This paper proposes a preliminary design algorithm for application of a turbocharger axial turbine, based on turbine thermodynamic analysis and the Ainley-Mathieson performance model that converges to the optimal design based on a set of input parameters and engine boundary conditions. A design space sweep was conducted, and a preliminary design was generated with a predicted total to static efficiency of 74.94%. CFD (computational fluid dynamics) was used to successfully validate the algorithm and show the preliminary design had a total to static efficiency of 73.98%. The design also produces the required power to support steady-state operation of the compressor in both free flow conditions and with a constrained pressure outlet.

In a previous paper [1], a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work, the focus is on the optimisation of this preliminary design to improve on the axial turbine efficiency using a genetic algorithm in order to make the axial turbine a more viable proposition for turbocharger turbine application. The implementation of multidisciplinary design optimisation is essential to the aerodynamic shape optimisation of turbocharger turbines, as changes in blade geometry lead to variations in both structural and aerodynamics performance. Due to the necessity to have multiple design objectives and a significant number of variables, genetic algorithms seem to offer significant advantages. However, large generation sizes and simulation run times could result in extensively long periods of time for the optimisation to be completed. This paper proposes a dimensioning of a multi-objective genetic algorithm, to improve on a preliminary blade design in a reasonable amount of time. The results achieved a significant improvement on safety factor of both blades whilst increasing the overall efficiency by 2.55%. This was achieved by testing a total of 399 configurations in just over 4 h using a cluster network, which equated to 2.73 days using a single computer.