首页|An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanism-data fusion

An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanism-data fusion

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Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model's efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model's output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanism-data fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Nota-bly,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17％to merely 6.54％,while errors for other flight conditions consis-tently stay below the 2.95％threshold.These findings underscore the clear superiority of the pro-posed method.

Aero-propulsion systemIntegrated inlet-engine-nozzleComponent-level modelOn-board adaptive modelMechanism-data fusionExtreme learning machine

Chen WANG、Ziyang YU、Xian DU、Ximing SUN

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School of Control Science and Engineering,Dalian University of Technology,Dalian 116024,China

Key Laboratory of Intelligent Control and Optimization for Industrial Equipment,Ministry of Education,Dalian University of Technology,Dalian 116024,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Science and Technology Major Project,China

6189092161890924J2019-1-0019-0018

2024

10.1016/j.cja.2024.03.037

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(8)
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