Aeroengine transients in altitude test facility

Sammanfattning

Altitude test facilities offer a safe and reliable way to perform a wide variety of tests on aircraft engine. Among them, aeroengine transients represent a necessary step in the certification and qualification of engines. Mission requirements for military aircraft impose fast variations of the engine regime requiring large and complex testing facilities. These facilities are expected to allow large variations of mass flow rate in a short period: from 25 𝑘𝑔/𝑠 to about 100 𝑘𝑔/𝑠 in less than 10 seconds. Besides, the facilities must perform these fast transients at pressure conditions above and below atmospheric pressure, thus requiring large industrial equipment and machinery. In this context, this study aims to create a physical model of the air network at Direction Générale de l’Armement Essais Propulseurs (DGA EP) to replicate aeroengine transient testing of a military aircraft engine, using a 0D/1D simulation software: Simulink. The model relies on the geometry of the network, previous studies carried out on the centre and test data provided by DGA EP. Elements of the upstream part of the test bed (pipes, bends, grid…) and the regulation system (valves, bypass network) are represented in the model. The modelling of Plenum C is put aside because of the complex phenomena occurring inside, which would be too demanding considering the time allowed for this project. A two-dimensional axisymmetric geometry of a test bed was created and a transient manoeuvre is finally simulated using Computational Fluid Dynamics (CFD) to observe other aerodynamic phenomena occurring during testing. Results are compared with the 0D/1D simulation software to assess the validity of the linearization of fluid mechanics equations done by Simulink. The independence of the position of the sensors inside Plenum A is also shown in this study. This project has shown the ability to reproduce test data during aeroengine transient manoeuvres using Simulink. Further work on the model could allow making predictions on the behaviour of the network leading to improvements in transient testing at DGA EP by adapting regulation strategies.