Blade tip clearance (BTC) is one of the key factors affecting the efficiency and reliability of high performance turbomachinery such as heavy duty steam turbines, aircraft engines and other gas turbo machines. The self-adjusting ability of BTC according to the operation condition changing is important to meet the requirement of performance. In this paper, the principle and method of adjusting the BTC by controlling the axial displacement of the rotor were proposed and studied. The basic principle is that the BTC of the turbomachinery with a conical tail shroud will be affected by the axial displacement of rotor and thereby can be adjusted by controlling the axial position of rotor, which can be adjusted by the controllable oil pressure acting on the thrust bearing. To reach a higher control precision, lower noise and model perturbation, an adaptive quasi-sliding mode control (AQSMC) algorithm based on the disturbance observer (DOB) was designed, and numerical and experimental investigations were carried out. The numerical simulation results show that this algorithm can not only effectively suppress the disturbance, but also, compared with the general reaching law, effectively reduce the chattering and transient high gain switching effect of the closed-loop controller system and avoid the instability caused by the controller. Based on the DOB-AQSMC algorithm, the BTC was stabilized within 2 s with no overshoot and no misalignment in the test rig, and this algorithm achieves a better control performance than the proportion-integral-differential (PID) algorithm. These achievements can be used to push forward the intelligent turbomachinery development.
[1]
Jonathan A. DeCastro,et al.
A Study on the Requirements for Fast Active Turbine Tip Clearance Control Systems
,
2004
.
[2]
Bruce M. Steinetz,et al.
Evaluation of an Active Clearance Control System Concept
,
2006
.
[4]
Kevin J. Melcher,et al.
Toward a Fast-Response Active Turbine Tip Clearance Control
,
2003
.
[5]
Yunpeng Zhu,et al.
Determining Dynamic Scaling Laws of Geometrically Distorted Scaled Models of a Cantilever Plate
,
2016
.
[6]
Chen Yanhua,et al.
ADVANCES IN THE RESEARCH ON NONLINEAR PHENOMONA IN ROTOR/STATOR RUBBING SYSTEMS
,
2013
.
[7]
Albert-László Barabási,et al.
Controllability of complex networks
,
2011,
Nature.
[9]
Fei Wang,et al.
The NARX Model-Based System Identification on Nonlinear, Rotor-Bearing Systems
,
2017
.
[10]
Haibin Yu,et al.
Advanced Manufacturing Technology in China: A Roadmap to 2050
,
2010
.
[11]
Ion Stiharu,et al.
More Intelligent Gas Turbine Engines
,
2007
.