A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss
Xinhai Zhang,
Kang Wang,
Jia Geng,
Ming Li and
Zhiping Song
Energy, 2024, vol. 294, issue C
Abstract:
The acceleration performance of turbofan engine is mainly limited by the surge boundary of high-pressure compressor (HPC). A certain amount of surge margin (SM) is reserved between the boundary and the acceleration schedule (AS) of acceleration control to avoid the risk caused by intake distortion, etc. However, the performance improvement by low SM design is the requirement for advanced engines. Hence it must execute a fault-tolerant strategy for the impact of sensor error, fuel metering error, etc. This paper proposes a method by adopting four ASs for weighted average to replace the single AS. The weights are altered by the multi-layer perceptron with reference to the real-time fuel results from four ASs, intake condition and rotor speed. For safety and accuracy, an exponential Gumbel loss function is introduced into the model training. The simulation results demonstrate that the method can significantly tolerate the impact of a single fault and the normal deviations of other factors without causing SM of HPC to be less than 3.6%. These also indicate that the acceleration time on the ground does not exceed acceptable 5 s, with a median that is 0.34 s less than the minimum method of two ASs within the flight envelope.
Keywords: Acceleration schedule; Exponential Gumbel loss; Fault-tolerant; Multi-layer perceptron; Surge margin; Turbofan (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224006455
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006455
DOI: 10.1016/j.energy.2024.130873
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().