This two-parts paper addresses the design of a U-bend for serpentine internal cooling channels optimized for minimal pressure loss. The total pressure loss for the flow in a U-bend is a critical design parameter as it augments the pressure required at the inlet of the cooling system, resulting in a lower global efficiency. In this first part of the paper the design methodology of the cooling channel is presented. The minimization of the total pressure loss is achieved by means of a numerical optimization method that uses a metamodel assisted differential evolution algorithm in combination with an incompressible Navier-Stokes solver. The profiles of the internal and external side of the bend are parameterized using piece-wise Bezier curves. This allows for a wide variety of shapes, respecting the manufacturability constraints of the design. The pressure loss is computed by the Navier-Stokes solver, which is based on a two-equation turbulence model and is available from the open source software OpenFOAM. The numerical method predicts an improvement of 36% in total pressure drop with respect to a circular U-bend, mainly due to the reduction of the separated flow region along the internal side of the bend. The resulting design is subjected to experimental validation, presented in Part II of the paper.

A multidisciplinary optimization system and its application to the design of a small radial compressor impeller are presented. The method uses a genetic algorithm and artificial neural network to find a compromise between the conflicting demands of high efficiency and low centrifugal stresses in the blades. Concurrent analyses of the aero performance and stress predictions replace the traditional time consuming sequential design approach. The aerodynamic performance, predicted by a 3D Navier–Stokes solver, is maximized while limiting the mechanical stresses to a maximum value. The stresses are calculated by means of a finite element analysis, and controlled by modifying the blade camber, lean, and thickness at the hub. The results show that it is possible to obtain a significant reduction of the centrifugal stresses in the blades without penalizing the performance.

This paper presents a numerical investigation of the heat transfer inside a micro gas turbine and its impact on the performance. The large temperature difference between turbine and compressor in combination with the small dimensions results in a high heat transfer causing a drop in efficiency of both components. Present study aims to quantify this heat transfer and to reveal the different mechanisms that contribute to it. A conjugate heat transfer solver has been developed for this purpose. It combines a three-dimensional (3D) conduction calculation inside the rotor and the stator with a 3D flow calculation in the radial compressor, turbine and gap between stator and rotor. The results for micro gas turbines of different size and shape and different material characteristics are presented and the impact on performance is evaluated.

This paper presents a multidisciplinary design optimization of a turbocharger radial turbine for automotive applications with the aim to improve two major manufacturer requirements: the total-to-static efficiency and the moment of inertia of the radial turbine impeller. The search for the best design is constrained by mechanical stress limitations, by the mass flow and power, and by aerodynamic constraints related to the isentropic Mach number distribution on the rotor blade. The optimization of the radial turbine is performed with a two-level optimization algorithm developed at the von Karman Institute for Fluid Dynamics. The system makes use of a differential evolution algorithm, an artificial neural network (ANN), and a database as a compromise between accuracy and computational cost. The ANN performance predictions are periodically validated by means of accurate steady state 3D Navier-Stokes and centrifugal stress computations. The results show that it is possible to improve the efficiency and the moment of inertia only in a few numbers of iterations while limiting the stresses to a maximum value. Based on the large number of evaluated designs during the optimization, this paper provides design recommendations of a turbocharger radial turbine at least for a good preliminary design.

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