Improved hybrid modeling method with input and output self-tuning for gas turbine engine

Maojun Xu, Jinxin Liu, Ming Li, Jia Geng, Yun Wu and Zhiping Song

Energy, 2022, vol. 238, issue PA

Abstract: Gas-path model (GPM) plays a key role in the application of sensor fault tolerant control for the gas turbine engine (GTE). However, the modeling accuracy of traditional physics-based GPM is restricted by modeling inaccuracies and measurement uncertainty of sensors, etc., making fault-tolerant control of sensors difficult. In this paper, an improved input and output self-tuning hybrid modeling (IOSTHM) method is proposed for improving the GPM modeling accuracy of a dual shaft turbofan GTE. The proposed IOSTHM consists of an input self-tuning model (ISTM) and an output self-tuning model (OSTM). In the framework of ISTM, a fundamental traditional physics-based model (PBM) is constructed by using the component level modeling method firstly. Based on the PBM and the actual measurement of rotor speed of low-pressure shaft (N1) which has high measurement accuracy, the ISTM is conducted by tuning the measurement of fuel flow in real-time according to the deviation of N1 between engine and PBM, aiming to deal with the measurement uncertainty of fuel flow. Based on the ISTM, the IOSTHM is constructed by combining with an OSTM which is constituted by a bank of residual learning models to reduce the modeling inaccuracies further. The effectiveness of the proposed IOSTHM is evaluated with the verification of simulated flight data and actual ground test data. Both flight and ground verification results reveal that the proposed hybrid model IOSTHM achieves the best modeling performance when compared with other models such as traditional PBM, ISTM, and OSTM, it shows the superiority of this methodology.

Keywords: Gas turbine engine; Hybrid model; Physics-based model; Self-tuning; Extreme learning machine (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221019204
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:238:y:2022:i:pa:s0360544221019204

DOI: 10.1016/j.energy.2021.121672

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 ().