that integrates infrared stealth technology. Initially, the parameter cycle model of the adaptive cycle engine is established based on the principles of aerodynamic and thermodynamic calculations. Subsequently, the model incorporates a serpentine two-dimensional (2-D) exhaust system to achieve infrared suppression. Meanwhile, a method for predicting the infrared characteristics is proposed to calculate the infrared radiation intensity of the engine exhaust system. Finally, the sequential quadratic programming algorithm is applied to comprehensively optimize the engine’s performance. The simulation results reveal that the multi-objective optimization design can effectively select appropriate design parameters to im-prove the engine, thereby reducing fuel consumption while meeting thrust requirements. This approach combines the consideration of infrared stealth technology with the optimization of engine performance, thus contributing to the development of advanced adaptive cycle engines.

Interdisciplinary Studies of Jet Systems using Euler Methodology and Computational Fluid Dynamics Technologies

“…Ejectors are widely applied in mechanical engineering, aerospace, and power engineering [28][29][30], including carbonfree technologies [31,32]. Bencharif et al [33] and Chen et al [34] used AI for optimal ejector design. Yan et al [35] and Zhang et al [36] investigated multiflow ejectors of different structures.…”