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Thermodynamic Optimization of Turboshaft Engine using Multi-Objective Genetic Algorithm

Authors: S. Farahat, E. Khorasani Nejad, S. M. Hoseini Sarvari


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: Genetic Algorithm, multi-objective, Turboshaft Engine

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[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.