Aero-engine dynamic model based on an improved compact propulsion system dynamic model

Author(s):  
Qiangang Zheng ◽  
Yong Wang ◽  
Chongwen Jin ◽  
Haibo Zhang

The modern advanced aero-engine control methods are onboard dynamic model–based algorithms. In this article, a novel aero-engine dynamic modeling method based on improved compact propulsion system dynamic model is proposed. The aero-engine model is divided into inlet, core engine, surge margin and nozzle models for establishing sub-model in the compact propulsion system dynamic model. The model of core engine is state variable model. The models of inlet, surge margin and nozzle are nonlinear models which are similar to the component level model. A new scheduling scheme for basepoint control vector, basepoint state vector and basepoint output vector which considers the change of engine total inlet temperature is proposed to improve engine model accuracy especially the steady. The online feedback correction of measurable parameters is adopted to improve the steady and dynamic accuracy of model. The modeling errors of improved compact propulsion system dynamic model remain unchanged when engine total inlet temperature of different conditions are the same or changes small. The model accuracy of compact propulsion system dynamic model, especially the measurable parameters, is improved by online feedback correction. Moreover, the real-time performance of compact propulsion system dynamic model and improved compact propulsion system dynamic model are much better than component level model.

Author(s):  
Juan Fang ◽  
Qiangang Zheng ◽  
Haibo Zhang

The on-board dynamic model is the basis of numerous advanced control technologies for modern aero-engine, and its accuracy and real-time performance are two crucial indicators. Since the simulation accuracy declines with the deviation from the ground point of the conventional compact propulsion system dynamic model (CPSDM), an improved CPSDM (ICPSDM) based on deep neural network is proposed in this paper. First, the K-means algorithm is utilized to get the sampling point, and the engine simulation data is collected in the cluster centers. Then, the batch normalize deep neural network (BN-DNN) is applied to build the ICPSDM, which can shorten the training time tremendously. The simulation results show that, in the same simulation environment, the accuracy of turbine inlet temperature T 4 , fan surge margin SMc, thrust F, and specific fuel consumption SFC calculated by ICPSDM are 11.04, 3.17, 1.89, and 1.98 times of CPSDM, respectively, at off-design point, and the real-time performance of ICPSDM is more than 30 times faster than the component-level model.


2021 ◽  
Vol 301 ◽  
pp. 126901
Author(s):  
Tsai-Chi Kuo ◽  
Ni-Ying Hsu ◽  
Reza Wattimena ◽  
I-Hsuan Hong ◽  
Chin-Jung Chao ◽  
...  

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