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.

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.

Part-Load Performance of Gas Turbines: Part II — Multi-Point Adaptation With Compressor Map Generation and GA Optimization

2012 ◽  
Author(s):  
E. Tsoutsanis ◽  
Y. G. Li ◽  
P. Pilidis ◽  
M. Newby
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Combined Cycle ◽  
Performance Model ◽  
Part Load ◽  
Design Performance ◽  
Turbine Performance ◽  
Map Generation ◽  
Compressor Map ◽  
Performance Adaptation

Accurate gas turbine performance simulation is a vital aid to the operational and maintenance strategy of thermal plants having gas turbines as their prime mover. Prediction of the part load performance of a gas turbine depends on the quality of the engine’s component maps. Taking into consideration that compressor maps are proprietary information of the manufacturers, several methods have been developed to encounter the above limitation by scaling and adapting component maps. This part of the paper presents a new off-design performance adaptation approach with the use of a novel compressor map generation method and Genetic Algorithms (GA) optimization. A set of coefficients controlling a generic compressor performance map analytically is used in the optimization process for the adaptation of the gas turbine performance model to match available engine test data. The developed method has been tested with off-design performance simulations and applied to a GE LM2500+ aeroderivative gas turbine operating in Manx Electricity Authority’s combined cycle power plant in the Isle of Man. It has been also compared with an earlier off-design performance adaptation approach, and shown some advantages in the performance adaptation.

Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

2006 ◽  
Author(s):  
E. Lo Gatto ◽  
Y. G. Li ◽  
P. Pilidis
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Performance Prediction ◽  
Engine Performance ◽  
Design Data ◽  
Adaptive Approach ◽  
Design Point ◽  
Design Performance ◽  
Engine Design ◽  
Performance Adaptation

Gas turbine gas path diagnostics is heavily dependent on performance simulation models accurate enough around a chosen diagnostic operating point, such as design operating point. With current technology, gas turbine engine performance can be predicted easily with thermodynamic models and computer codes together with basic engine design data and empirical component information. However the accuracy of the prediction is highly dependent on the quality of those engine design data and empirical component information such as component characteristic maps but such expensive information is normally exclusive property of engine manufacturers and only partially disclosed to engine users. Alternatively, estimated design data and assumed component information are used in the performance prediction. Yet, such assumed component information may not be the same as those of real engines and therefore poor off-design performance prediction may be produced. This paper presents an adaptive method to improve the accuracy of off-design performance prediction of engine models near engine design point or other points where detailed knowledge is available. A novel definition of off-design scaling factors for the modification of compressor maps is developed. A Genetic Algorithm is used to search the best set of scaling factors in order to adapt the predicted off-design engine performance to observed engine off-design performance. As the outcome of the procedure, new compressor maps are produced and more accurate prediction of off-design performance is provided. The proposed off-design performance adaptation procedure is applied to a model civil aero engine to test the effectiveness of the adaptive approach. The results show that the developed adaptive approach, if properly applied, has great potential to improve the accuracy of engine off-design performance prediction in the vicinity of engine design point although it does not guarantee the prediction accuracy in the whole range of off-design conditions. Therefore, such adaptive approach provides an alternative method in producing good engine performance models for gas turbine gas path diagnostic analysis.

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.

Improved Method for Gas-Turbine Off-Design Performance Adaptation Based on Field Data

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Shiyao Li ◽  
Zhenlin Li ◽  
Shuying Li
Keyword(s):  
Gas Turbine ◽  
Error Control ◽  
Field Data ◽  
Operating Conditions ◽  
Performance Parameters ◽  
Improved Method ◽  
Design Performance ◽  
Average Prediction Error ◽  
Comparison Results ◽  
Performance Adaptation

Abstract Obtaining accurate components' characteristic maps has great significant for gas-turbine operating optimization and gas-path fault diagnosis. A common approach is to modify the original components' characteristic maps by introducing correction factors, which is known as performance adaptation. Among the existing methods, total average prediction error of measurable parameters (MPTAPE) at specified conditions is used to evaluate the adaptation accuracy. However, when a gas turbine undergoes a field operation, the performance parameters of each component are zonally distributed under the operating conditions. Under such circumstances, randomly selecting a few data points as the error control points (ECPs) for performance adaptation may lead to an inappropriate correction of the characteristic maps, further lowering the prediction accuracy of the simulation model. In this paper, a genetic-algorithm-based improved performance adaptation method is proposed, which provides improvements in two aspects. In one aspect, similarity between the components' predicted performance curves and the performance regression curves is used as the criterion with which to evaluate the adaptation accuracy. In the other aspect, in the process of off-design performance adaptation, the performance parameters at the design point are recalibrated. The improved method has been verified by using rig test data and applied to field data of a GE LM2500+SAC gas turbine. The comparison results show that the improved method can obtain more accurate and stable adaptation results, while the computational load can be significantly reduced.

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.

