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Deep Analysis of Modeling and Simulation Technology for Aircraft Engine Control System

As the "heart" of modern aircraft, the performance optimization and reliability improvement of aircraft engines rely on advanced control system design. This article explores the modeling method and optimization strategy of aircraft engine speed control system based on AMESim simulation platform.


1. Fundamentals of Modeling Technology

The core of modeling aircraft engine control systems lies in constructing accurate mathematical models, covering multiple physical fields such as mechanical, hydraulic, and electronic coupling. Taking the hydraulic mechanical speed control system as an example, it needs to be implemented through the following steps:


Component database construction: including oil pump parameters (such as K3=1.0, K4=1.0), engine dynamic parameters (TE=0.9s, KE=0.23), and sensor characteristics.

Component classification and modeling: System components are divided into mechanical and hydraulic categories, described using dynamic equations and fluid dynamics models, respectively.

System integration and validation: Build a closed-loop simulation model using AMESim, input unit step signals, and analyze steady-state errors and root locus diagrams.

2. Analysis of simulation results

Simulation experiments show that the dynamic performance of the engine is the worst under low-speed, high-altitude, and low-speed flight conditions. For example, improper fuel supply during ground starting tests of a certain type of engine can lead to exhaust temperature exceeding the limit. By adjusting the fuel supply curve (such as reducing the fuel volume by 6% on the original basis in Scheme 2), the starting time and temperature peak can be significantly reduced.


3. Optimization strategy and future direction

Current research is mostly focused on single rotor jet engines, while the new generation full authority digital engine controller (FADEC) has implemented complex control algorithms. In the future, it is necessary to further integrate deep learning algorithms to enhance adaptive control capabilities and expand into the field of multi rotor and variable cycle engines.


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