The rotating components in gas turbines are very highly stressed as a result of the centrifugal and thermal loads. One of the main functions of the secondary air system (SAS) is to ensure that the rotating components are surrounded by air that optimizes disc lifing and integrity. The SAS is also responsible for the blade cooling flow supply, preventing hot gas ingestion from the main annulus into the rotor-stator cavities, and for balancing the net axial load in the thrust bearings. Thus, the SAS design requires a multidisciplinary compromise to provide the above functions, while minimizing the penalty of the secondary flows on engine performance. The phenomenon known as rotor-stator drag or windage is defined as the power of the rotor moment acting on its environment. The power loss due to windage has a direct impact on the performance of the turbine and the overall efficiency of the engine. This paper describes a novel preliminary design approach to calculate the windage of the rotor-stator cavities in the front of a typical aero engine HP turbine. The new method is applied to investigate the impact of the SAS design parameters on the windage losses and on the properties of the cooling flows leading to the main annulus. Initially, a theoretical approach is followed to calculate the power losses of each part of the HPT front air feed system. Then, a 1D-network integral model of the cavities and flow passages of the HPT front is built and enhanced with detailed flow field correlations. The new 1D-flow network model offers higher fidelity regarding local effects. A result comparison between the theoretical calculation and the prediction of the enhanced flow network model puts forward the relevance of the local flow field effects in the design concept of the SAS. Using the enhanced 1D-flow network models, the SAS design parameters are varied to assess their influence on the windage and pumping power calculation. As a conclusion, the paper shows how the SAS design can have a significant influence on the HPT overall power and the air that is fed back into the turbine blade rows. Controlling these features is essential to bid a competitive technology in the aero engine industry.
The design and development process of an aero engine is a complex and time-consuming task that involves many disciplines and company departments with different objectives and requirements. Along the preliminary design phase, multiple concepts are assessed in order to select a competitive technology. The engine design process, which was traditionally subdivided into modular component tasks, is nowadays considered as a multi-disciplinary workflow. Having recognized the need for developing advanced turbine preliminary design tools, this work focuses on enhancing the integration of turbine design disciplines, improving the accuracy of models and speeding the time to generate models. The proposed process facilitates an automated turbine Secondary Air System (SAS) and turbine discs concept definition. Furthermore, the process of CAD models and flow network models generation is accelerated via automation of the engineering workflow. This is accomplished through a novel Java based data model, where the design of turbine discs and SAS features is captured in a programmable framework. In the application section, the preliminary design definition of a reference HP turbine subsystem is replicated using the newly developed common design environment. The automated workflow is then used to generate the corresponding CAD models, recognize the subsystem flow network, and generate the 1D flow network model. The results are then compared to the experimentally validated model of a reference engine. As conclusion, the automated workflow offers a quick and parametric model generation process, while providing a good level of fidelity for the preliminary design phase.
At the preliminary design stage of the engine design process, the behaviour and efficiency of different engine designs are investigated and evaluated in order to find a best matching design for a set of engine objectives and requirements. The prediction of critical part temperatures as well as the reduction of the uncertainty of these predictions is decisive to bid a competitive technology in aerospace technology. Automated workflows and Design of Experiments (DOE) are widely used to investigate large number of designs and to find an optimized solution. Nowadays, technological progress in computational power as well as new strategies for data handling and management enables the implementation of large DOEs and multi-objective optimizations in less time, which also allows the consideration of more detailed investigations in early design stages. This paper describes an approach for a preliminary-design workflow that implements adaptive modelling and evaluation methods for cavities in the secondary air system (SAS). The starting point for the workflow is a parametric geometry model defining the rotating and static components. The flow network within the SAS is automatically recognized and CFD and Thermal-FE models are automatically generated using a library of generic models. Adaptive evaluation algorithms are developed and used to predict values for structural, air system and thermal behaviour. Furthermore, these models and evaluation techniques can be implemented in a DOE to investigate the impact of design parameters on the predicted values. The findings from the automated studies can be used to enhance the boundary conditions of actual design models in later design stages. A design investigation on a rotor-stator cavity with axial through flow has been undertaken using the proposed workflow to extract windage, flow field and heat transfer information from adiabatic CFD calculations for use in thermal modelling. A DOE has been set up to conduct a sensitivity analysis of the flow field properties and to identify the impact of the design parameters. Additionally, impacts on the distribution of the flow field parameters along the rotating surface are recognized, which offers a better prediction for local effects in the thermal FE model.
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