Numerical Study of High-Altitude Relight for an Aviation Gas-Turbine Engine

The altitude relight capability of an aero-engine is a critical requirement that defines the operational flight envelope of the engine. Regulatory requirements from the Federal Aviation Administration (FAA) and European Union Aviation Safety Agency (EASA) ask to establish the altitude and airspeed envelope for in-flight engine restarting and adherence to engine performance. Further, engine manufacturers are changing combustor designs to meet aggressive goals that limit the emission of nitrogen oxides (NOx). While these design changes help reduce the NOx formation, they can be problematic for restart capabilities at high altitudes. Therefore, the engine design process becomes a complex optimization problem with conflicting goals. Test-rig data can provide insights into the performance; however, using testing to explore the entire design space is challenging, expensive, and sometimes infeasible. In this scenario, high fidelity computational fluid dynamics (CFD) simulations can bridge this gap and are, therefore, widely evaluated by designers and simulation engineers. Such simulations need to resolve flow structures, spray distribution, and ignition processes to predict the high-altitude relight accurately. Moreover, no, or limited parameter adjustments should be required for correctly predicting the relight outcome across different operating conditions. In this work, numerical simulations are performed to predict an aviation gas-turbine combustor’s relight performance, operating under different conditions, including sea level and 40000 ft operation. The CFD simulations are performed using the unsteady RANS approach for turbulence, solution-adaptive meshing, and finite-rate kinetics for the combustion modeling that tracks the flame propagation during and after the spark event. The results are encouraging and predict accurate behavior of lighting and not lighting operating conditions consistent with the light/no-light outcomes from the experimental tests. The simulation methodology, best practices, and obtained results are discussed in this paper.