Training Future Engineers on Gas Turbine Gas Path Diagnostics Using PYTHIA

2014 ◽  
Author(s):  
Y. G. Li
Keyword(s):  
Gas Turbine ◽  
Condition Monitoring ◽  
Measurement Data ◽  
Engine Performance ◽  
Training Model ◽  
Industrial Applications ◽  
Performance Models ◽  
Training And Education ◽  
Thermodynamic Performance ◽  
User Friendly

Technology on gas turbine gas path diagnostics has become more and more mature over the years and the demand of the technology in industrial applications, training and education is increasing. To make the technology more accessible to gas turbine users in different areas, including gas turbine design, operation, condition monitoring, maintenance, education, training, etc., comprehensive software to support an easy, fast and user friendly application of the technology has become highly desirable. In this paper, gas turbine thermodynamic performance and diagnostics software, PYTHIA, and a training model on gas turbine gas path diagnostics have been introduced. The objective of the training is to provide trainees the knowledge and an opportunity to effectively learn how to set up gas turbine thermodynamic performance models, understand degradation effects on engine performance, improve the accuracy of engine performance models, pre-process measurement data, detect sensor faults and carry out engine diagnostic analysis. Four tutorial exercises are provided to demonstrate how to use the PYTHIA software to support the training and education. As the gas path diagnostic technology and the software have been developed for industrial applications, particular for gas turbine condition monitoring and diagnostics, it is user friendly and has a high level of flexibility to be used for different gas turbine engines and can be accessed remotely via internet. Therefore it provides a useful tool and a virtual environment to enhance the effectiveness of the training and education of the technology. Such technology and training model is suitable for professional engineers and postgraduate students.

Calibration and Uncertainty Quantification of Gas Turbine Performance Models

2015 ◽  
Author(s):  
Manuel Arias Chao ◽  
Darrel S. Lilley ◽  
Peter Mathé ◽  
Volker Schloßhauer
Keyword(s):  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Bayesian Approach ◽  
Least Squares Method ◽  
Measurement Data ◽  
Operating Conditions ◽  
Performance Models ◽  
Regression Problem ◽  
Calibration Problem ◽  
Calibration Parameters

Calibration and uncertainty quantification for gas turbine (GT) performance models is a key activity for GT manufacturers. The adjustment between the numerical model and measured GT data is obtained with a calibration technique. Since both, the calibration parameters and the measurement data are uncertain the calibration process is intrinsically stochastic. Traditional approaches for calibration of a numerical GT model are deterministic. Therefore, quantification of the remaining uncertainty of the calibrated GT model is not clearly derived. However, there is the business need to provide the probability of the GT performance predictions at tested or untested conditions. Furthermore, a GT performance prediction might be required for a new GT model when no test data for this model are available yet. In this case, quantification of the uncertainty of the baseline GT, upon which the new development is based on, and propagation of the design uncertainty for the new GT is required for risk assessment and decision making reasons. By using as a benchmark a GT model, the calibration problem is discussed and several possible model calibration methodologies are presented. Uncertainty quantification based on both a conventional least squares method and a Bayesian approach will be presented and discussed. For the general nonlinear model a fully Bayesian approach is conducted, and the posterior of the calibration problem is computed based on a Markov Chain Monte Carlo simulation using a Metropolis-Hastings sampling scheme. When considering the calibration parameters dependent on operating conditions, a novel formulation of the GT calibration problem is presented in terms of a Gaussian process regression problem.

Experience With GSP as a Gas Path Analysis Tool

2006 ◽  
Author(s):  
W. P. J. Visser ◽  
H. Pieters ◽  
M. Oostveen ◽  
E. van Dorp
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Reference Model ◽  
Measurement Data ◽  
Engine Performance ◽  
Analysis Data ◽  
Calibration Method ◽  
Analysis Tool ◽  
Operational Environment ◽  
Adaptive Modeling

