Thermodynamic Optimization of Turboshaft Engine using Multi-Objective Genetic Algorithm
In this paper multi-objective genetic algorithms are
employed for Pareto approach optimization of ideal Turboshaft
engines. In the multi-objective optimization a number of conflicting
objective functions are to be optimized simultaneously. The
important objective functions that have been considered for
optimization are specific thrust (F/m& 0), specific fuel consumption
( P S ), output shaft power 0 (& /&) shaft W m and overall efficiency( ) O
η .
These objectives are usually conflicting with each other. The design
variables consist of thermodynamic parameters (compressor pressure
ratio, turbine temperature ratio and Mach number).
At the first stage single objective optimization has been
investigated and the method of NSGA-II has been used for multiobjective
optimization. Optimization procedures are performed for
two and four objective functions and the results are compared for
ideal Turboshaft engine. In order to investigate the optimal
thermodynamic behavior of two objectives, different set, each
including two objectives of output parameters, are considered
individually. For each set Pareto front are depicted. The sets of
selected decision variables based on this Pareto front, will cause the
best possible combination of corresponding objective functions.
There is no superiority for the points on the Pareto front figure,
but they are superior to any other point. In the case of four objective
optimization the results are given in tables.
[1] N. Srinivas, K. Deb, "Multi-Objective Optimization using
Nondominated Sorting in Genetic Algorithm," Evolutionary
Computation, Vol. 2, No. 3, pp. 221-248, 1994.
[2] C.M. Fonseca, P.J. Fleming, "Genetic Algorithm for Multi-Objective
optimization: Formulation, discussion and generalization, in: S.
Forrest(Ed.), proc. Of the Fifth Int. Conf. On genetic Algorithm,"
Morgan Kaufmann, San Mateo, CA, 1993, pp. 416-423.
[3] C.A. Coello, A.D. Christiansen, "MultiObjective Optimization of
Trusses using Genetic Algorithm, Compute," Structures 75, pp, 647-660,
2000.
[4] C.A. Coello Coello, D.A. Van Veldhuizen, G.B. Lamont, "Evolutionary
Algorithm for solving MultiObjective Problems," Kluwer Academic,
Dordrecht, 2002.
[5] Deb, K., Pratap, S., Agarward, S., "A Fast and Elitist Multi-Objective
Genetic Algorithm, " NSGAII, kangal report, 2001.
[6] K. Atashkari, N. Nariman-zadeh, A. Pilchi, A. Jamali, "Thermodynamic
Pareto Optimization of Turbojet Engine Using Multi-Objective Genetic
Algorithm," International Journal of Thermal Sciences, 44, PP. 1061-
1071, 2005.
[7] A. Osyezka, "Multicriteria optimization for engineering design," in: J. S.
Gero(Ed.), Design Optimization, Academic Press, New York, 1985, pp.
193-227.
[8] Mattingly, J.P., "Elements of Gas Turbine Propulsion," Mc Graw Hill,
1996.
[9] E. Khorasani Nejad, "Turboshaft Engine Performance Optimization
using Multi-Objective Genetic Algorithm," M.Sc. Dissertation, The
university of Sistan & Baluchestan, 2009.
[1] N. Srinivas, K. Deb, "Multi-Objective Optimization using
Nondominated Sorting in Genetic Algorithm," Evolutionary
Computation, Vol. 2, No. 3, pp. 221-248, 1994.
[2] C.M. Fonseca, P.J. Fleming, "Genetic Algorithm for Multi-Objective
optimization: Formulation, discussion and generalization, in: S.
Forrest(Ed.), proc. Of the Fifth Int. Conf. On genetic Algorithm,"
Morgan Kaufmann, San Mateo, CA, 1993, pp. 416-423.
[3] C.A. Coello, A.D. Christiansen, "MultiObjective Optimization of
Trusses using Genetic Algorithm, Compute," Structures 75, pp, 647-660,
2000.
[4] C.A. Coello Coello, D.A. Van Veldhuizen, G.B. Lamont, "Evolutionary
Algorithm for solving MultiObjective Problems," Kluwer Academic,
Dordrecht, 2002.
[5] Deb, K., Pratap, S., Agarward, S., "A Fast and Elitist Multi-Objective
Genetic Algorithm, " NSGAII, kangal report, 2001.
[6] K. Atashkari, N. Nariman-zadeh, A. Pilchi, A. Jamali, "Thermodynamic
Pareto Optimization of Turbojet Engine Using Multi-Objective Genetic
Algorithm," International Journal of Thermal Sciences, 44, PP. 1061-
1071, 2005.
[7] A. Osyezka, "Multicriteria optimization for engineering design," in: J. S.
Gero(Ed.), Design Optimization, Academic Press, New York, 1985, pp.
193-227.
[8] Mattingly, J.P., "Elements of Gas Turbine Propulsion," Mc Graw Hill,
1996.
[9] E. Khorasani Nejad, "Turboshaft Engine Performance Optimization
using Multi-Objective Genetic Algorithm," M.Sc. Dissertation, The
university of Sistan & Baluchestan, 2009.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:57100", author = "S. Farahat and E. Khorasani Nejad and S. M. Hoseini Sarvari", title = "Thermodynamic Optimization of Turboshaft Engine using Multi-Objective Genetic Algorithm", abstract = "In this paper multi-objective genetic algorithms are
employed for Pareto approach optimization of ideal Turboshaft
engines. In the multi-objective optimization a number of conflicting
objective functions are to be optimized simultaneously. The
important objective functions that have been considered for
optimization are specific thrust (F/m& 0), specific fuel consumption
( P S ), output shaft power 0 (& /&) shaft W m and overall efficiency( ) O
η .
These objectives are usually conflicting with each other. The design
variables consist of thermodynamic parameters (compressor pressure
ratio, turbine temperature ratio and Mach number).
At the first stage single objective optimization has been
investigated and the method of NSGA-II has been used for multiobjective
optimization. Optimization procedures are performed for
two and four objective functions and the results are compared for
ideal Turboshaft engine. In order to investigate the optimal
thermodynamic behavior of two objectives, different set, each
including two objectives of output parameters, are considered
individually. For each set Pareto front are depicted. The sets of
selected decision variables based on this Pareto front, will cause the
best possible combination of corresponding objective functions.
There is no superiority for the points on the Pareto front figure,
but they are superior to any other point. In the case of four objective
optimization the results are given in tables.", keywords = "Multi-objective, Genetic algorithm, Turboshaft
Engine.", volume = "3", number = "8", pages = "896-7", }