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.

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.

Improved Multiple Point Nonlinear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Vol 134 (3) ◽  
Author(s):  
Y.G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Design Point ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A nonlinear multiple point genetic algorithm based performance adaptation developed earlier by the authors using a set of nonlinear scaling factor functions has been proven capable of making accurate performance predictions over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain the optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of the trial and error process. In this paper, an improvement on the present adaptation method is presented using a least square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the least square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

Micro-Turbine Design Point Definition Using Genetic Algorithm With Single and Multi-Objective Optimization

2012 ◽  
Author(s):  
Diogo F. Cavalca ◽  
Cleverson Bringhenti
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Pressure Ratio ◽  
Inlet Temperature ◽  
Specific Work ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Design Point ◽  
Multi Objective ◽  
Engine Design

During a gas turbine development phase an important engineer task is to find the appropriate engine design point that meet the required specifications. This task can be very arduous because all possible operating points in the gas turbine operational envelope need to be analyzed, for the sake of verification of whether or not the established performance might be achieved. In order to support engineers to best define the engine design point that meet required performance a methodology was developed in this work. To accomplish that a computer program was written in Matlab®. In this program was incorporated the thermoeconomic and thermodynamic optimization. The thermodynamic calculation process was done based in enthalpy and entropy function and then validated using a commercial program. The methodology uses genetic algorithm with single and multi-objective optimization. The micro gas turbine cycle chosen to study was the recuperated. The cycle efficiency, total cost and specific work were chosen as objective functions, while the pressure ratio, compressor and turbine polytropic efficiencies, turbine inlet temperature and heat exchange effectiveness were chosen as decision variables. For total cost were considered the fixed costs (equipment, installation, taxes, etc.) and variable costs (fuel, environmental and O&M). For emissions were taken into account the NOx, CO and UHC. An economic analysis was done for a recuperated cycle showing the costs behavior for different optimized design points. The optimization process was made for: single-objective, where each objective was optimized separately; two-objectives, where they were optimized in pairs; three-objectives, where it was optimized in trio. After, the results were compared each other showing the possible design points.

scholarly journals Assessment of a Wave Rotor Topped Demonstrator Gas Turbine Engine Concept

1996 ◽  
Author(s):  
Philip H. Snyder ◽  
Raymond E. Fish
Keyword(s):  
Gas Turbine ◽  
Engine Performance ◽  
Gas Turbine Engine ◽  
Wave Rotor ◽  
Turbine Engine ◽  
Design Point ◽  
Engine Design ◽  
Engine Concept ◽  
Engine Cycle

A wave rotor topped gas turbine engine has been identified which incorporates five basic requirements of a successful demonstrator engine. Predicted performance maps of the wave rotor cycle have been used along with maps of existing gas turbine hardware in a design point study. The effects of wave rotor topping on the engine cycle and the subsequent need to rematch compressor and turbine sections in the topped engine are addressed. Comparison of performance of the resulting engine is made on the basis of wave rotor topped engine versus an appropriate baseline engine using common shaft compressor hardware. The topped engine design clearly demonstrates an improvement in shaft horsepower and SFC. Predicted off design part power engine performance for the wave rotor topped engine is presented including that at engine idle conditions. Operation of the engine at off design is closely examined with wave rotor operation at less than design burner outlet temperatures and rotor speeds. Challenges remaining in the development of a demonstrator engine are addressed.

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.

A Genetic Algorithm Approach to Estimate Performance Status of Gas Turbines

2008 ◽  
Author(s):  
Y. G. Li
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Performance Status ◽  
Engine Performance ◽  
Gas Turbine Engine ◽  
Accurate Estimation ◽  
Performance Parameters ◽  
Turbine Engine ◽  
Design Point

Accurate estimation of performance status of a gas turbine engine at certain ambient and operating condition based on measured gas path parameters is very important for both engine designers and users alike. It could be a very challenging task for engine performance engineers to estimate the value of component design parameters in order to match measured gas path parameters when the number of design point component parameters and the number of measurable performance parameters become large. Such status estimation can be used to distinguish the performance difference among fleet engines and build accurate engine models at an artificial design point for individual engines, which is also crucially important for gas path diagnostic analysis. In this paper, a gas turbine design point performance adaptation approach based on the integration of gas turbine thermodynamic performance modelling and a Genetic Algorithm has been developed in order to estimate the design point component parameters and match the available gas path measurements of real engines. 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 measurable performance parameters may be thrust and specific fuel consumption for aero engines, shaft power and thermal efficiency for industrial engines, gas path pressures and temperatures, etc. The developed adaptation approach has been applied to a design point performance status estimation 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 robust in producing a model engine that matches the actual engine performance with acceptable computation speed. Theoretically the developed techniques can be applied to different gas turbine engines.