SKF’s primary tool for gas turbine engine performance analysis is GSP (Gas turbine Simulation Program), a component based modeling environment that is developed at National Aerospace Laboratory NLR and Delft University of Technology, The Netherlands. One of the applications is gas path analysis (GPA) using GSP’s generic adaptive modeling capability. With GSP, gas path analysis has been applied to different aero engines at several maintenance facilities. Additional functionalities have been developed to analyze multiple engine operating points and combine results of different adaptive modeling configurations automatically, resulting in more accurate and reliable GPA results. A ‘multi-point calibration’ method for the reference model was developed providing a significant improvement of GPA accuracy and stability. Also, a method was developed using ‘multiple analysis cycles’ on different condition indicator subsets, which successfully generated values for all condition parameters in cases with fewer measurement parameters than condition indicators and where measurement data are unreliable. The method has been successfully demonstrated on the GEM42 turbo shaft engine. A number of case studies have shown GPA results corresponding to available maintenance notes and inspection data. The extension of the GSP GPA tool with a database system provides a useful tool for analyzing engine history and comparison of analyzed component conditions throughout the fleet. When a large amount of analysis data is stored in the database, statistic analyses, trending and data mining can be performed. Also maintenance work scope effect on engine performance can be predicted. In this paper, the newly developed GSP gas path analysis functionalities are described and experiences and results with the GEM42 engine operational environment are presented.

Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

2011 ◽  
Vol 133 (7) ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Scaling Factor ◽  
Performance Model ◽  
Operating Conditions ◽  
Performance Models ◽  
Design Performance ◽  
Turbine Performance ◽  
Thermodynamic Performance ◽  
Performance Adaptation

Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modeling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a nonlinear multiple point performance adaptation approach using a genetic algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced nonlinear map scaling factor functions by “modifying” initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A genetic algorithm is used to search for an optimal set of nonlinear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turbo-shaft aero gas turbine engine and has demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.

Evaluating Gas Turbine Performance Using Machine-Generated Data: Quantifying Degradation and Impacts of Compressor Washing

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Uyioghosa Igie ◽  
Pablo Diez-Gonzalez ◽  
Antoine Giraud ◽  
Orlando Minervino
Keyword(s):  
Gas Turbine ◽  
Measurement Data ◽  
Engine Performance ◽  
Large Data ◽  
Simulation Software ◽  
Engine Operation ◽  
Compressor Inlet ◽  
Data Points ◽  
Global And Local ◽  
The Impact

Gas turbine (GT) operators are often met with the challenge of utilizing and making meaning of the vast measurement data collected from machine sensors during operation. This can easily be about 576 × 106 data points of gas path measurements for one machine in a base load operation in a year, if the width of the data is 20 columns of measured and calculated parameters. This study focuses on the utilization of large data in the context of quantifying the degradation that is mostly related to compressor fouling, in addition to investigations on the impact of offline and online compressor washing. To achieve this, four GT engines operating for about 3.5 years with 51 offline washes and 1184 occasions of online washes were examined. This investigation includes different wash frequencies, liquid concentrations, and one engine operation without online washing (only offline). This study has involved correcting measurement data not only just with compressor inlet temperatures (CITs) and pressures but also with relative humidity (RH). turbomatch, an in-house GT performance simulation software has been implemented to obtain nondimensional factors for the corrections. All of the data visualization and analysis have been conducted using tableau analytics software, which facilitates the investigation of global and local events within an operation. The concept of using of handles and filters is proposed in this study, and it demonstrates the level of insight to the data and forms the basis of the outcomes obtained. This work shows that during operation, the engine performance is mostly deteriorating, though to varying degrees. Online washing also showed an influence on this, reducing the average degradation rate each hour by half, when compared to the engine operating only with offline washing. Hourly marginal improvements were also observed with an increased average wash frequency of nine hours and a similar outcome obtained when the washing solution is 2.3 times more concentrated. Clear benefits of offline washes are also presented, alongside the typically obtainable values of increased power output after a wash, also in relation to the number of operating hours before a wash.

scholarly journals On-Board Condition Management for Aircraft Gas Turbines

1992 ◽  
Author(s):  
G. W. Gallops ◽  
F. D. Gass ◽  
M. H. Kennedy
Keyword(s):  
Control System ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
System Architecture ◽  
Gas Turbines ◽  
Performance Model ◽  
Performance Models ◽  
Turbine Performance ◽  
Secondary Air ◽  
System Operating

A revolutionary approach to gas turbine condition monitoring is made possible by the recent development of accurate real-time gas turbine performance models. This paper describes an approach for an integrated condition management system operating concurrently with the gas turbine control system for improved availability, safety and economy. This paper considers the system subject to the requirements and constraints of aircraft gas turbines. A system architecture is described based on a primary, gas path performance model with supplementary models representing the secondary air, fuel and lubrication systems and the rotor system dynamics. Measurement and processing requirements for the system are defined. Preflight, in-flight and postflight application and analysis by the gas turbine operator are discussed.

