When modeling an aeroengine control system, effects of fuel metering unit are neglected or oversimplified, which most probably leads to unreliable simulation or experimental results. In order to well understand influences of fuel metering unit on performance of an aeroengine and to enrich the component-level model of a turboshaft aeroengine, a stepping motor controlled fuel metering unit is examined by combining process-based modeling and object-oriented modeling and is employed in the component-level engine model. Performances of the engine with and without the fuel metering unit are compared by simulation. The component-level engine model with the fuel metering unit characteristics considered meets the simulation requirements on the steady precision and can describe dynamics of the engine more precisely. A semi-physical experimental verification is implemented to confirm the influence of the fuel metering unit. The results show that simulation using the component-level engine model with the fuel metering unit sub-model is in accordance with the experiment, and that comparing to the engine model without fuel metering unit, the model with a fuel metering unit can extend the performance parameter response dozens of milliseconds.
IminAMontazeri-GhM. Improvement of min-max limit protection in aircraft engine control. Aerosp Sci Technol2017; 68: 214–222.
2.
FuQWangHYanX. Evaluation of the aeroengine performance reliability based on generative adversarial networks and Weibull distribution. Proc IMechE, Part G: J Aerospace Engineering2019, pp. 233–233.
3.
Simon DL and Armstrong JB. Constructing an efficient self-tuning aircraft engine model for control and health management applications. In: The annual conference of the Prognostics and Health Management Society, PHM, Minneapolis, MN, 23–27 December 2012. Rochester, NY: Prognostics and Health Management Society (PHM).
4.
Connolly JW, Csank J and Chicatelli A. Model-based control of a nonlinear aircraft engine simulation using an optimal tuner kalman filter approach. In: 49th AIAA/ASME/SAE/ASEE joint propulsion conference, San Jose, CA, 14–17 July 2013. Reston, VA: American Institute of Aeronautics and Astronautics.
5.
LeibovR. Aircraft turbofan engine linear model with uncertain eigenvalues. IEEE Trans Automat Contr2002; 47: 1367–1369.
6.
QiuXJZhengWHTangYT, et al.The test verification design method based on rapid prototyping technology of aero-engine. Procedia Eng2015; 99: 981–990.
7.
Chapman JW, Lavelle TM and Litt JS. Practical techniques for modeling gas turbine engine performance. In: 52th AIAA/SAE/ASEE joint propulsion conference, Salt Lake City, UT, 25–27 July 2016. Reston, VA: American Institute of Aeronautics and Astronautics.
8.
Luis JY, Sebastian D, Luis ED, et al. Object-oriented dynamic modelling of gas turbines for CSP hybridization. In: EUROSIM 2016 & SIMS 2016, Oulu, Finland, 12–16 September 2016. NW Washington, DC: IEEE Computer Society.
9.
Bazazzade M, Shahriari A and Badihi H. Improved turbine engine hierarchical modeling and simulation based on engine fuel control system. In: 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Denver, Colorado, 2–5 August 2009. Reston, VA: American Institute of Aeronautics and Astronautics.
10.
LuJJHuangJQLuF. Sensor fault diagnosis for aero engine based on online sequential extreme learning machine with memory principle. Energies2017; 10: 39–39.
11.
CezaryGMateuszLJaroslawH. Electric propulsion concepts for an inverted joined wing airplane demonstrator. Energies2017; 6: 762–762.
12.
SujataMMadanMBhaumikSK. Investigation of failure in main fuel pump of an aeroengine. Eng Fai Anal2014; 42: 377–389.
13.
Hitoshi O, Noriko M, Manabu S, et al. A motor control design for the more electric aero engine fuel system. In: SAE 2011 AeroTech congress and exhibition, Toulouse, France, 18–21 October 2011. Warrendale, PA: SAE International.
14.
LyantsevODBreikinTVKulikovGG, et al.On-line performance optimisation of aero engine control system. Automatica2003; 39: 2115–2121.
15.
