Retrofittable Solutions Capability for Gas Turbine Compressors

The increasing introduction of renewable energy capacity has changed the perspective on the operation of conventional power plants, introducing the necessity of reaching extreme off-design conditions. There is a strong interest in the development and optimization of technologies that can be retrofitted to an existing power plant to enhance flexibility as well as increase performance and lower emissions. Under the framework of the European project TURBO-REFLEX, a typical F-class gas turbine compressor designed and manufactured by Ansaldo Energia has been studied. Numerical analyses were performed using the TRAF code, which is a state-of-the-art 3D CFD RANS/URANS flow solver. In order to assess the feasibility of lower minimum environmental load operation, by utilizing a reduction in the compressor outlet mass-flow rate, with a safe stability margin, two different solutions have been analyzed: blow-off extractions and extra-closure of Variable Inlet Guide Vanes. The numerical steady-state results are compared and discussed in relation to an experimental campaign, which was performed by Ansaldo Energia. The purpose is to identify the feasibility of the technologies and implementation opportunity in the existing thermal power plant fleet.

Pressure Drop Measurements and Simulations for the Protective Mesh Screen Before the Gas Turbine Compressor

This paper characterizes the pressure drop of incompressible airflow when passing by a metal mesh screen which acts as a protection from sucking foreign solid matters before the gas turbine compressor. The wire diameter is 1.2mm and the mesh number is 3. Two experiments are conducted in different time period of a day to guarantee the experimental repeatability. The experimental data are used in regression analysis to obtain a quadratic correlation between the pressure drop across the screen and the fluid velocity. Numerical simulations are utilized to investigate detailed velocity and pressure fields around the wires and the Standard k-ε turbulence model is used. The results show that the fluid suffers from around 140Pa and 250Pa total pressure drop at the velocity of 20m/s and 30m/s respectively. The pressure closely upstream of the wires is as high as 4 times of the inlet flow level, while wide negative pressure regions are observed downstream of the wires resulting from fluid stagnation, reverse flow and recirculation. The empirical correlation obtained in the paper has a high confidence level and can be used in calculating the overall pressure drop of the gas turbine air intake system.

Comparative analysis and forecasting of isentropic efficiency of gas turbine compressor with ARIMA, VAR, NARNN and ANFIS approaches

It is extremely important to monitor the status of gas turbine to ensure its safe and reliable operation. In this work, the variation trend of isentropic efficiency of compressor is analysed based on the measured data of F-class heavy-duty gas turbine in practical industrial application. The actual measured data of F-class heavy-duty gas turbine includes the data under start-stop and unstable working conditions, which cannot be directly used for calculation and analysis. To solve this problem, the data selection rules are designed and determined according to the operating conditions of gas turbine to select the data under effective working state. The isentropic efficiency of compressor is calculated based on the selected data. Then the forecasting effects of four forecasting methods on the variation trend of isentropic efficiency of compressor are studied. Four indexes, namely, symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAPE), root mean square error (RMSE), and similarity (SIM) values are utilized to evaluate the forecasting accuracy. The research results indicate that the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has better forecasting effect than Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR) and Nonlinear Autoregression Neural Network (NARNN) for this F-class heavy-duty gas turbine. Through the ANFIS method, the SIM up to 96.77%, the SMAPE and MAPE are less than 0.1, and the RMSE is only 0.1157. Therefore, the ANFIS method is suitable for forecasting the isentropic efficiency of this F-class heavy-duty gas turbine compressor.

A gas turbine thermal performance prediction method based on dynamic neural network

In order to ensure safety and reliability of energy transportation, it is necessary to understand and predict the performance of the gas turbine components. A prediction frame of the gas turbine compressor isentropic efficiency is established using the neural time series theory based on the Dynamic Neural Network. In order to obtain appropriate parameters for the network, a validation set is introduced to generalize the model. The compressor isentropic efficiency can be predicted based on the suggested model which provides an effective technical mean for the early warning of gas turbine performance. The experiment verified that the performance calculation model and the isentropic entropy efficiency prediction model based on the neural time series are effective.

Dependence of thermogasodynamic parameters of operation of a helicopter gas turbine engine on its operation and dust in the atmosphere

By scanning the blades of the impeller and the guide devices of the full-scale compressor of the helicopter gas turbine engine, a solid-state design model of the compressor with a tunable geometry in the height of the blades was developed. The empirical dependencies of the values of nonlinear wear of the blade of the 1st stage of the compressor on the operating time and the concentration of dust in the air are presented. A block diagram of the procedure for calculating the characteristics and parameters of a helicopter gas turbine compressor is presented. When calculating the flow in the gas–air path of the compressor, the numerical solution of the Navier–Stokes equations averaged by Reynolds, the finite element method in combination with the establishment of patterns of erosive wear of the blades depending on the operating conditions of the engine is used. Mathematical dependencies of the thermogasodynamic parameters of the helicopter gas turbine compressor operation on its operating time and the dustiness of the atmosphere are obtained using modeling. The obtained results can be used in the development of a methodology for automated monitoring of the wear condition of the compressor blades of a helicopter gas turbine engine according to its thermogasodynamic parameters, applied to various climatic operating conditions.

Development of an accelerated test for pitting corrosion of CUSTOM 450 by rDHM

A key application of CUSTOM 450 alloy is in the construction of gas turbine compressor blades. The study of pitting corrosion can prevent the failure of many gas turbine compressor blades. In this study, a reflective digital holography microscopy method was employed to investigate the growth of pitting corrosion in depth. To this end, a constant potential of 350 mVSCE in a 3.5 wt.% NaCl solution was applied to the specimen. The generated pits were simulated in three dimensions, and it was indicated that pitting corrosion rate was decreased as time passed. Comparing the obtained experimental data with the data gathered from the real industrial environment surrounding a compressor installation, an accelerated test was proposed. By the proposed accelerated test, it is possible to produce a pit similar to the one that will be initiated and propagated at any time in the future in real conditions.