Instructing the Principles of Gas Turbine Performance Monitoring and Diagnostics by Means of Interactive Computer Models

2000 ◽  
Author(s):  
K. Mathioudakis ◽  
A. Stamatis ◽  
A. Tsalavoutas ◽  
N. Aretakis
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Measurement Data ◽  
Engine Performance ◽  
Performance Model ◽  
Operating Conditions ◽  
Engine Operation ◽  
Turbine Performance ◽  
Engine Components

The paper discusses how the principles employed for monitoring the performance of gas turbines in industrial duty can be explained by using suitable Gas Turbine performance models. A particular performance model that can be used for educational purposes is presented. The model allows the presentation of basic rules of gas turbine engine behavior and helps understanding different aspects of its operation. It is equipped with a graphics interface, so it can present engine operating point data in a number of different ways: operating line, operating points of the components, variation of particular quantities with operating conditions etc. Its novel feature, compared to existing simulation programs, is that it can be used for studying cases of faulty engine operation. Faults can be implanted into different engine components and their impact on engine performance studied. The notion of fault signatures on measured quantities is clearly demonstrated. On the other hand, the model has a diagnostic capability, allowing the introduction of measurement data from faulty engines and providing a diagnosis, namely a picture of how the performance of engine components has deviated from nominal condition, and how this information gives the possibility for fault identification.

Non-Linear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

2010 ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Scaling Factor ◽  
Operating Conditions ◽  
Performance Models ◽  
Design Performance ◽  
Turbine Performance ◽  
Thermodynamic Performance ◽  
Non Linear ◽  
Performance Adaptation

Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modelling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a non-linear multiple point performance adaptation approach using a Genetic Algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced non-linear map scaling factor functions by ‘modifying’ initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A Genetic Algorithm is used to search for an optimal set of non-linear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turboshaft aero gas turbine engine and demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.

Inverse Generation of Gas Turbine Component Performance Maps From Experimental Test Data

2010 ◽  
Author(s):  
Changduk Kong ◽  
Semyeong Lim ◽  
Seonghwan Oh ◽  
Jihyun Kim
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Test Data ◽  
Performance Test ◽  
Monitoring Program ◽  
Engine Performance ◽  
Gas Turbine Engine ◽  
Performance Data ◽  
Turbine Engine

The gas turbine engine performance is greatly relied on its component performance characteristics. Generally, acquisition of component maps is not easy for engine purchasers because it is an intellectual property of gas turbine engine supplier. In the previous work, the maps were inversely generated from engine performance deck data. However this method is limited to obtain the realistic maps from the calculated performance deck data. Present work proposes a novel method to generate more realistic component maps from experimental performance test data. In order to demonstrate the proposed method, firstly the NI data acquisition device with the proposed LabVIEW on-condition monitoring program monitors and collects real-time performance data such as temperature, pressure, thrust, and fuel flow etc. from a micro turbojet engine of the test setup which is specially manufactured for this study. Real-time data obtained from the test results are used for inverse generation of the component maps after processing by some numerical schemes. Realistic component maps can then be generated from those processed data using the proposed extended scaling method at each rotational speed. Verification can be made through comparison between performance analysis results using the performance simulation program including the generated compressor map and on-condition monitoring performance data.

scholarly journals Development of low-cost non-contact structural health monitoring system for rotating machinery

2018 ◽  
Vol 5 (6) ◽  
pp. 172430 ◽  
Author(s):  
Vanraj ◽  
Robin Singh ◽  
S. S. Dhami ◽  
B. S. Pabla
Keyword(s):  
Condition Monitoring ◽  
Low Cost ◽  
Industrial Applications ◽  
General Purpose ◽  
Rotating Machinery ◽  
Efficiency Condition ◽  
Contact Data ◽  
Condition Monitoring Systems ◽  
User Friendly ◽  
Friendly Graphical User Interface

Condition monitoring systems are increasingly being employed in industrial applications to improve the availability of equipment to increase the overall equipment efficiency. Condition monitoring of gearboxes, a key element of rotating machines, ensures to continuously reduce and eliminate costs, unscheduled downtime and unexpected breakdowns. This study demonstrates a low-cost microcontroller-based non-contact data acquisition system for condition monitoring of rotating machinery. Experimental validation of the proposed system was carried out by performing examination tests on a gearbox test rig. A user-friendly graphical user interface was also developed which facilitates users to perform signal processing in both real-time and offline mode. The proposed system can perform most of the functions available in complex, stand-alone vibration analysers. The use of a general-purpose PC and standard programing language makes the system simple, economical and adaptable to a variety of problems. The tests show the developed system can perform properly as proposed.

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