Multi-Objective Optimization of a U-Bend for Minimal Pressure Loss and Maximal Heat Transfer Performance in Internal Cooling Channels

Abstract

A multi-objective design optimization is performed on a U-bend in serpentine internal cooling channels. The aim is to achieve both minimized total pressure loss and maximized heat transfer ability. The optimization technique used is a two-level routine developed at the Von Karman Institute for Fluid Dynamics (VKI), featuring a Differential Evolution algorithm assisted by a metamodel, which is continuously updated during the course of the optimization process to increase its accuracy The geometries are carefully parameterized by means of Bezier curves. In total, 26 geometrical parameters are used as design variables, allowing an extensive variation of the U-bend geometries. The fluid dynamic and heat transfer performances of the selected geometries are predicted by a Reynolds-averaged Navier-Stokes solver in OpenFOAM. The result shows that dozens of optimized geometries of enhanced performances in both design objectives can be obtained after a few numbers of iterations. The enhancement ranges from roughly 12∼30% decrease in total pressure loss and a 8∼17% increase in heat flow rate. A clear trade-off between pressure loss and heat transfer is observed, allowing designers to select a compromising geometry between both criteria after the optimization process, depending on the application type of the internal cooling channel. Generally, a stronger secondary flow motion in the channel will be responsible for higher heat transfer at the cost of increased losses. A discussion is held on the geometrical features that have an impact on the secondary flow motion strength and lead to general applicable conclusions.

Aerothermal Optimization of Internal Cooling Passages Using a Discrete Adjoint Method

“…When increasing w 2 to 0.98 (Opt0), we obtain a 40.3% increase in N u. Again, we obtain much more N u increase, compared with the value (16.9%) reported by Verstraete et al [4]. However, this design is not favorable, since it generates excessive flow separation.…”

“…There are two main classes of design optimization methods: gradient-free and gradient-based methods. A number of researchers have optimized turbine internal cooling designs using the gradient-free methods such as artificial neural network, genetic algorithms, and design of experiments [3][4][5][6]. However, one of the issues with these gradient-free methods is that their computational cost scales exponentially with the number of design variables [7].…”

Pressure Loss Reduction in U-Bend Using the Adjoint Method

“…Verstraete and Li [11] followed another approach to reach the goal, getting optimum design for U-bend, using geometry parameterization using several 3D simulations. Their results showed that U-bend shape could change to minimize the pressure drop (69.2% of original) or maximize the heat transfer rate (116.93% of original).…”

Multi-objective surrogate model-based optimization of a small aircraft engine air-intake duct

“…Authors were able to simultaneously reduce flow velocity variance and pressure drop by 36% and 26%, respectively. The optimization of Ubend duct was performed by Verstraete et al (2013) with use of metamodel assisted Differential Evolution algorithm 16 in order to minimize the total pressure drop. 16 Authors compared two surrogates: based on Kriging and based on ANN to assess their performance.…”