The aim of this study is to present a fully automated computational fluid dynamics-based optimization chain, implementing a radial basis function meta-model combined with an improved Latin hypercube design of experiments strategy. The objective function (aerodynamic performance) is evaluated through computational fluid dynamics calculations by using the commercial code ANSYS-CFX. The optimization strategy is hybridization between a stochastic bi-objective nondominated sorting genetic algorithm and a gradient-based method known as modified method of feasible direction to get benefit from their combined capabilities. The testing of this optimization chain consisted in finding the optimal operating conditions of an airfoil NACA0012. This methodology may help to a great extent in the better exploration of the design space and to guide numerical and experimental studies to the potentially optimal design parameters.

Nomenclature C = work output coefficient Cp = specific heat, kJ/kgK e = polytropic efficiency F = objective function f = fuel-to-air ratio gX = inequality constraint _ m = mass flow, kg/s H = altitude, m Lcv = fuel heating value, kJ/kg M = Mach number P = pressure, Pa T = temperature, K V = speed, m/s Wsp = specific power, kW=kg=s X = design variables = isentropic index = isentropic efficiency # r = throttle ratio = total pressure ratio = total temperature ratio Subscripts C, c = compressor, core Cr = cruise d = diffuser f = fuel g = gear H = high pressure L = low pressure m = mechanical n = nozzle prop = propeller r = ram T = turbine t = total To = takeoff

This article presents a Pareto approach to design for the optimal performance of four configurations of turboprop engines matching the power requirements of a class of propellerdriven aircrafts. In these bi-objective optimizations of the thermal cycle parameters, the powerspecific fuel consumption is minimized and the specific power is maximized while maintaining the power levels and limiting the temperature of the power turbine blades. For this purpose, a multi-objective evolutionary optimization algorithm called non-dominated sorting genetic algorithm is used. To avoid engine performance deterioration and constraint violation at extreme operating conditions, the objective functions and constraints are evaluated at both design and off-design conditions. The trade-off surfaces representing the sets of alternative solutions are obtained based on the Pareto optimality. By considering additional subjective criteria, three design points are proposed for each engine configuration.(TIT) of about 190 • F [2], which required air-cooling in the first turbine stage.At early stages in the design process, the selection of the best turboprop configuration ensuring optimum performance is an intricate task because of many involved design parameters and constraints. Several studies were performed to determine the optimal propulsion cycle and many of them were basically parametric analyses. Brooks and Hirschkron [3] proposed, for a commuter aircraft, an overall pressure ratio (CPR) of 17 and TIT of 2300 • F. Similarly, Hirschkron and Davis [4] carried out a study of advanced turboprops in the 5000-6000 hp class and reached a CPR of 22 and a TIT of 2400 • F. In fact, the optimization tools become inevitable to explore the whole design space and produce feasible solutions without recourse to extensive parameter variations, as used in the basic parametric studies.Owing to the facts that maximizing the power, minimizing the fuel consumption, while maintaining the power levels, or maximizing the engine life by reducing the temperature of the power turbine blades, while maintaining the power levels, are important objectives in the turboprop applications, thus it is very important to make trade-offs between all design criteria and constraints. When combining these objectives, which

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