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

2021 ◽  
Author(s):  
Eric T. 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.

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.

Influence of a Cylindrical Exhaust Hood Installation on the Last Stage Rotor Blades of a Low Pressure Model Steam Turbine

2017 ◽  
Author(s):  
Fabian F. Müller ◽  
Markus Schatz ◽  
Damian M. Vogt ◽  
Jens Aschenbruck
Keyword(s):  
Flow Field ◽  
Steam Turbine ◽  
Operating Conditions ◽  
Rotor Blades ◽  
Pressure Measurements ◽  
Exhaust Hood ◽  
Blade Vibration ◽  
Time Resolved ◽  
Vibration Behavior ◽  
Last Stage

The influence of a cylindrical strut shortly downstream of the bladerow on the vibration behavior of the last stage rotor blades of a single stage LP model steam turbine was investigated in the present study. Steam turbine retrofits often result in an increase of turbine size, aiming for more power and higher efficiency. As the existing LP steam turbine exhaust hoods are generally not modified, the last stage rotor blades frequently move closer to installations within the exhaust hood. To capture the influence of such an installation on the flow field characteristics, extensive flow field measurements using pneumatic probes were conducted at the turbine outlet plane. In addition, time-resolved pressure measurements along the casing contour of the diffuser and on the surface of the cylinder were made, aiming for the identification of pressure fluctuations induced by the flow around the installation. Blade vibration behavior was measured at three different operating conditions by means of a tip timing system. Despite the considerable changes in the flow field and its frequency content, no significant impact on blade vibration amplitudes were observed for the investigated case and considered operating conditions. Nevertheless, time-resolved pressure measurements suggest that notable pressure oscillations induced by the vortex shedding can reach the upstream bladerow.

scholarly journals Data-driven Adaptive Thresholding Model for Real-time Valve Delay Estimation in Digital Pump/Motors

2020 ◽  
Author(s):  
Abdallah Chehade ◽  
Farid Breidi ◽  
Keith Scott Pate ◽  
John Lumkes
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Domain Knowledge ◽  
Operating Conditions ◽  
Delay Model ◽  
Hydraulic Systems ◽  
Pressure Transducers ◽  
Statistical Behavior ◽  
Shift Detection ◽  
And Performance

Valve characteristics are an essential part of digital hydraulics. The on/off solenoid valves utilized on many of these systems can significantly affect the performance. Various factors can affect the speed of the valves causing them to experience various delays, which impact the overall performance of hydraulic systems. This work presents the development of an adaptive statistical based thresholding real-time valve delay model for digital Pump/Motors. The proposed method actively measures the valve delays in real-time and adapts the threshold of the system with the goal of improving the overall efficiency and performance of the system. This work builds on previous work by evaluating an alternative method used to detect valve delays in real-time. The method used here is a shift detection method for the pressure signals that utilizes domain knowledge and the system’s historical statistical behavior. This allows the model to be used over a large range of operating conditions, since the model can learn patterns and adapt to various operating conditions using domain knowledge and statistical behavior. A hydraulic circuit was built to measure the delay time experienced from the time the signal is sent to the valve to the time that the valve opens. Experiments were conducted on a three piston in-line digital pump/motor with 2 valves per cylinder, at low and high pressure ports, for a total of six valves. Two high frequency pressure transducers were used in this circuit to measure and analyze the differential pressure on the low and high pressure side of the on/off valves, as well as three in-cylinder pressure transducers. Data over 60 cycles was acquired to analyze the model against real time valve delays. The results show that the algorithm was successful in adapting the threshold for real time valve delays and accurately measuring the valve delays. 

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

Artificial Intelligence and Data Analytics for Virtual Flow Metering

2021 ◽  
Author(s):  
Anton Gryzlov ◽  
Liliya Mironova ◽  
Sergey Safonov ◽  
Muhammad Arsalan
Keyword(s):  
Machine Learning ◽  
Multiphase Flow ◽  
Real Time ◽  
Production Control ◽  
Measurement Data ◽  
Sufficient Accuracy ◽  
Operating Conditions ◽  
Machine Learning Techniques ◽  
Flow Metering ◽  
Oil Gas

Abstract Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain. Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm. In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.

