In a cooperative project between the Institute of Aircraft Propulsion Systems and MTU Aero Engines GmbH, a two-stage low pressure turbine with integrated 3D airfoil and endwall contouring is tested. The experimental data taken in the altitude test-facility study the effect of high incidence in off-design operation. Steady measurements are covering a wide range of Reynolds numbers between 40,000 and 180,000. The results are compared with steady multistage CFD predictions with a focus on the stator rows. A first unsteady simulation is taken into account as well. The CFD simulations include leakage flow paths with disk cavities modeled. Compared to design operation the extreme off-design high-incidence conditions lead to a different flow-field Reynolds number sensitivity. Airfoil lift data reveals changing incidence with Reynolds number of the second stage. Increased leading edge loading of the second vane indicates a strong cross channel pressure gradient in the second stage leading to larger secondary flow regions and a more three-dimensional flow-field. Global characteristics and area traverse data of the second vane are discussed. The unsteady CFD approach indicates improvement in the numerical prediction of the predominating flow-field.
Recent publications have demonstrated the influence of unsteady work terms on the inviscid recovery of wake momentum. So far, this so-called wake differential work effect was only validated based on selected locations and time steps in turbine rotors. The magnitude of this effect over a whole blade passing cycle and the local unsteady work mechanisms causing it are still not fully understood. Using a numerical simulation, the unsteady static pressure field of a turbine rotor is assessed. Three regions are identified in which work is transfered unsteadily to the fluid, caused by the fluid interaction with the unsteady rotor pressure field. A Lagrangian analysis is performed to validate and quantify the wake differential work concept. To be representative, a large number of wake and free stream fluid particle paths are evaluated. Overall, a 7 per cent lower wake work in the rotor is identified, averaged over a whole blade passing cycle. From a particle point of view, the rotor pressure field acts as a pressure wave propagating in circumferential direction. Due to inviscid unsteady work, this pressure wave influences the stagnation enthalpy of the fluid particles. It is shown that this effect is more dominant for wake fluid, as the wake velocity is closer to the propagation velocity of the pressure wave. A mathematical model of this so-called “wake surfing effect” and the two other unteady work mechanisms reveals how the wake momentum is recovered depending on the initial wake velocity vector. If exploited well, this unsteady work mechanism could cause a reduction of wake mixing loss, leading to an increased turbine efficiency.
To ensure the quality standards in engine testing, a growing research effort is put into the modeling of full engine test cell systems. A detailed understanding of the performance of the combined system, engine and test cell, is necessary e.g. to assess test cell modifications or to identify the influence of test cell installation effects on engine performance. This study aims to give solutions on how such a combined engine and test cell system can be effectively modeled and validated in the light of maximized test cell observability with minimum instrumentation and computational requirements. An aero-thermodynamic performance model and a CFD model are created for the Fan-Engine Pass-Off Test Facility at MTU Maintenance Berlin-Brandenburg GmbH, representing a W-shape configuration, indoor Fan-Engine test cell. Both models are adjusted and validated against each other and against test cell instrumentation. A fast-computing performance model is delivering global parameters, whereas a highly-detailed aerodynamic simulation is established for modeling component characteristics. A multi-disciplinary synthesis of both approaches can be used to optimize each of the specific models by calibration, optimized boundary conditions etc. This will result in optimized models, which, in combination, can be used to assess the respective design and operational requirements.
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