Different active flow control techniques have been investigated in a 1.5-stage axial-flow compressor. Looking at a low-speed single-stage environment, many researchers have shown that highly loaded compressors are tip critical, showing stall inception caused by short length scale disturbances (spikes). It has been shown by several authors that these disturbances are related to the spillage of endwall flow ahead of the blading (spill forward). For the present work, different tip injection configurations were investigated in order to stabilize the near casing flow, increasing the operating range of the compressor. Stall margin improvement and the impact on stage efficiency are compared and discussed. Oil flow pictures of the casing wall above the rotor and of the stator blades as well as traverse data from pneumatic 5-hole probes show the impact of flow control on rotor and stator performance. Another method of energizing the casing wall boundary layer is the removal of low energy fluid by a circumferential slot above the rotor, which was also studied experimentally. Again, the impact on compressor operating range and efficiency, as well as flow field information collected by oil flow visualization and traverse data are discussed. Comparing the different flow control techniques, it is shown that increasing stall margin is not directly linked to stage efficiency. As described in various publications, discrete tip injection is a very powerful technique as far as range extension is concerned, but it also has substantial drawbacks such as the circumferential inhomogeneity of the rotor exit flow. These inhomogeneities may result in poor stator performance, overall resulting in a drop of stage efficiency. This problem does not occur if circumferential boundary layer removal above the rotor is used. This method however shows much less potential for increasing the operating range.
This paper shows how operational data in combination with a calibrated stream line curvature method (SCM) and fleet statistics can be used for turbine map generation. The operational data storage system of industrial gas turbines can be used, in case the customer agreed to provide the data, for fleet statistics, degradation behavior investigation or component map generation. The generation of updated component maps using operational data is mainly necessary for older gas turbines types since the available numerical gas turbine models often do not represent the current state of knowledge. The process for component map generation based on operational data incorporates several steps explained in detail in this paper. The first step is a full thermodynamic evaluation, including the calculation of relevant parameters like isentropic efficiencies of compressor and turbine. Furthermore, the compressor mass flow and the turbine inlet temperature are determined. This step is accompanied with the calculation of systematic and random uncertainties for all required performance parameters. The thermodynamic evaluation is coupled with a data validation system. This system incorporates signal checks, a statistics based anomaly detection, a Kalman filter based single fault isolation und a fuzzy logic based multiple fault isolation. After the evaluation and validation of the data, aging effects are eliminated. In the next step, data sets from different sites are consolidated and shifted to meet the fleet average. The first step in calibrating the SCM is the rasterizing of the operational data. The Jacobian matrix of the SCM for the loss factors to be calibrated is generated automatically at the raster points. Afterwards, the calibration is done taking into account measurement uncertainties as well as different systematic uncertainties for the loss factors in order to achieve the minimum variance result. The updated SCM, calibrated to actual engine data, is then used for component map generation.
This paper describes how operational data from heavy duty gas turbines can be used for component map generation. The main aspects described are the data evaluation and validation process, the applied degradation correction methodology and the component map generation using calibrated streamline curvature methods for compressor and turbine. The operational data storage system of heavy duty gas turbines can be used, in case the customer agreed to provide the data, for fleet statistics, degradation behavior investigation or component map generation. The update of existing component maps using operational data is mainly necessary for older gas turbines types since the available numerical gas turbine models do not always represent the current state of knowledge. The process of generating component maps based on operational data requires several steps which are explained in detail in this paper. The first step is the data evaluation and validation part. This step is based on a full thermodynamic evaluation including an evaluation of systematic and random uncertainties for all required performance parameters. This generated dataset is then validated using a combination of a Kalman Filter based single fault isolator and a fuzzy logic based multiple fault isolator. A short performance evaluation of this data validation system is given as well. After the validation part the operational dataset is corrected for aging effects regarding compressor and turbine performance in order to get the new and clean component characteristic. Subsequently, a validated and aging corrected high quality database for the component map generation is available. The applied steps as well as a direct comparison for the compressor efficiency prior and post aging correction are displayed. In the following steps, already existing streamline curvature methods (SCM) for compressor and turbine are adapted to the generated dataset using a probabilistic based calibration process. The applied optimization technique is identical for compressor and turbine, but two different approaches for the calibration of the loss modeling have been implemented. The compressor SCM is calibrated with a minimum set of modified loss parameters which are modeled as a function of load. For the turbine, the modifications of the loss coefficients are constant over load. This requires an increased set of loss parameters for calibration compared to the compressor. The calibration results for both components are presented and discussed in detail. The calibrated SCMs can now be used for the component map generation in order to yield high quality component maps in accordance with current fleet experience even for older gas turbine frames.
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