Maximising Asset Value through Implementation of Dynamic Well Operating Envelop

2021 ◽  
Author(s):  
Rafael Islamov ◽  
Eghbal Motaei ◽  
Bahrom Madon ◽  
Khairul Azhar Abu Bakar ◽  
Victor Hamdan ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Oil And Gas ◽  
Operating Conditions ◽  
Well Performance ◽  
Time Dynamic ◽  
Well Model ◽  
Maximum Range ◽  
Facilities Design ◽  
Well Models

Abstract Dynamic Well Operating Envelop (WOE) allows to ensure that well is maintained and operated within design limits and operated in the safe, stable and profitable way. WOE covers the Well Integrity, Reservoir constraints and Facility limitations and visualizes them on well performance chart (Hamzat et al., 2013). Design and operating limits (such as upper and lower completion/facilities design pressures, sand failure, erosion limitations, reservoir management related limitations etc) are identified and translated into two-dimensional WOE (pressure vs. flowrate) to ensure maximum range of operating conditions that represents safe and reliable operation are covered. VLP/IPR performance curves were incorporated based on latest Validated Well Model. Optimum well operating window represents the maximum range of operating conditions within the Reservoir constraints assessed. By introducing actual Well Performance data the optimisation opportunities such as production/injection enhancement identified. During generating the Well Operating Envelops tremendous work being done to rectify challenges such as: most static data (i.e. design and reservoir limitations) are not digitized, unreliable real-time/dynamic data flow (i.e. FTHP, Oil/Gas rates etc), disintegrated and unreliable well Models and no solid workflows for Flow assurance. As a pre-requisite the workflows being developed to make data tidy i.e.ready and right, and Well Model inputs being integrated to build updated Well Models. Successful WOE prototype is generated for natural and artificially lifted Oil and Gas wells. Optimisation opportunities being identified (i.e. flowline pressure reduction, reservoir stimulation and bean-up) Proactive maintenance is made possible through dynamic WOE as a real time exceptional based surveillance (EBS) tool which is allowing Asset engineers to conduct the well performance monitoring, and maintain it within safe, stable and profitable window. Additionally, it allows to track all Production Enhancement jobs and seamless forecasting for new opportunities.

Bidirectional GRU-Based Attention Model for Kid-Specific URL Classification

2020 ◽  
pp. 78-90 ◽  
Author(s):  
Rajalakshmi R. ◽  
Hans Tiwari ◽  
Jay Patel ◽  
Rameshkannan R. ◽  
Karthik R.
Keyword(s):  
Domain Knowledge ◽  
Machine Learning Techniques ◽  
Web Content ◽  
Web Page ◽  
Web Page Classification ◽  
Short Text ◽  
Attention Model ◽  
Effective Learning ◽  
Learning Techniques ◽  
Page Classification

The Gen Z kids highly rely on internet for various purposes like entertainment, sports, and school projects. There is a demand for parental control systems to monitor the children during their surfing time. Current web page classification approaches are not effective as handcrafted features are extracted from the web content and machine learning techniques are used that need domain knowledge. Hence, a deep learning approach is proposed to perform URL-based web page classification. As the URL is a short text, the model should learn to understand where the important information is present in the URL. The proposed system integrates the strength of attention mechanism with recurrent convolutional neural network for effective learning of context-aware URL features. This enhanced architecture improves the design of kids-relevant URL classification. By conducting various experiments on the benchmark collection Open Directory Project, it is shown that an accuracy of 0.8251 was achieved.

Evaluating the Effects of Transient Purge Flow on Stator-Rotor Seal Performance

2020 ◽  
pp. 1-55
Author(s):  
Reid A. Berdanier ◽  
Eric DeShong ◽  
Karen A. Thole ◽  
Christopher Robak
Keyword(s):  
Measurement Techniques ◽  
Operating Conditions ◽  
Pressure Measurements ◽  
Physical Mechanisms ◽  
Melting Temperatures ◽  
Thermally Driven ◽  
Purge Flow ◽  
Transient Events ◽  
Critical Components ◽  
The Relationship

Abstract As engine designs target higher efficiencies through increased turbine inlet temperatures, critical components are at risk of damage from conditions exceeding material melting temperatures. In particular, improperly designed underplatform hardware are susceptible to damage when hot gas is ingested into the stator-rotor cavity. While all turbines inherently experience transients during operation, a majority of publicly available turbine studies have been executed using steady operating conditions or transient “blowdown” rigs. For this reason, routine transient events are not well understood. To address this need, this study utilized a combined experimental and computational approach. The test article is a continuous-duration, one-stage turbine operating with true-scale engine hardware and seal geometries at engine-representative flow conditions. The nature of the continuous-duration facility supports assessment of transient events through its ability to transition between steady-state operating conditions. The effects of transient purge flow were investigated to identify general trends for transient events in a full-scale engine. Results from multiple measurement techniques in the wheelspace region show an interdependence of transient purge flow with thermal lag of the underplatform hardware. Through experiments conducted at different coolant-to-main gas path temperature ratios, the use of pressure measurements as an indicator of fully-purged behavior was introduced, and a thermally-driven influence on rim seal performance was quantified. The computational results show good agreement with experimental pressure measurements and provide insight into the physical mechanisms that drive the relationship between pressure and sealing effectiveness observed in the experiments.

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