For gas turbine engine performance analysis, a variety of simulation tools is available. In order to minimize model development and software maintenance costs, generic gas turbine system simulation tools are required for new modeling tasks. Many modeling aspects remain engine specific however and still require large implementation efforts. One of those aspects is adaptive modeling. Therefore, an adaptive modeling functionality has been developed that can be implemented in a generic component based gas turbine environment. A single component in a system modeling environment is able to turn any new or existing model into an adaptive model without extra coding. The concept has been demonstrated in the GSP gas turbine modeling environment. An object-oriented architecture allows automatic addition of the necessary equations for the adaptation to measurement values. Using the adaptive modeling component, the user can pre-configure the adaptive model and quickly optimize gas path diagnostics capability using experimentation with field data. The resulting adaptive model can be used by maintenance engineers for diagnostics. In this paper the integration of the adaptive modeling function into a system modeling environment is described. Results of a case study on a large turbofan engine application are presented.

This paper describes the integration of advanced methods such as component zooming and distributed computing, in an object-oriented simulation environment dedicated to gas turbine engine performance modelling. A 1-D compressor stage stacking method is used to demonstrate three approaches for integrating numerical zooming in an engine model. In the first approach a 1-D compressor model produces a compressor map that is then used in the engine model in place of the default one. In the second approach the results of the 1-D analysis are passed to the 0-D component through appropriate ‘zooming’ scalars. In the final approach the 1-D compressor component directly replaces the 0-D one in the engine model. Distributed computing is realized using Web Services technology. The implementation steps for a distributed scenario are presented. The standalone compressor stage stacking method, in the form of a shared library, is placed in a remote site and can be accessed over the internet through a Web Service Operation (server side). An engine simulation is set up containing a 1-D compressor component which acts as the client for the Web Service operation. Future development of the tool’s advanced capabilities is finally discussed.

For gas turbine engine performance analysis, a variety of simulation tools is available. In order to minimize model development and software maintenance costs, generic gas turbine system simulation tools are required for new modeling tasks. Many modeling aspects remain engine specific however and still require large implementation efforts. One of those aspects is adaptive modeling. Therefore, an adaptive modeling functionality has been developed that can be implemented in a generic component-based gas turbine environment. A single component in a system modeling environment is able to turn any new or existing model into an adaptive model without extra coding. The concept has been demonstrated in the GSP gas turbine modeling environment. An object-oriented architecture allows automatic addition of the necessary equations for the adaptation to measurement values. Using the adaptive modeling component, the user can preconfigure the adaptive model and quickly optimize gas path diagnostics capability using experimentation with field data. The resulting adaptive model can be used by maintenance engineers for diagnostics. In this paper the integration of the adaptive modeling function into a system modeling environment is described. Results of a case study on a large turbofan engine application are presented.

Engine performance is a result of the interaction between individual components. Any deviation from the design geometry does not only affect the flow locally, but can also lead to significantly altered whole engine performance. Specifically for Low Pressure Turbine (LPT) vanes, erosion and subsequent refurbishment can lead to considerable changes in geometry. Following vane refurbishment, the part’s effective flow area may be measured and adjusted to meet turbine nozzle matching requirements for the engine build. Other parameters such as pressure loss and outlet flow angle are not evaluated, but rather assumed equivalent to a new part. Consequently, a large portion of vanes is rejected only after engine test, making it an expensive process. A new methodology is presented here that promises to reduce the cost of acceptance tests by predicting the performance of an engine with a refurbished vane. It follows a multi-fidelity approach involving experimental testing, zero-dimensional cycle modeling and three-dimensional Computational Fluid Dynamics (CFD). Baseline performance maps of the LPT stage with varied vane geometries are generated using CFD. The obtained performance maps are incorporated into an engine cycle model. A multiple map feature for the cycle model was developed for this purpose. It enables accessing a plurality of stored maps representing a single LPT. Using performance parameters derived from test data of the isolated vane, a performance map is generated through interpolation of the baseline maps. The expected engine performance can now be readily predicted, and a well-founded decision on acceptance of the refurbished vane made.