ALGOR Gas Turbine Performance Modular Tool: One Dimensional Turbine Module Validation

2016 ◽  
Author(s):  
Stefano Piola ◽  
Roberto Canepa ◽  
Andrea Silingardi ◽  
Stefano Cecchi ◽  
Carlo Carcasci ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Pressure Ratio ◽  
Operating Conditions ◽  
Computational Time ◽  
Flow Angle ◽  
One Dimensional ◽  
Design Performance ◽  
Turbine Performance ◽  
Air System ◽  
Secondary Air

One dimensional codes play a key role in gas turbine performance simulation: once they are calibrated they can give reliable results within very short computational time if compared to two or three dimensional analysis. Thanks to their ability to quickly evaluate flow, pressure and temperature along the energy conversion from fluid to shaft or reverse, one dimensional tools fit the requirements of modular-structured program for the simulation of complete gas turbine. In ASEN experience, ALGOR heat and mass balance software is used as a platform for system integration between each disciplines by means of a modular structure in which a large number of modules, chosen from the available library, are freely connected allowing to potentially analyze any gas turbine engine configuration. ALGOR code provides advanced cycle calculation capabilities for example in case that cooling and secondary air system layout modification have to be considered in design process. In these situations, a turbine map-based approach is hardly applicable, while a one dimensional aerodynamic row by row simulation can provide a suitable method for off-design turbine behavior prediction. In ASEN practice, ALGOR turbine module is calibrated at design point on one dimensional data provided by turbine designers and is then adopted for the engine configuration optimization or off-design performance evaluation. This paper presents the validation of the off-design performance prediction given by the ALGOR embedded 1D turbine model comparing calculated results with experimental ones. The warm air full scale test rig investigated within the GE-NASA “Energy Efficient Engine” program for the aerodynamic evaluation of a two stages high pressure turbine has been chosen as validation case. It includes both experimental performance maps varying turbine operating conditions such as speed and pressure ratio extending to the sub-idle and starting region and an analysis of cooling flow variation effect on turbine performance. Literature available loss and exit flow angle correlations are implemented and compared to experimental data. The results given by each of them are analyzed to appreciate their accuracy in evaluating efficiency and flow variations. In addition the paper shows the ability of the 1D turbine module to consider secondary air system modification impact on performance comparing calculated results to experimental ones. Literature correlations tuning on proprietary experimental results could further improve the tool performance for the off-design evaluation of ASEN turbine geometries.

scholarly journals Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

2017 ◽  
Vol 121 (1245) ◽  
pp. 1758-1777 ◽  
Author(s):  
Elias Tsoutsanis ◽  
Yi-Guang Li ◽  
Pericles Pilidis ◽  
Mike Newby
Keyword(s):  
Experimental Data ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Simulation Software ◽  
Design Performance ◽  
Turbine Performance ◽  
Map Generation ◽  
Compressor Map ◽  
Performance Adaptation ◽  
Improved Accuracy

ABSTRACTOne of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics.

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.

Marine Gas Turbine Performance Model for More Electric Ships

2011 ◽  
Author(s):  
George M. Koutsothanasis ◽  
Anestis I. Kalfas ◽  
Georgios Doulgeris
Keyword(s):  
Gas Turbine ◽  
Fuel Consumption ◽  
Diesel Engines ◽  
Gas Turbines ◽  
Electric Propulsion ◽  
Operating Conditions ◽  
Prime Mover ◽  
Creep Life ◽  
Hull Fouling ◽  
Turbine Performance

This paper presents the benefits of the more electric vessels powered by hybrid engines and investigates the suitability of a particular prime-mover for a specific ship type using a simulation environment which can approach the actual operating conditions. The performance of a mega yacht (70m), powered by two 4.5MW recuperated gas turbines is examined in different voyage scenarios. The analysis is accomplished for a variety of weather and hull fouling conditions using a marine gas turbine performance software which is constituted by six modules based on analytical methods. In the present study, the marine simulation model is used to predict the fuel consumption and emission levels for various conditions of sea state, ambient and sea temperatures and hull fouling profiles. In addition, using the aforementioned parameters, the variation of engine and propeller efficiency can be estimated. Finally, the software is coupled to a creep life prediction tool, able to calculate the consumption of creep life of the high pressure turbine blading for the predefined missions. The results of the performance analysis show that a mega yacht powered by gas turbines can have comparable fuel consumption with the same vessel powered by high speed Diesel engines in the range of 10MW. In such Integrated Full Electric Propulsion (IFEP) environment the gas turbine provides a comprehensive candidate as a prime mover, mainly due to its compactness being highly valued in such application and its eco-friendly operation. The simulation of different voyage cases shows that cleaning the hull of the vessel, the fuel consumption reduces up to 16%. The benefit of the clean hull becomes even greater when adverse weather condition is considered. Additionally, the specific mega yacht when powered by two 4.2MW Diesel engines has a cruising speed of 15 knots with an average fuel consumption of 10.5 [tonne/day]. The same ship powered by two 4.5MW gas turbines has a cruising speed of 22 knots which means that a journey can be completed 31.8% faster, which reduces impressively the total steaming time. However the gas turbine powered yacht consumes 9 [tonne/day] more fuel. Considering the above, Gas Turbine looks to be the only solution which fulfills the next generation sophisticated high powered ship engine requirements.

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