Development of a One-Dimensional Dynamic Gas Turbine Secondary Air System Model—Part I: Tool Components Development and Validation

Abstract

The Secondary Air System (SAS) of a jet engine is an open system: there is a bleed off take from the compressor — at the lowest possible pressure compatible with the sink where the flow is to be discharged —, the air then travels through the internal cavities of the engine cooling down the compressor and turbine discs and sealing and cooling bearing chambers. Eventually, the air is discharged at the turbine rims preventing the air of the main gas path from entering the internal turbomachinery cavities that would damage the turbine assembly. Ultimately the system is also primarily responsible for determining the endloads exerted on the turbine discs. The amount of air bled from the main gas path, although necessary, impairs the engine performance because it is purged from the main engine cycle. In order to quantify and minimise its pernicious effect, the usual practice is to model the engine SAS in steady state conditions with 1D network solvers where the net nodes represent the various components of the system. For usual engine transients it is sufficient to analyse the system performance with a quasi steady approach because the time constant of the air system is insignificant compared with the turbomachinery characteristic time. Nonetheless, the rapid changes that occur during slam accelerations or failure scenarios — particularly shaft failure events — call for a different approach to calculate the endloads fluctuations. However, to the author’s knowledge, there is not such an approach to predict the transient response of the system available in the literature hitherto. The aim of the present research is to develop a dynamic model for gas turbine secondary air systems capable of tackling the sudden changes in the flow properties that occur within the system in the aforementioned cases. The whole system is initially broken down into a series of chambers of a finite volume connected by pipes that are initially modelled in isolation and then interconnected. The resultant tool constitutes a baseline onto which further improvements and modifications will be implemented in subsequent works. This first part of the paper explains the mathematical apparatus behind the model of the two main components of the SAS — chambers and pipes — isolated. Then the assumptions made and the limitations that arise as a result are described thoroughly. Finally, the computational results obtained are successfully compared against experimental data available in the public literature for validation.