Component Map Generation of a Gas Turbine Using Genetic Algorithms

2004 ◽  
Author(s):  
Changduk Kong ◽  
Seonghee Kho ◽  
Jayoung Ki
Keyword(s):  
Genetic Algorithms ◽  
Performance Analysis ◽  
Gas Turbine ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Experimental Tests ◽  
Real Component ◽  
Map Generation ◽  
Turboshaft Engine ◽  
Component Map

In order to estimate the precise performance of the existing gas turbine engine, the component maps with more realistic performance characteristics are needed. Because the components maps are engine manufacturer’s propriety obtained from very expensive experimental tests, they are not provided to the customers, generally. Therefore, because the engineers, who are working the performance simulation, have been mostly relying on component maps scaled from the similar existing maps, the accuracy of the performance analysis using the scaled maps may be relatively lower than that using the real component maps. Therefore, a component map generation method using experimental data and the genetic algorithms are newly proposed in this study. The engine test unit to be used for map generation has a free power turbine type small turboshaft engine. In order to generate the performance map for components of this engine, after obtaining engine performance data through many experimental tests, and then the third order equations, which have relationships the mass flow function, the pressure ratio and the isentropic efficiency as to the engine rotational speed were derived by using the genetic algorithm. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB (Kruzke, 2001). In order to verify predominance of the proposed scheme, the performance analysis results using the maps obtained by this study were compared with those using the compressor map provided by the engine manufacturer and the scaled turbine maps obtained from the GASTURB, as well as experimental results. In comparison, it was found that the component maps can be generated from the experimental test data by using the genetic algorithms, and it was confirmed that the analysis results using the generated maps were very similar to those using the scaled maps from the GASTURB.

Component Map Generation of a Gas Turbine Using Genetic Algorithms

2004 ◽  
Vol 128 (1) ◽  
pp. 92-96 ◽  
Author(s):  
Changduk Kong ◽  
Seonghee Kho ◽  
Jayoung Ki
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Performance Analysis ◽  
Gas Turbine ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Experimental Tests ◽  
Real Component ◽  
Map Generation ◽  
Component Map

In order to estimate the precise performance of the existing gas turbine engine, the component maps with more realistic performance characteristics are needed. Because the component maps are the engine manufacturer’s propriety obtained from very expensive experimental tests, they are not provided to the customers, generally. Therefore, because the engineers, who are working the performance simulation, have been mostly relying on component maps scaled from the similar existing maps, the accuracy of the performance analysis using the scaled maps may be relatively lower than that using the real component maps. Therefore, a component map generation method using experimental data and the genetic algorithms are newly proposed in this study. The engine test unit to be used for map generation has a free power turbine type small turboshaft engine. In order to generate the performance map for compressor of this engine, after obtaining engine performance data through experimental tests, and then the third order equations, which have relationships with the mass flow function, the pressure ratio, and the isentropic efficiency as to the engine rotational speed, were derived by using the genetic algorithms. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB (Kurzke, 2001). In order to verify the proposed scheme, the experimental data for verification were compared with performance analysis results using traditional scaled component maps and performance analysis results using a generated compressor map by genetic algorithms (GAs). In comparison, it was found that the analysis results using the generated map by GAs were well agreed with experimental data. Therefore, it was confirmed that the component maps can be generated from the experimental data by using GAs and it may be considered that the more realistic component maps can be obtained if more various conditions and accurate sensors would be used.

Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data

2006 ◽  
Vol 129 (2) ◽  
pp. 312-317 ◽  
Author(s):  
Changduk Kong ◽  
Jayoung Ki
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Gas Turbine ◽  
Test Data ◽  
Engine Performance ◽  
Scaling Method ◽  
Turbine Engine ◽  
Scaling Methods ◽  
Map Generation ◽  
Component Map

