Design and Structures of Aircraft Engines

Nagappa, Rajaram; Jeyaraman, Sankarkumar; Kumar, Ch. Ravi

doi:10.1007/978-981-10-2143-5_14

Cited by 1 publication

(1 citation statement)

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“…Aero-engine thermodynamic models are widely used in engine design [1], development [2] and monitoring [3]. However, in the field of military turbofans, thermodynamic models suffer from serious non-convergence [4,5], mainly due to the following reasons.…”

Section: Introductionmentioning

confidence: 99%

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

Ren¹,

Qin²,

Xie³

et al. 2022

Sustainability

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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.

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