Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation

Abstract

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.

Design of an Axial Turbine for Highly Downsized Internal Combustion Engines

“…Figures 26 and 27 show the Mach number and Buri criterion distribution along the blade section. These were always checked in such a way to ensure the fulfilling of the Buri criterion and to avoid Mach number values over 1.35, as suggested by [14] in order to limit losses.…”

“…Figures 26 and 27 show the Mach number and Buri criterion distribution along the blade section. These were always checked in such a way to ensure the fulfilling of the Buri criterion and to avoid Mach number values over 1.35, as suggested by [14] in order to limit losses. It must be pointed out that since the blade is prismatic, it was decided to optimize the middle blade section airfoil, as being in between the hub and tip, it seemed to be a reasonable choice to avoid high differences in loss factors spanwise.…”

Preliminary Investigation of the Performance of an Engine Equipped with an Advanced Axial Turbocharger Turbine

“…A genetic algorithm-based optimisation technique was used for the turbine design optimisation [23]. Figure 33a,b shows the 3D model of the optimized turbine blade wheel and the turbine, respectively.…”

Integrated Aerodynamic and Structural Blade Shape Optimization of Axial Turbines Operating With Supercritical Carbon Dioxide Blended With Dopants

“…The selected geometric modelling platform was CAESES® while the numerical flow solver was MISES. Many authors have used the commercial flow solver ANSYS CFX to simulate the aerodynamic performance in their optimisation models [12][13][14][15][16] while the optimisation solver is commonly genetic algorithm (GA) and multi-objective genetic algorithm (MOGA) [17][18][19]. Various optimisation objectives have been presented through the published studies, however the common target is achieving higher aerodynamic performance.…”

“…Various optimisation objectives have been presented through the published studies, however the common target is achieving higher aerodynamic performance. Berchiolli et al [13], Klonowicz et al [14],…”