Off-design performance analysis of hybridised aircraft gas turbine

2019 ◽  
Vol 123 (1270) ◽  
pp. 1999-2018 ◽  
Author(s):  
X. Zhao ◽  
S. Sahoo ◽  
K. Kyprianidis ◽  
J. Rantzer ◽  
M. Sielemann
Keyword(s):  
Power System ◽  
Gas Turbine ◽  
Power Flow ◽  
Flow Direction ◽  
Electrical Power ◽  
Engine Performance ◽  
Power Input ◽  
Electrical Power System ◽  
Design Point ◽  
Design Performance

ABSTRACTAn advanced geared turbofan with year 2035 technology level assumptions was established and used for the hybridisation study in this paper. By boosting the low-speed shaft of the turbofan with electrical power through the accessory gearbox, a parallel hybrid concept was set up. Focusing on the off-design performance of the hybridised gas turbine, electrical power input to the shaft, defined as positive hybridisation in this context, generally moves the compressor operation towards surge. On the other hand, the negative hybridisation, which is to reverse the power flow direction can improve the part-load operations of the turbofan and minimise the use of compressor handling bleeds. For the pre-defined mission given in the paper, negative hybridisation of descent, approach and landing, and taxi operations with 580 kW, 240 kW and 650 kW, respectively was found sufficient to keep a minimum compressor surge margin requirement without handling bleed.Looking at the hybridisation of key operating points, boosting the cruise operation of the baseline geared turbofan is, however, detrimental to the engine efficiency as it is pushing the cruise operation further away from the energy optimal design point. Without major modifications to the engine design, the benefit of the hybridisation appears primarily at the thermomechanical design point, the hot-day take-off. With the constraint of the turbine blade metal temperature in mind, a 500kW positive hybridisation at hot-day take-off gave cruise specific fuel consumption (SFC) reduction up to 0.5%, mainly because of reduced cooling flow requirement. Through the introduction of typical electrical power system performance characteristics and engine performance exchange rates, a first principles assessment is illustrated. By applying the strategies discussed in the paper, a 3% reduction in block fuel burn can be expected, if a higher power density electrical power system can be achieved.

Improved Multiple Point Non-Linear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Non Linear ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A non-linear multiple point Genetic Algorithm based performance adaptation developed earlier by the authors using a set of non-linear scaling factor functions has been proven capable of making accurate performance prediction over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of trial and error process. In this paper, an improvement on the present adaptation method is presented using a Least Square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the Least Square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

A New Scaling Method for Component Maps of Gas Turbine Using System Identification

2002 ◽  
Author(s):  
Changduk Kong ◽  
Jayoung Ki ◽  
Myoungcheol Kang
Keyword(s):  
Gas Turbine ◽  
Engine Performance ◽  
Operating Conditions ◽  
Scaling Method ◽  
Design Point ◽  
The Real ◽  
Simulated Performance ◽  
Flight Envelope ◽  
Scaling Methods ◽  
Performance Error

A scaling method for characteristics of gas turbine components using experimental data or partially given data from engine manufacturers was newly proposed. In case of currently used traditional scaling methods, the predicted performance around the on-design point may be well agreed with the real engine performance, but the simulated performance at off-design points far away from the on-design point may not be well agreed with the real engine performance generally. It would be caused that component scaling factors, which were obtained at on-design point, is also used at all other operating points and component maps are derived from different known engine components. Therefore to minimize the analyzed performance error in the this study, firstly component maps are constructed by identifying performances given by engine manufacturers at some operating conditions, then the simulated performance using the identified maps is compared with performances using currently used scaling methods. In comparison, the analyzed performance using the currently used traditional scaling method was well agreed with the real engine performance at the on-design point but had maximum 12% error at off design points within the flight envelope of a calculation example turboprop engine. However the performance result using the newly proposed scaling method had maximum 6% reasonable error even at all flight envelope.

Sign in / Sign up

Export Citation Format

Share Document