Estimation of the rotordynamic characteristics of a single brush seal using least-squares and instrumental variable methods under super-heated steam environment

• Published in: Advances in mechanical engineering Vol. 12; no. 3; p. 168781402091367
• Main Authors: Ha, Yunseok, Ha, Tae-Woong, Byun, Jaeseung, Lee, Yongbok
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.03.2020; Sage Publications Ltd; SAGE Publishing
• Subjects: Brush seals; Brushes; Damping; Dynamic characteristics; Least squares method; Noise reduction; Parameters; Rotor dynamics; Springs (elastic); Steam turbines; Stiffness; super-heated steam; Brush seal; rotordynamics; dynamic characteristics; steam turbine
• ISSN: 1687-8132; 1687-8140; 1687-8140
• DOI: 10.1177/1687814020913676

The exact understanding of the dynamic characteristic of the seal is a crucial parameter for designing the system. This article presents an experiment that estimates the dynamic characteristics of a brush seal under a super-heated steam environment for a steam turbine. The super-heated steam facility makes super-heated steam (523.15 K) to replicate the real steam turbine environment. Two brush seal units are utilized with a housing connected by springs to measure the modal parameters of the system. To extract the brush seal characteristics, the modal parameters of the pure housing were subtracted from the overall dynamic system. Moreover, to predict modal parameters more accurately, the least-squares method and the instrumental variable method were used to reduce the noise caused by the steam. Two major effects were experimentally investigated by varying the operating speed (0–16,900 r/min) of the rotor and the injection flow of super-heated steam. The results showed that the direct stiffness and damping of the brush seal increased significantly when the steam was injected. Under steam exposure, obtaining the modal parameters using instrumental variable method was confirmed to reduce more noise than obtaining the parameters via least-squares method.