Robust Optimization of the Secondary Air System Axial Bearing Loads with the Labyrinth Clearance Uncertainty

Abstract

The secondary air system of a gas turbine engine is a complex fluid network that is sensitive to geometric variations in the flow elements caused by manufacturing, assembly, and operating conditions. Geometric uncertainty of the flow elements is a major cause of secondary air system failure. To reduce the performance uncertainty due to geometric uncertainty, this paper constructs a probabilistic analysis method that couples the primary air system and the secondary air system and proposes a robust optimization mathematical model for the rotor axial bearing loads of the secondary air system, using the control of the rotor axial bearing loads as an example. A case study is presented to demonstrate the application of the proposed robust optimization method in controlling the scatter of axial bearing loads. Probabilistic analysis and sensitivity analysis are performed for the original design scheme and the robust optimization design scheme of the secondary air system. The results show that the standard deviation of the high-pressure axial bearing loads in the robust optimization design scheme is 72.9% lower than that in the original design scheme under the same uncertainty distribution of the labyrinth clearance. In addition, the sensitivity of the axial bearing loads to the labyrinth clearance is reduced. Thus, it is demonstrated that the proposed robust optimization method can effectively reduce the functional scatter and the sensitivity to geometric variations in the gas turbine secondary air system.