A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines

Likun Ren, Haiqin Qin, Zhenbo Xie, Jing Xie and Bianjiang Li
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Likun Ren: Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China
Haiqin Qin: Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China
Zhenbo Xie: Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China
Jing Xie: Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China
Bianjiang Li: Department of Mechanical Engineering, Qingdao Campus, Naval Aviation University, Qingdao 266041, China

Sustainability, 2022, vol. 14, issue 10, 1-15

Abstract: Traditional thermodynamic models for military turbofans suffer from non-convergence and inaccuracy due to inaccuracy of the component maps and the instability of the iterative process. To address these problems, a thermodynamically oriented and neural network-based hybrid model for military turbofans is proposed. Different from iteration-based thermodynamic models, the proposed hybrid model transforms the iteration process into a multi-objective optimization and training process for a component-level neural network in order to improve convergence and modeling accuracy. The experiment shows that the accuracy of the proposed hybrid model can reach about 7%, 5% better than the map-fitting-based thermodynamic model and 8% better than the purely data-driven method, with a similar number of network neutrons, verifying its effectiveness. The contributions of this work mainly lie in the following aspects: a new component-level neural network structure is proposed to improve convergence and computational efficiency; a multi-objective loss function based on component co-working is proposed to direct the model to converge toward the physical thermodynamic process; a fusion training method of multiple data sources is established to train the model with good convergence and high computational accuracy.

Keywords: aero-engine modeling; hybrid model; neural network; flight data evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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