Multidisciplinary Optimization of a Turbocharger Radial Turbine

Abstract

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.

Design and performance prediction of radial ORC turboexpanders

“…In fact, it is still possible to achieve high efficiencies also with a number of rotor blades much lower than that proposed by Eq. (13). Within the considered field of flow coefficient (typical of radial turboexpanders, 0.08 < U < 0.22), no significant differences in the ''optimal'' number of blades was found for the different investigated fluids (R134a shows the lowest optimal number of blades, whereas Cyclohexane shows the highest one, for both IFR and IFG geometries).…”

“…The optimization of the thermodynamic cycle, with special reference to fluid selection, has been studied widely in the last years. Fluid-dynamic design of turbo-expanders can take advantage of the availability of modern CFD methods [13,14]; however, there is a need for preliminary design methods, and of modeling tools capable of predicting the off-design performance (which is determined, for example, by the variation of the resource for solar-driven EGS, or by variation of ambient temperature in geothermal or heat recovery applications). In the field of low power output (i.e.…”

Constrained global optimization via a DIRECT-type constraint-handling technique and an adaptive metamodeling strategy

“…Finally, it is notable that eDIRECT-C is developed for expensive constrained problems, in which a simulation of the problem may require several hours or even days, e.g., the multidisciplinary simulation of a turbocharger radial turbine (Mueller et al 2013). Hence, for practical engineering problems, the running time of eDIRECT-C is much smaller than the computing time spent on simulations.…”

Design and performance analysis of radial-inflow turboexpander for OTEC application

“…However, to improve the aerodynamic performance of a turbine a detailed computational study is required [12]. NASA technical reports give fundamental design approach of radial turbine for aerospace application based on experimental work with helium and air which were treated as ideal gas [13e17].…”