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be used. Because the components map is an engine manufacturer’s propriety obtained from many experimental tests with high cost, they are not provided to the customer generally. Some scaling methods for gas turbine component maps using experimental data or data partially given by engine manufacturers had been proposed in a previous study. Among them the map generation method using experimental data and genetic algorithms had showed the possibility of composing the component maps from some random test data. However not only does this method need more experimental data to obtain more realistic component maps but it also requires some more calculation time to treat the additional random test data by the component map generation program. Moreover some unnecessary test data may introduced to generate inaccuracy in component maps. The map generation method called the system identification method using partially given data from the engine manufacturer (Kong and Ki, 2003, ASME J. Eng. Gas Turbines Power, 125, 958–979) can improve the traditional scaling methods by multiplying the scaling factors at design point to off-design point data of the original performance maps, but some reference map data at off-design points should be needed. In this study a component map generation method, which may identify the component map conversely from some calculation results of a performance deck provided by the engine manufacturer using the genetic algorithms, was newly proposed to overcome the previous difficulties. As a demonstration example for this study, the PW206C turbo shaft engine for the tilt rotor type smart unmanned aerial vehicle which has been developed by Korea Aerospace Research Institute was used. In order to verify the proposed method, steady-state performance analysis results using the newly generated component maps were compared with them performed by the Estimated Engine Performance Program deck provided by the engine manufacturer. The performance results using the identified maps were also compared with them using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method would be more effective than the traditional scaling method and the methods explained above.

Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data

2006 ◽  
Author(s):  
Changduk Kong ◽  
Jayoung Ki ◽  
Changho Lee
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Gas Turbine ◽  
Test Data ◽  
Engine Performance ◽  
Scaling Method ◽  
Turbine Engine ◽  
Scaling Methods ◽  
Map Generation ◽  
Component Map

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be needed. Because the components map is an engine manufacturer’s propriety obtained from many experimental tests with high cost, they are not provided to the customer generally. Some scaling methods for gas turbine component maps using experimental data or data partially given by engine manufacturers had been proposed in previous study. Among them the map generation method using experimental data and genetic algorithms (Kong et al., 2004) had showed a possibility composing the component maps from some random test data. However not only this method needs more experimental data to obtain the more realistic component maps but also it requires some more calculation time to treat the additional random test data by component map generation program. Moreover some unnecessary test data may introduce to generate inaccuracy in component maps. And the map generation method called as the system identification method using partially given data from engine manufacturer (Kong et al., 2003) can improve the traditional scaling methods by multiplying the scaling factors at design point to off-design point data of the original performance maps, but some reference map data at off-design points should be needed. In this study a component map generation method which may identify component map conversely from some calculation results of a performance deck provided by engine manufacturer using the Genetic Algorithms was newly proposed to overcome the previous difficulties. As a demonstration example for this study, the PW206C turbo shaft engine for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute) was used. In order to verify the proposed method, steady-state performance analysis results using the newly generated component maps were compared with them performed by EEPP (Estimated Engine Performance Program) deck provided by engine manufacturer. And also the performance results using the identified maps were compared with them using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method would be more effective than the traditional scaling method and the methods explained at the above.

Component Map Tuning Procedure Using Adaptive Modeling

2012 ◽  
Author(s):  
Michel L. Verbist ◽  
Wilfried P. J. Visser ◽  
Rene Pecnik ◽  
Jos P. van Buijtenen
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Engine Performance ◽  
Diagnostic Techniques ◽  
Reference Points ◽  
Performance Data ◽  
Adaptive Modeling ◽  
Real Component ◽  
Modeling Errors ◽  
Component Map

Performance models are effective tools for analysis of engine condition throughout the life cycle of a gas turbine engine. Component maps necessary for accurate performance modeling are typically not provided by the original equipment manufacturers. To compensate for the missing information, available maps of similar components are scaled to match component performance at one or more reference points. Although scaled maps can provide sufficiently accurate results close to the reference points, modeling errors tend to increase further away from these reference points. For applications such as gas path analysis, the resulting modeling errors can be of the same order of magnitude as the deterioration to be detected. This severely limits the application of such techniques. This article presents a component map tuning procedure that tunes maps with more detail than just scaling. The tuned maps are a closer match to real component performance. The tuning procedure combines the adaptive modeling capability of the Gas turbine Simulation Program (GSP) and on-wing measured engine performance data. On-wing measured engine performance data allows map tuning over a wider range of power settings compared to engine performance data measured in a test cell. Effects of measurement uncertainty and scatter, and effects of compressor bleed flows on the map tuning procedure are analyzed and discussed. The tuned component maps enabled more accurate component condition estimations, mainly characterized by less scatter. By improving the accuracy of gas path analysis with on-wing measured performance data, this work has enabled more effective use of performance diagnostic techniques in the aero-engine maintenance industry.

