TY - GEN
T1 - CORRELATING TIME-RESOLVED PRESSURE MEASUREMENTS with RIM SEALING EFFECTIVENESS for REAL-TIME TURBINE HEALTH MONITORING
AU - Eric, T. DeShong
AU - Benjamin Peters, Peters
AU - Reid, A. Berdanier
AU - Karen, A. Thole
AU - Kamran Paynabar, Paynabar
AU - Nagi Gebraeel, Gebraeel
N1 - Publisher Copyright:
© 2021 by ASME.
PY - 2021
Y1 - 2021
N2 - 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, the present 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 modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. 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 three percent of measured values.
AB - 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, the present 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 modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. 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 three percent of measured values.
UR - http://www.scopus.com/inward/record.url?scp=85115440899&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115440899&partnerID=8YFLogxK
U2 - 10.1115/GT2021-59586
DO - 10.1115/GT2021-59586
M3 - Conference contribution
AN - SCOPUS:85115440899
T3 - Proceedings of the ASME Turbo Expo
BT - Heat Transfer - General Interest; Internal Air Systems; Internal Cooling
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition, GT 2021
Y2 - 7 June 2021 through 11 June 2021
ER -