Optimization of an Asymmetric Twin Scroll Volute Turbine under Pulsating Engine Boundary Conditions

Future CO₂ emission legislation requires the internal combustion engine to become more efficient than ever. Of great importance is the boosting system enabling down-sizing and down-speeding. However, the thermodynamic coupling of a reciprocating internal combustion engine and a turbocharger poses a great challenge to the turbine as pulsating admission conditions are imposed onto the turbocharger turbine. This paper presents a novel approach to a turbocharger turbine development process and outlines this process using the example of an asymmetric twin scroll turbocharger applied to a heavy duty truck engine application. In a first step, relevant operating points are defined taking into account fuel consumption on reference routes for the target application. These operation points are transferred into transient boundary conditions imposed on the turbine. These pulsating admission conditions to the turbocharger turbine are analyzed and subsequently discretized using the method of quasi-steadiness to avoid numerically very expensive unsteady CFD simulations. Following, an automated in-house developed workflow based on a parameterized model of the entire turbine stage is introduced and described. The parameterization is based on design parameters linked to aerodynamic properties, hence it is not limited to one specific geometry but rather able to represent a large variety of designs with comparatively few input parameters. Concluding, a meta-model based multi-disciplinary and multi-objective numerical optimization is performed to obtain the best geometry possible. The optimization objectives are linked to a perfect turbine-compressor matching with an existing benchmark compressor stage and regards to the engine’s air fuel ratio and exhaust gas recirculation requirements. The entire optimization is based on numerical methods, that is, a CFD study. However, the numerical models used throughout the paper are validated against experimental data to ensure the quality and accuracy of the predictions regarding its behavior in an experimental setup.