GSP, a Generic Object-Oriented Gas Turbine Simulation Environment

2000 ◽  
Author(s):  
Wilfried P. J. Visser ◽  
Michael J. Broomhead
Keyword(s):  
Performance Analysis ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Engine Performance ◽  
Object Oriented ◽  
Control Logic ◽  
Turbine Engine ◽  
Industrial Gas Turbines ◽  
On Line ◽  
Turboshaft Engine

NLR’s primary tool for gas turbine engine performance analysis is the ‘Gas turbine Simulation Program’ (GSP), a component based modeling environment. GSP’s flexible object-oriented architecture allows steady-state and transient simulation of any gas turbine configuration using a user-friendly drag&drop interface with on-line help running under Windows95/98/NT. GSP has been used for a variety of applications such as various types of off-design performance analysis, emission calculations, control system design and diagnostics of both aircraft and industrial gas turbines. More advanced applications include analysis of recuperated turboshaft engine performance, lift-fan STOVL propulsion systems, control logic validation and analysis of thermal load calculation for hot section life consumption modeling. In this paper the GSP modeling system and object-oriented architecture are described. Examples of applications for both aircraft and industrial gas turbine performance analysis are presented.

A Novel Method for Component Map Identification of a Gas Turbine Using Intelligent Method and Engine Performance Deck Data

2007 ◽  
Author(s):  
Changduk Kong ◽  
Seonghee Kho ◽  
Jayoung Ki ◽  
Yongmin Jun
Keyword(s):  
Genetic Algorithms ◽  
System Identification ◽  
Rotational Speed ◽  
Pressure Ratio ◽  
Scaling Method ◽  
Characteristic Equations ◽  
System Identification Method ◽  
Identification Method ◽  
Map Generation ◽  
Component Map

In this study a component map generation method which can identify component characteristics conversely from limited performance deck data provided by engine supplier was newly proposed using a hybrid method with the genetic algorithms and the system identification method. Generally component performance characteristics of compressor and turbine can be expressed as the third order equation with the related function of the engine rotational speed versus the pressure ratio, the mass flow function and the isentropic efficiency. According to the newly proposed scheme, firstly the scaling is performed at each engine rotational speed with some limited performance data which are provided by engine manufacturer, and then the component characteristic equations are obtained using system identification method. Then the initially obtained component characteristics are modified by considering engine component behavior for various operational conditions such as flight Mach number, altitude and atmospheric temperature using genetic algorithms. In the modification, the component characteristics obtained by system identification are used as initial data to reduce the calculation time. And finally component maps are generated by integrating the component characteristic equations taken at each engine rotational speed. In order to verify the proposed method, steady-state performance analysis using the newly generated component maps were performed, and the analysis results were compared with the performance deck data and the analysis results using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method (1% errors) is more effective than the traditional scaling method (10% errors) in overall operational region.

scholarly journals Impact of compressed air energy storage demands on gas turbine performance

2020 ◽  
pp. 095765092090627 ◽  
Author(s):  
Uyioghosa Igie ◽  
Marco Abbondanza ◽  
Artur Szymański ◽  
Theoklis Nikolaidis
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Compressed Air ◽  
Design Stage ◽  
Simulation Code ◽  
Ratio Distribution ◽  
Stall Margin ◽  
Industrial Gas Turbines

Industrial gas turbines are now required to operate more flexibly as a result of incentives and priorities given to renewable forms of energy. This study considers the extraction of compressed air from the gas turbine; it is implemented to store heat energy at periods of a surplus power supply and the reinjection at peak demand. Using an in-house engine performance simulation code, extractions and injections are investigated for a range of flows and for varied rear stage bleeding locations. Inter-stage bleeding is seen to unload the stage of extraction towards choke, while loading the subsequent stages, pushing them towards stall. Extracting after the last stage is shown to be appropriate for a wider range of flows: up to 15% of the compressor inlet flow. Injecting in this location at high flows pushes the closest stage towards stall. The same effect is observed in all the stages but to a lesser magnitude. Up to 17.5% injection seems allowable before compressor stalls; however, a more conservative estimate is expected with higher fidelity models. The study also shows an increase in performance with a rise in flow injection. Varying the design stage pressure ratio distribution brought about an improvement in the stall margin utilized, only for high extraction.

