<p>Ingestion of hot main gas path fluid through the turbine rim seal causes damage to the vulnerable under-platform hardware components. To prevent this ingress, purge flow is bled from the upstream compressor and supplied to the under-platform region. However, the purge flow must be used effectively and sparingly to optimize the efficiency of the engine. 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.&nbsp;</p><p>To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with engine-representative hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are correlated 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 turbine operating conditions. Results show that an initial purely data-driven model can be further developed using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.</p>

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

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2021/volume/84898

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