1D Secondary Air System Modeling for Application in Engine Predesign and Multi-Fidelity

Abstract

The secondary air system (SAS) is vital for the safe operation of aero engines and longevity of high-thermally loaded parts. The extraction of secondary air in the compressor, its transfer and provision as e.g. cooling and sealing air leads to manifold interactions on whole engine level. To enhance the evaluation of novel engine concepts with noticeable changes in secondary air requirements and supply concepts, the DLR has identified the necessity to integrate the SAS in its design processes. For this purpose, a 1D network solver for holistic SAS modeling is developed to extend the simulation capabilities. After a brief review of existing approaches and software implementations for 1D SAS modeling, the different, DLR-specific implementation requirements are discussed which can be categorized as follows. First of all, the 1D network solver shall be generally applicable for studies with focus on preliminary engine design. Beyond that, it should also be applicable in life cycle-attendant processes like engine health monitoring. Further, all physics-based interfaces to adjacent components and simulation tools must be properly defined. Finally, the software implementation must embed different interfaces in order to allow multi-fidelity simulations. All of this results in a specific solver architecture which is presented in this paper. With regards to the intended application in inter-disciplinary studies, three future key capabilities are discussed: The first is the need for methods to derive first SAS geometries at early design phases, which allows the realistic prediction of the mass flow distribution within the SAS. This particularly enhances cycle and turbine design. Second, the derivation of local pressures and temperatures in the inner SAS, which may serve as boundary conditions for high-fidelity simulations. The third is full off-design capability by means of an SAS model application within the scope of all certified steady-state operating points. As conclusion, research questions for future enhancements of 1D SAS modeling are presented.