Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

Journal Article · 27 January 2022 · Journal of Turbomachinery

DOI: https://doi.org/10.1115/1.4053175 · OSTI ID:1973654

DeShong, Eric T. ^[1]; Peters, Benjamin ^[2]; Berdanier, Reid A. ^[3]; Thole, Karen A. ^[3]; Paynabar, Kamran ^[2]; Gebraeel, Nagi ^[2]

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1. Pennsylvania State Univ., University Park, PA (United States); Georgia Tech
2. Georgia Institute of Technology, Atlanta, GA (United States)
3. Pennsylvania State Univ., University Park, PA (United States)

Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, this study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modeling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Here, the results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within 3% of measured values.

• Research Organization: Georgia Institute of Technology, Atlanta, GA (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE), Office of Clean Energy Systems (FE-22)
• Grant/Contract Number: FE0031288
• OSTI ID: 1973654
• Journal Information: Journal of Turbomachinery, Journal Name: Journal of Turbomachinery Journal Issue: 6 Vol. 144; ISSN 0889-504X
• Publisher: ASMECopyright Statement
• Country of Publication: United States
• Language: English