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

作     者：Chen WANG Ziyang YU Xian DU Ximing SUN Chen WANG;Ziyang YU;Xian DU;Ximing SUN

作者机构：School of Control Science and EngineeringDalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial EquipmentMinistry of EducationDalian University of TechnologyDalian 116024China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2024年第37卷第8期

页      面：190-207页

核心收录：

学科分类：08[工学] 082503[工学-航空宇航制造工程] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 080201[工学-机械制造及其自动化] 

基　　金：co-supported by the National Natural Science Foundation of China(Nos.61890921,61890924) the National Science and Technology Major Project,China(No.J2019-1-0019-0018) 

主　　题：Aero-propulsion system Integrated inlet-enginenozzle Component-level model On-board adaptive model Mechanism-data fusion Extreme learning machine 

摘      要：Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion *** 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 *** paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental ***,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is *** 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 *** model’s output closely aligns with typical experimental *** combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight ***,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location *** approach significantly curbs performance deviations in supersonic *** example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%*** findings underscore the clear superiority of the proposed method.