Multi-mode acceleration optimization control for adaptive cycle engine based on variable geometry components

The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine (ACE). However, separating the processes of acceleration and mode transition will slow down the response speed of thrust. Therefore, this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition. Firstly, an ACE component model with inlet flow characteristics was established, and the performance before and after mode transition were analyzed. Secondly, the principle of ACE acceleration optimization was analyzed, and the FVABI (Front Variable Area Bypass Injector) and MSV (Mode Selection Valve) were adopted in the acceleration process. Finally, based on the Sequential Quadratic Programming (SQP) algorithm, considering the degradation effects of engine components, we optimize the acceleration control plan for fuel and variable geometry mechanisms. The simulation results show that at the subsonic cruise point, the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%, and accelerate the thrust response speed by 33.72%. When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%, the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%; within the flight envelope, the acceleration time is reduced by more than 25%, and the thrust response speed is increased by more than 20%.

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Publication Details

Journal
Chinese Journal of Aeronautics
Published
2025-03-04
DOI
https://doi.org/10.1016/j.cja.2025.103460
Citations
2
Primary Topic
Rocket and propulsion systems research
Type
article
Field-Weighted Citation Impact
9.99
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article

Multi-mode acceleration optimization control for adaptive cycle engine based on variable geometry components

Haoying Chen, Xuzheng Liu, Haibo Zhang, Yifan Wang
2 citations
Chinese Journal of Aeronautics
Rocket and propulsion systems research
9.99
article

Multi-mode acceleration optimization control for adaptive cycle engine based on variable geometry components

Haoying Chen, Xuzheng Liu, Haibo Zhang, Yifan Wang
article en
2 citations

Abstract

The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine (ACE). However, separating the processes of acceleration and mode transition will slow down the response speed of thrust. Therefore, this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition. Firstly, an ACE component model with inlet flow characteristics was established, and the performance before and after mode transition were analyzed. Secondly, the principle of ACE acceleration optimization was analyzed, and the FVABI (Front Variable Area Bypass Injector) and MSV (Mode Selection Valve) were adopted in the acceleration process. Finally, based on the Sequential Quadratic Programming (SQP) algorithm, considering the degradation effects of engine components, we optimize the acceleration control plan for fuel and variable geometry mechanisms. The simulation results show that at the subsonic cruise point, the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%, and accelerate the thrust response speed by 33.72%. When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%, the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%; within the flight envelope, the acceleration time is reduced by more than 25%, and the thrust response speed is increased by more than 20%.

Chinese Journal of AeronauticsVol. 38(9)
Nanjing University of Aeronautics and Astronautics (CN)
Affordable and clean energy
Openalex Percentile: Top 3%
Rocket and propulsion systems research
9.99
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