Multidisciplinary Optimization of a Turbocharger Radial Turbine

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.