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In this paper, the optimum parameters of a row of cylindrical film cooling holes have been investigated using a multiobjective evolutionary approach so as to achieve a compromise between film cooling effectiveness and coolant mass flow rate which are in opposite directions and compete with each other. For this purpose, chord-wise position of film holes as well as diameter and injection angles and holes spacing were chosen as design parameters. Forty samples were generated as database through CFD runs; artificial neural network (ANN) method was used to construct the surrogate model to approximate the optimization targets as functions of design parameters and genetic algorithm (GA) was used as optimizer. Design iterations were repeated seven times through the mentioned CFD-ANN-AG loop and optimum configuration, including film holes spacing, diameter, injection position and angle, based on objective function values was found. However, added row imposed an excess amount of coolant mass flow rate through the vane cavity which had a negative impact on the engine performance. Therefore, at last part of this work a thermal barrier coating layer was applied on external surfaces of the vane in order to assess the possibility of decreasing coolant mass flow rate with no additional increase on its exerted thermal loads. Keywords Artificial neural network (ANN) Á Computational fluid dynamics (CFD) Á Conjugate heat transfer (CHT) Á Film cooling Á Turbine blade Á Genetic algorithm (GA) Á Multi-objective optimization Á Thermal barrier coating (TBC)

In this paper, aerodynamic optimization of the tangential stacking line of the NASA Rotor 37 as a transonic axial flow compressor rotor is carried out using computational fluid dynamics and genetic algorithm. To cover a wide range of curves with a minimum number of design parameters, a B-spline curve with three control points at 33, 66 and 100% of the blade span is used to define the blade stacking lines. Firstly, by rotating the tangential position of the control points, different rotors have been created and are simulated using the Navier-Stokes governing equations. Then, using genetic algorithm operators, based on the adiabatic efficiency as an objective function, new blades are created and numerically simulated. This process is repeated to achieve maximum adiabatic efficiency. The comparison of the optimum blade and the original blade indicates that optimal tangential stacking line causes the shock wave to move downstream and reduce the secondary flow which has led to an improvement of about 1.7% of the adiabatic efficiency.

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