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Stochastic response analysis and robust optimization of nonlinear turbofan engine system

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Abstract

The quantitative analysis of the uncertainty influence on thermodynamic parameters and performance parameters is usually difficult to achieve due to the strong nonlinear working process in engine systems. It is very important to fully consider the uncertainty influences in the performance design for tapping the working potential. Therefore, this paper establishes a stochastic dynamic model based on the nonlinear characteristics of engine systems, analyzes the time-varying influence law of uncertainty on the responses of thermodynamic parameters and ascertains the stochastic response distribution features of thermodynamic parameters and performance parameters. Based on the quantitative analysis results, an optimization method considering stochastic responses is further established to design the performance parameters of engine systems and compared with the traditional mechanism model and Monte Carlo simulation. The results indicate that the thermodynamic parameters and performance parameters all show the normal distributions, and the fuel system actuation uncertainty has a greater impact on the stochastic responses. The robust optimization method significantly reduces the uncertainty influence and improves the thermodynamics performance with an 84.4% probability. Moreover, it reduces the computational cost by more than half. This work provides an effective method for the quantitative analysis of the uncertainty influence in engine systems and provides a useful reference for the robust performance design of new type engine.

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Acknowledgements

This work is supported by the Science and Technology Department of Ningxia (Grant No. 2022ZDYF1483) and Chinese-German Center for Research Promotion (Grant No. GZ1577).

Funding

This work is funded by the Science and Technology Department of Ningxia (Grant No.1015 2022ZDYF1483) and Chinese-German Center for Research Promotion (Grant No. GZ1577).

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  1. The Key Laboratory of Power Machinery and Engineering of Education Ministry, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Dengji Zhou & Dawen Huang

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Zhou, D., Huang, D. Stochastic response analysis and robust optimization of nonlinear turbofan engine system. Nonlinear Dyn 110, 2225–2245 (2022). https://doi.org/10.1007/s11071-022-07752-5

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