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A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines
| Content Provider | MDPI |
|---|---|
| Author | Ren, Likun Qin, Haiqin Xie, Zhenbo Xie, Jing Li, Bianjiang |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 6373 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su14106373 |
| Journal | Sustainability |
| Issue Number | 10 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-23 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Aero-engine Modeling Hybrid Model Neural Network Flight Data Evaluation |
| Content Type | Text |
| Resource Type | Article |
