A comprehensive parametric study and multi-objective optimization of turbulent jet array impingement for uniform cooling of gas turbine blades with minimized compression power

Abstract

In the present paper, a comprehensive parametric study and multi-objective optimizations on jet array impingement cooling are conducted for mid-chord sections of gas turbine blades to maximize the heat transfer uniformity on the target plate and minimize the air compression power consumption at different desired Nusselt numbers. The validated numerical method based on RANS equations is utilized to determine the effects of Reynolds number (2500≤Re≤35000), jet spacings (3≤Px,Py≤8), and the jet-to-target distance (0.75≤Pz≤3) on air compression power (Wc), average Nusselt number (Nu‾), and heat transfer uniformity index (UI). According to the parametric study, the increase of Re improves Nu‾ and UI, while intensively increasing Wc. The effect of jet-to-target distance is a function of jet spacings; with the increase of Pz at low jet spacings, Wc,Nu‾, and UI are reduced. Although, at large spacings, Nu‾ and UI increase with Pz, and Wc is independent of Pz. Additionally, the increase of Px decreases Wc, Nu‾, and UI. Increasing Py reduces Nu‾ and Wc. But at small Px and Pz, the UI is descending; while at large Px and Pz, the UI tends to ascend. Three high-accuracy surrogate models are developed using backpropagation artificial neural networks (ANN) for estimating Wc, Nu‾, and UI for input design variables. Sobol global sensitivity analysis is also performed based on the developed models for quantifying the influence of design variables and their interactions on objective functions. As expected, the results indicate that Wc and Nu‾ are the most sensitive to Re, and UI is mainly affected by Px, whereas Py is more affecting the uniformity index rather than compression power. Finally, to find out the best design and flow conditions, optimizations are conducted by the NSGA-II algorithm. The optimal Pareto frontier and final decided solutions by TOPSIS and LINMAP methods are then demonstrated for the desired Nusselt number (NuD) of 70. The TOPSIS method indicates Px=4.70, Py=3.25, Pz=1.40, and Re = 13800 as the best compromise for optimization. The analysis of Pareto solutions in the range of NuD from 35 to 130 suggests a variety of optimal flow and geometrical arrangements for a trade-off between objective functions; therefore, at most, 50% less compression power or 5% more uniformity is approachable corresponding to the design requirements.