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SUMMARYA rotating channel with staggered pin-fins is formulated numerically and optimized for performance (heat transfer/required pumping power) using a Kriging meta-model and hybrid multi-objective evolutionary algorithm. Two design variables related to cooling channel height, pin diameter, and spacing between the pins are selected for optimization, and two-objective functions related to the heat transfer and friction loss are employed. A design of experiment is performed, and 20 designs are generated by Latin hypercube sampling. The objective function values are evaluated using a Reynolds-averaged Navier-Stokes solver, and a Kriging model is constructed to obtain a Pareto-optimal front through a multi-objective evolutionary algorithm. Rotation in a cooling channel with staggered pin-fins induces Coriolis force that causes a heat transfer discrepancy between the trailing (pressure) and leading (suction) surfaces, with a higher Nusselt number on the trailing surface. The tradeoff between the two competing objective functions is determined, and the distribution of the Pareto-optimal solutions in the design space is discussed through k-means clustering. In the optimal designs, with a decrease in spacing between the pins, heat transfer is enhanced whereas friction loss is increased.

A rotating equilateral triangular cooling channel with staggered square ribs inside the leading edge of a turbine blade has been optimized in this work based on surrogate modeling. The fluid flow and heat transfer in the channel have been analyzed using three-dimensional Reynolds-averaged Navier-Stokes (RANS) equations under uniform heat flux condition. Shear stress transport turbulence model has been used as a turbulence closure. Computational results for area-averaged Nusselt number have been validated compared to the experimental data. The objectives related to the heat transfer rate and pressure drop has been linearly combined with a weighting factor to define the objective function. The angle of the rib, the rib pitch-to-hydraulic diameter ratio, and the rib width-tohydraulic diameter ratio have been selected as the design variables. Twenty-two design points have been generated by Latin Hypercube sampling, and the values of the objective function have been calculated by the RANS analysis at these points. The surrogate model for the objective function has been constructed using the radial basis neural network method. Through the optimization, the objective function value has been improved by 21.5 % compared to that of the reference geometry.

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A parametric study has been performed to find the effects of geometric parameters on sealing effectiveness of a rim seal, which is used to prevent the hot gas ingestion into the gas turbine component and to cool the turbine disk. The flow and temperature fields in the reference rim seal which was tested by Popović and Hodson have been investigated using three-dimensional Reynolds-averaged Navier-Stokes equations. The one-equation Spalart-Allmaras turbulence model has been selected as turbulence closure. Computational results for the area-averaged sealing effectiveness have been validated compared to the experimental data at the specified values of rim seal inlet velocities (Vc,in = 11.50m/s and 16.10m/s) and leakage fractions (0.50%, 0.75% and 1.00%). Best shape of the rim seal has been found among ten different shapes of the rim seal.