JafariSMontazeri-GhM. Evolutionary optimization for gain tuning of jet engine min-max fuel controller. J Propul Power2011; 27: 1015–1023.
16.
YinQNieHWeiX, et al.Aircraft electric anti-skid braking and combined direction control system using co-simulation and experimental methods. Proc IMechE, Part G: J Aerospace Engineering2020; 234: 173–191.
17.
Dub M, Bajer J and Stepanek M. Electronic starting control unit for small jet engine. In: International Conference on Military Technologies (ICMT), Brno, 9 July 2015. New York, NY: IEEE.
18.
Bharani ChandraKPGuptaNKAnanthkrishnanN. Modeling, simulation, and controller design for an air-breathing combustion system. J Propul Power2010; 26: 562–574.
19.
XieZWSuMWengSL. Extensible object model for gas turbine engine simulation. Appl Therm Eng2001; 21: 111–118.
20.
MoormannDMostermanPJLooyeG. Object-oriented computational model building of aircraft flight dynamics and systems. Aerosp Sci Technol1999; 3: 115–126.
21.
ClausRWEvansALLylteJK, et al.Numerical propulsion system simulation. Comput Syst Eng1991; 2: 357–364.
22.
AhmadianNKhosraviASarhadiP. Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models. Mech Syst Signal Proc2017; 92: 1–12.
23.
HaiboZChangkaiYGuoqiangC. Variable rotor speed control for an integrated helicopter/engine system. Proceedings of the Institution of Mechanical Engineers. Proc IMechE, Part G: J Aerospace Engineering2014; 228: 323–341.
24.
JawLC. Aircraft engine controls: design, system analysis, and health monitoring, Reston, VA: American Institute of Aeronautics & Astronautics, 2009.
25.
Kuert C and Jufer M. New method for dynamic modeling of hybrid stepping motors. In: Industry applications conference, IEEE, Pittsburgh, PA, USA, 2002. New York, NY: IEEE.
26.
ClarksonPJAcarnleyPP. Simplified approach to the dynamic modeling of variable-reluctance stepping motors. Inst Elect Eng Proc Elect Power Appl1989; 136: 1–1.
27.
MorarA. The modelling and simulation of bipolar hybrid stepping motor by MATLAB/Simulink. Proc Tech2015; 19: 576–583.
28.
KonowrockiRSzolcTPochankeA, et al.An influence of the stepping motor control and friction models on precise positioning of the complex mechanical system. Mech Syst Signal Process2016; 70: 397–413.
29.
AlomoushMIAlbatranSA. Simulink-based implementation of TCSC-operated single-phase induction motor as an educational tool. Comput Appl Eng Edu2011; 19: 514–524.
30.
WuXCGuoZFHuangYW. Modeling and simulation of three-phase reactive stepping motor based on MATLAB. Electric Technol2012; 1: 43–46.
31.
XiPPZhaoYPWangPX. Least squares support vector machine for class imbalance learning and their applications to fault detection of aircraft engine. Aerosp Sci Technol2019; 84: 56–74.
32.
PakmehrMFitzgeraldNFeronE. Physics-based dynamic modeling of a turboshaft engine driving a variable pitch propeller. J Propul Power2016; 32: 1–13.
33.
LuYMaX. Active control of multifrequency helicopter vibrations using discrete model predictive sliding mode control. Proc IMechE, Part G: J Aerospace Engineering2016; 230: 668–680.
34.
LuYMaX. Active control of multifrequency helicopter vibrations using discrete model predictive sliding mode control. Proc IMechE, Part G: J Aerospace Engineering2016; 230: 668–680.
35.
Mizutami K, Hayashi S and Matsui N. Modeling and control of hybrid stepping motors. In: Conference record of the 1993 IEEE industry applications conference twenty-eighth IAS annual meeting, Toronto, Ontario, Canada, 06 August 2002. New York, NY: IEEE.
36.
ShengHZhangTJiangW. Full-range mathematical modeling of turboshaft engine in aerospace. Int J Turbo Jet-Engines2015; 33: 309–317.
