| Citation: | REN Likun, XIE Jing, QIN Haiqin, et al. A thermodynamics-based neural network modeling approach for turbofan engines[J]. Journal of Aerospace Power, 2023, 38（7）：1571-1582 doi: 10.13224/j.cnki.jasp.20220726
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Owing to the inaccuracies of the component characteristic maps, the traditional thermodynamic model for on-wing turbofans exhibited a significant modeling error. Moreover, when the model was iterated near the boundary points of the maps, it was prone to non-convergence, rendering it unreliable. To address these issues, a neural network modeling method for turbofans based on thermodynamic was put forward. This method improved modeling accuracy by fully considering the optimization of thermodynamic constraints during the training process of neural network models. By constructing a component-level network structure, implementing a components-cooperating loss function, and applying a fusion training process, the traditional iterative process of the thermodynamic model was transformed based on the component characteristic maps into a multi-objective optimization and training process of the component-level neural network. This approach improved the convergence and modeling accuracy of the model. The model was trained and tested using 26970 actual engine flight data. The results demonstrated that the maximum error of the proposed modeling method was approximately 7%, even under loose quasi-steady-state data, which was about 5% lower than that of the thermodynamic model based on the characteristic maps.
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