A high-dimensional and high-precision onboard modeling method for adaptive variable cycle engine
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Qiangang Zheng
Abstract
Adaptive variable cycle engines, capable of switching seamlessly between subsonic and supersonic modes, have become a central focus in propulsion research. Accurate and real-time onboard models are crucial for predicting performance in flight and supporting advanced functions such as control optimization, health monitoring, and fault-tolerant control. Yet, reconciling accuracy with computational efficiency remains challenging due to the engines’ inherent high dimensionality and strong nonlinearity. Here we present an NN-ΔNN-based modeling framework, where a neural network captures nonlinear dynamics, a ΔNN component represents high-dimensional features, and a Kalman filter enhances adaptability. Simulations show that this approach improves accuracy of key parameters by 0.17–1.6 times compared with NN-PSM across a wide flight envelope. It also achieves rapid thrust-tracking under single- and multi-component degradation within seconds, with low steady-state error, demonstrating strong adaptability and real-time capability.
Funding source: General Project of the National Natural Science Foundation of China
Award Identifier / Grant number: 52372389
Funding source: the National Science and Technology Major Project
Award Identifier / Grant number: J2022-I-0003-0003
Funding source: Aero Engine Corporation of China Industry-University Research Cooperation Project
Award Identifier / Grant number: HFZL2023CXY013
Funding source: Nanjing University of Aeronautics and Astronautics Forward Layout Research Special Key Cultivation Project
Award Identifier / Grant number: ILB240101A24
Acknowledgments
The authors would like to thank the institutions and project teams involved for their support during the development of this study, as well as the reviewers for their insightful comments that helped improve the manuscript.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This work was supported in part by the General Project of the National Natural Science Foundation of China (No. 52372389), in part by the National Science and Technology Major Project (J2022-I-0003-0003), in part by the Aero Engine Corporation of China industry-university research cooperation project (No. HFZL2023CXY013), and in part by the Nanjing University of Aeronautics and Astronautics Forward Layout Research Special Key Cultivation Project (ILB240101A24).
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Data availability: Not applicable.
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