Compressor Characteristics in Gas Turbine Performance Modelling

2001 ◽  
Author(s):  
Geoff Jones ◽  
Pericles Pilidis ◽  
Barry Curnock
Keyword(s):  
Gas Turbine ◽  
Pressure Loss ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Performance Model ◽  
Flow Function ◽  
Performance Modelling ◽  
Turbine Engine ◽  
Loss Parameter ◽  
Modelling Methods

The choice of how to represent the performance of the fans and compressors of a gas turbine engine in a whole-engine performance model can be critical to the number of iterations required by the solver or indeed whether the system can be solved. This paper therefore investigates a number of compressor modelling methods and compares their relative merits. Particular attention is given to investigating the ability of the various representations to model the performance far from design point. It is noted that, for low rotational speeds and flows, matching on pressure ratio will produce problems, and that efficiency is a discontinuous function at these conditions. Thus, such traditional representations of compressors are not suitable for investigations of starting or windmilling performance. Matching on pressure ratio, Beta, the Crainic exit flow function and the true exit flow function is investigated. The independent parameters of isentropic efficiency, pressure loss, a modified pressure loss parameter, specific torque, and ideal and actual enthalpy rises are compared. The requirements of the characteristic choice are investigated, with regard to choosing matching variables and ensuring that relationships are smooth and continuous throughout the operating range of the engine.

An Adaptation Approach for Gas Turbine Design-Point Performance Simulation

2005 ◽  
Author(s):  
Y. G. Li ◽  
P. Pilidis ◽  
M. A. Newby
Keyword(s):  
Performance Analysis ◽  
Gas Turbine ◽  
Engine Performance ◽  
Performance Parameters ◽  
Matching Problem ◽  
Design Point ◽  
Engine Component ◽  
Target Performance ◽  
Turbine Design ◽  
Unknown Component

Accurate simulation and understanding of gas turbine performance is very useful for gas turbine users. Such a simulation and performance analysis must start from a design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be carried out. However, the initially simulated design point performance of the engine using estimated engine component parameters may give a result that is different from the actual measured performance. This difference may be reduced with better estimation of these unknown component parameters. However, this can become a difficult task for performance engineers, let alone those without enough engine performance knowledge and experience, when the number of design point component parameters and the number of measurable/target performance parameters become large. In this paper, a gas turbine design point performance adaptation approach has been developed to best estimate the unknown design point component parameters and match the available design point engine measurable/target performance. In the approach, the initially unknown component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, air mass flow rate, cooling flows, by-pass ratio, etc. The engine target (measurable) performance parameters may be thrust and SFC for aero engines, shaft power and thermal efficiency for industrial engines, gas path pressures and temperatures, etc. To select initially the design point component parameters, a bar chart has been used to analyze the sensitivity of the engine target performance parameters to the design point component parameters. The developed adaptation approach has been applied to a design point performance matching problem of an industrial gas turbine engine GE LM2500+ operating in Manx Electricity Authority (MEA), UK. The application shows that the adaptation approach is very effective and fast to produce a set of design point component parameters of a model engine that matches the actual engine performance very well. Theoretically the developed techniques can be applied to other gas turbine engines.

scholarly journals Ideal Power Output — An Alternative Aircraft Engine Performance Comparator

1997 ◽  
Author(s):  
Marvin F. Schmidt ◽  
Christopher M. Norden ◽  
Jeffrey M. Stricker
Keyword(s):  
Gas Turbine ◽  
Power Output ◽  
State Of The Art ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Aircraft Engine ◽  
The State ◽  
Power Cycle ◽  
Shaft Power ◽  
Bypass Ratio

The gas turbine is applied in four basic configurations; the turbojet, the turbofan, the turboprop and the turboshaft. Comparisons of the performance of these various configurations is difficult since they convert the energy to different forms, i.e. thrust or shaft power. Cycle variables which do not necessarily constitute advancements in the state-of-the-art such as bypass ratio and fan pressure ratio can have a profound effect on thrust and shaft power. Differences in flight speed and altitude capability further confound the comparisons. What is required is a comparison methodology that removes all of these variables and yet puts all the various types of engines on an equitable basis. This paper will provide such a comparison tool. All turbomachinery, regardless of configuration, can be compared with this method.

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