Estimation of Real-Time Demands on the Basis of Pressure Measurements

2012 ◽  
Author(s):  
A. Vassiljev ◽  
T. Koppel
Keyword(s):  
Real Time ◽  
Pressure Measurements ◽  
Time Demands

scholarly journals Addressing Real-Time Demands for Robotic Path Planning Systems: A Routing Protocol Approach

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohammed Baziyad ◽  
Mohamed Saad ◽  
Raouf Fareh ◽  
Tamer Rabie ◽  
Ibrahim Kamel
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Routing Protocol ◽  
Planning Systems ◽  
Time Demands

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

2021 ◽  
pp. 1-40
Author(s):  
Eric DeShong ◽  
Benjamin Peters ◽  
Reid A. Berdanier ◽  
Karen A. Thole ◽  
Kamran Paynabar ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Feedback ◽  
Domain Knowledge ◽  
Performance Monitoring ◽  
Operating Conditions ◽  
Machine Learning Techniques ◽  
Pressure Measurements ◽  
Time Resolved ◽  
Learning Techniques ◽  
Purge Flow

Abstract 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.

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