GA-based design-point performance adaptation and its comparison with ICM-based approach

2010 ◽  
Vol 87 (1) ◽  
pp. 340-348 ◽  
Author(s):  
Y.G. Li ◽  
P. Pilidis
Keyword(s):  
Design Point ◽  
Performance Adaptation

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.

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.

Study of pressure swirl atomizer with tangential input at design point and outside of design point

2020 ◽  
Vol 32 (12) ◽  
pp. 127113
Author(s):  
Kiumars Khani Aminjan ◽  
Balaram Kundu ◽  
D. D. Ganji
Keyword(s):  
Design Point ◽  
Swirl Atomizer ◽  
Pressure Swirl ◽  
Pressure Swirl Atomizer

Design Point for Predicting Year-Round Performance of Solar Parabolic Trough Concentrator Systems

2013 ◽  
Author(s):  
Men Wirz ◽  
Matthew Roesle ◽  
Aldo Steinfeld
Keyword(s):  
Operating Conditions ◽  
Transfer Model ◽  
Parabolic Trough ◽  
Specific System ◽  
Simple Method ◽  
Operating Condition ◽  
Average Efficiency ◽  
Design Point ◽  
Yearly Average ◽  
Solar Field

Thermal efficiencies of the solar field of two different parabolic trough concentrator (PTC) systems are evaluated for a variety of operating conditions and geographical locations, using a detailed 3D heat transfer model. Results calculated at specific design points are compared to yearly average efficiencies determined using measured direct normal solar irradiance (DNI) data as well as an empirical correlation for DNI. It is shown that the most common choices of operating conditions at which solar field performance is evaluated, such as the equinox or the summer solstice, are inadequate for predicting the yearly average efficiency of the solar field. For a specific system and location, the different design point efficiencies vary significantly and differ by as much as 11.5% from the actual yearly average values. An alternative simple method is presented of determining a representative operating condition for solar fields through weighted averages of the incident solar radiation. For all tested PTC systems and locations, the efficiency of the solar field at the representative operating condition lies within 0.3% of the yearly average efficiency. Thus, with this procedure, it is possible to accurately predict year-round performance of PTC systems using a single design point, while saving computational effort. The importance of the design point is illustrated by an optimization study of the absorber tube diameter, where different choices of operating conditions result in different predicted optimum absorber diameters.

Performance Prediction for Automotive Turbocharger Compressors

1975 ◽  
Vol 189 (1) ◽  
pp. 557-565 ◽  
Author(s):  
A. Whitfield ◽  
F. J. Wallace
Keyword(s):  
Performance Prediction ◽  
Flow Analysis ◽  
Compressor Stage ◽  
Thermodynamic Models ◽  
Flow Rates ◽  
Design Point ◽  
One Dimensional ◽  
Dimensional Flow ◽  
Performance Map ◽  
The Individual

A procedure to predict the complete performance map of turbocharger centrifugal compressors is presented. This is based on a one-dimensional flow analysis using existing published loss correlations that were available and thermodynamic models to describe the incidence loss and slip factor variation at flow rates which differ from the design point. To predict the losses within the complete compressor stage using a one-dimensional flow procedure, it is necessary to introduce a number of empirical parameters. The uncertainty associated with these empirical parameters is assessed by studying the effect of varying them upon the individual losses and upon the overall predicted performance.

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