Fast uncertainty assessment of in-service thrust control for turbofan engines: An equivalent model using Taylor expansion

Zhiyuan Wei, Shuguang Zhang and Shuiting Ding

Energy, 2024, vol. 308, issue C

Abstract: An equivalent model using Taylor expansion (TEEM) is proposed for a fleet of engines regulated by the industrial baseline controller, aiming at the fast assessment of in-service thrust control under gas path and measurement uncertainties in advance. TEEM is fulfilled initially by the equivalent transformation of measurement uncertainties into engine set-point distribution, followed by a first-order Taylor expansion on the steady-state aero-thermal engine model along the average degradation curve of engine fleets. Simulations are conducted at take-off states on a validated aero-thermal turbofan engine model with publicly available uncertainty statistics on a desktop computer. Results show that the equivalent transformation is successful for both new and degraded engine fleets. Moreover, TEEM can estimate the mean and standard deviation values of key engine control parameters including thrust with the maximum errors of 0.04 % and 3.85 % for new engine fleets, 0.04 % and 6.25 % for degraded engine fleets, respectively, compared to the classic sample-based Monte-Carlo simulations (MCS). Computational time for TEEM and MCS for the tested life cycle are 3,018,564s and 137.52s accordingly, which means 99.995 % simulation time reduction is achieved by TEEM. Hence, the accuracy and efficiency of the proposed model for uncertainty assessment of thrust control of turbofan engines are confirmed.

Keywords: Gas turbine engines; Thrust control; Fast uncertainty quantification; Gas path degradation; Manufacture tolerance; Measurement uncertainties (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224025763
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:308:y:2024:i:c:s0360544224025763

DOI: 10.1016/j.energy.2024.132802

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 ().