Numerical Investigation of the Impact of Nozzle Endwall Clearance Distribution on Variable Nozzle Turbine Performance 2017-01-1034
As the variable nozzle turbine(VNT) becomes an important element in engine fuel economy and engine performance, improvement of turbine efficiency over wide operation range is the main focus of research efforts for both academia and industry in the past decades. It is well known that in a VNT, the nozzle endwall clearance has a big impact on the turbine efficiency, especially at small nozzle open positions. However, the clearance at hub and shroud wall sides may contribute differently to the turbine efficiency penalty. When the total height of nozzle clearance is fixed, varying distribution of nozzle endwall clearance at the hub and shroud sides may possibly generate different patterns of clearance leakage flow at nozzle exit that has different interaction with and impact on the main flow when it enters the inducer. It is possible that variation of the nozzle endwall clearances between hub side and shroud side, e.g. tiny transverse movement of nozzle vanes along their pivotal shafts, results in significant deviations in turbine aerodynamic performances at some operation conditions. In this paper, the deviations of turbine efficiency at three typical nozzle vane openings and different rotational speeds, with different distribution of nozzle endwall clearances were numerically analyzed. It was found that when the total height of nozzle clearances is fixed, changing the nozzle endwall clearance distribution between the hub and shroud sides can impact the turbine performance, and shifting clearance towards hub side can effectively improve the VNT turbine efficiency, especially at high speed ratio and small vane open positions.
Citation: Zhao, B., Hu, L., Engeda, A., and Sun, H., "Numerical Investigation of the Impact of Nozzle Endwall Clearance Distribution on Variable Nozzle Turbine Performance," SAE Technical Paper 2017-01-1034, 2017, https://doi.org/10.4271/2017-01-1034. Download Citation
Ben Zhao, Liangjun Hu, Abraham Engeda, Harold Sun
Michigan State University, Ford Motor Company, Michigan State Univeristy