Time accurate modelling of the secondary air system response to rapid transients

Abstract

The amount of air drawn by the secondary air system (SAS) from the main gas path, although necessary, impairs the engine performance because it does not contribute to engine thrust. In order to quantify and minimize this pernicious effect, the usual practice is to model the air system with one-dimensional 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 two orders of magnitude smaller than the turbomachinery characteristic time. Nonetheless, the rapid changes that occur during certain transient or failure scenarios – particularly shaft failure events – call for a different approach to calculate the air system performance and the fluctuations of the turbomachinery endloads. However, there is no such approach available in the open literature to predict the transient response of the system. For steady-state conditions, the differential equations that govern the fluid evolution are not time discretized and thus can be solved in a relatively straightforward fashion. Moreover, unlike during transients, the flow is not supposed to reach sonic conditions anywhere within the network and, more importantly, flow reversal is not expected to occur. The aim of this study is to develop a model for gas turbine SAS dynamics capable of tackling the sudden changes in the flow properties that occur within the system in the rapid transient scenarios. 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 single-component models and their assembly are successfully validated against numerical and experimental data. The resultant modular tool constitutes a baseline into which further improvements and modifications can be integrated in subsequent works. The usefulness of the computational tool is demonstrated through the analysis of the flow evolution within the SAS and the subsequent turbine endload during a shaft failure event.

An Integrated Approach to Simulate Gas Turbine Secondary Air System

“…In the Network Solver subroutine, the mass conservation equations for each internal node is applied using Eq. (12).…”

“…Some others focused on comparing the numerical correlations derived from the 1-D calculation procedure and 3-D CFD modeling for the rotor-stator cavities pressure loss and heat transfer coefficient [11]. Gallar and his colleagues in Cranfield University published a paper to address the transient and time accurate modeling of SAS which were capable to model transient response of the system in rapid transient scenarios [12]. Zheng and Liu [13] investigated the interaction of the SAS and main gas path in a micro gas turbine utilizing 3D CFD approach.…”

Research on pressure dynamic characteristics of multi-cavity model in aero-engine secondary air system

“…The related research were as follows: the sim ulation of local transient processes in the air system by the C F D m ethod [4][5][6], dynam ic sim ulation of therm al fluid network based on volum etric m ethod [7][8][9], solution of unsteady air system based on integral m ethod [10]. H owever, m ost of the sim ulation studies did not consider the coupled oscillation between m ultiple cavities during strong transients in the air system [11][12], propagation and reflection of pressure waves in long pipelines [13].…”

Turbofan engine performance prediction methodology integrated high-fidelity secondary air system models

“…Although many papers have been published regarding the various elements encountered in a SAS, these approaches have difficulty accounting for the interactions and the change in the working state of secondary flow cannot provide timely feedback to the main path. 8 The influence of the SAS is considered in specific stations of bleeding and blending processes, which is a “black box” to the engine performance model. 9 Therefore, a coupling model is needed to analyze the turbofan engine as a robust design for the whole engine requires several runs to account for variations in boundary conditions.…”