Steady-State Experimental and Meanline Study of an Asymmetric Twin-Scroll Turbine at Full and Unequal and Partial Admission Conditions 2018-01-0971
The use of twin-scroll turbocharger turbines has gained popularity in recent years. The main reason is its capability of isolating and preserving pulsating exhaust flow from engine cylinders of adjacent firing order, hence enabling more efficient pulse turbocharging. Asymmetrical twin-scroll turbines have been used to realize high pressure exhaust gas recirculation (EGR) using only one scroll while designing the other scroll for optimal scavenging. This research is based on a production asymmetrical turbocharger turbine designed for a heavy duty truck engine of Daimler AG. Even though there are number of studies on symmetrical twin entry scroll performance, a comprehensive modeling tool for asymmetrical twin-scroll turbines is yet to be found. This is particularly true for a meanline model, which is often used during the turbine preliminary design stage. This study presents the development of a generalized meanline model for a twin-scroll turbine, which can be used in the early design stages, concentrating on asymmetrical scrolls. The improvements from the previous meanline model, i.e., the inlet duct and interspace model, in order to enable asymmetrical scroll prediction is described. The latter is based on the popular theory of turbomachinery wakes mixing, adopted from literature. The model is validated against experimental cold gas stand data under equal and unequal-admission conditions. Comparison between the model and experiments indicates the importance of the inlet duct and interspace model between the scrolls in obtaining satisfactory predictions across different admission conditions, due to the non-symmetrical features between the scrolls.
Citation: Palenschat, T., Mueller, M., Rajoo, S., Chiong, M. et al., "Steady-State Experimental and Meanline Study of an Asymmetric Twin-Scroll Turbine at Full and Unequal and Partial Admission Conditions," SAE Technical Paper 2018-01-0971, 2018, https://doi.org/10.4271/2018-01-0971. Download Citation
Torsten Palenschat, Markus Mueller, Srithar Rajoo, Meng Soon Chiong, Peter Newton, Ricardo Martinez-Botas, Feng Xian Tan
Daimler AG, Universiti Teknologi Malaysia, Imperial College London