gas turbine blades
Recently Published Documents

This article presents issues concerning the relationship between the degradation of the coating of gas turbine blades and changes in the color of its surface. Conclusions were preceded by the determination of parameters characterizing changes in the technical condition of protective coatings made based on a metallographic examination that defined the morphological modifications of the microstructure of the coating, chemical composition of oxides, and roughness parameters. It has been shown that an increased operating time causes parameters that characterize the condition of the blades to deteriorate significantly. Results of material tests were compared with those of blade surface color analyses performed using a videoscope. Image data were represented in two color models, i.e., RGB and L*a*b* with significant differences being observed between parameters in both representations. The study results demonstrated a relationship between the coating degradation degree and changes in the color of the blade’s surface. Among others, this approach may be used as a tool to assess the condition of turbine blades as well as entire gas turbines.

An experimental study was conducted to measure the heat transfer and pressure drop in a rectangular channel emphasizing a compound structure to improve the cooling performance of gas turbine blades. W shaped, semicircular, and multi semicircular shaped ribs with dimples are studied and applied to a lower surface of a channel. The experiment was carried out at a Reynolds number ranging from 10,000 to 32,000 and the ratio of pitch (P) to height (e) of the rib was 10. Also, the ratio of rib height (e) to channel hydraulic diameter (Dh) was 0.187  and the dimple depth (δ) to dimple diameter (D) ratio was 0.2. It was observed that the combination of ribs and dimple channel (compound channel) has an average of 23 %  more heat transfer than the ribbed channel. W rib compound channel shows the highest thermal performance and enhanced up to 30 % more heat transfer than semi and multi-semicircular compound channel. friction rise was observed in the compound channel compared to the ribbed channel.

Abstract Accurate thermal prediction of gas turbine blades is essential to ensure successful operation throughout the design life. Large Gas turbines operate in different conditions based on customer requirements, due to which turbine blades are subjected to variations in thermal loading conditions. Simulating this behavior using conventional finite element modeling involves detailed and time-consuming analyses for calculation of blade temperature, which can be further utilized to assess cyclic and creep life. This paper deals with developing and utilizing machine learning based surrogate models to predict the sectional temperature (output) of a radially cooled blade. The surrogate models are developed to predict the output using turbine inlet temperature, hot gas mass flow, cooling air temperature and cooling air mass flow as input to the machine learning (ML) model. All thermal parameters for ML model have been obtained from CFD based 3D thermal calculations. A comparative study is presented between linear regression, decision tree, random forest, and gradient boost ML models, to select the model with the least mean absolute error. Additionally, hyperparameter optimization is performed using grid search to minimize the error. The results show that the linear regression-based model outputs the least mean absolute error of 6.5°C and the highest dependence of the output is on the turbine inlet temperature, followed by the cooling air temperature. The findings show a good agreement between the predicted output of the surrogate model and multi-dimensional physics based thermal calculations, while offering a considerate reduction in analysis time.

Abstract Gas turbine blades and vanes face very severe operating conditions - high temperature and pressure which necessitates the creation of complex cooling and component designs, resulting in high computational cost. The ability to predict cyclic failure in these components is therefore a critical activity that has been historically performed using 3D commercial finite element (FE) codes for baseload conditions. However, these codes take substantial time and resources which restricts their application in failure prediction at variable operating conditions. Newer data-driven techniques such as machine learning (ML) provide a valuable tool that can be utilized to predict the occurrence of cyclic failure for these conditions with minimal time and resource requirement. In this paper, a machine learning based surrogate model is developed to predict the cyclic failure of a radially cooled turbine blade. The features used as input to machine learning model are turbine inlet temperature, coolant inlet temperature, hot gas mass flow rate, cooling air mass flow rate and blade materials. The output for the model is a binary variable depicting the incident of component failure. 70% of the FE data points are used to train the ML model while the remaining are used for testing. A comparative study between Logistic Regression, Random Forest, K-nearest neighbor, and Support Vector Machine (SVM) was performed to select the most accurate algorithm for the classification model. Finally, the results show that the Random Forest and SVM algorithms predicts failure with the highest f-1 score of 0.92. The model also demonstrates that Turbine Inlet temperature has the highest importance amongst the input features followed by blade material. Additionally, this methodology offers a tremendous advantage for failure prediction by reducing analysis time from multiple hours to a few seconds, rendering this technique especially beneficial for time sensitive business decisions in the gas turbine industry.

Abstract Gas turbine blades and vanes are typically manufactured with small clearances between adjacent vane and blade platforms, termed the midpassage gap. The midpassage gap reduces turbine efficiency and causes additional heat load into the vane platform, as well as changing the distribution of endwall heat transfer and film cooling. This paper presents a low-order analytical analysis to quantify the effects of the midpassage gap on aerodynamics and heat transfer, verified against an experimental campaign and CFD. Using this model, the effects of the gap can be quantified, for a generic turbine stage, based only on geometric features and the passage static pressure field. It is found that at present there are significant losses and a large proportion of heat load caused by the gap, but that with modified design this could be reduced to negligible levels. Cooling flows into the gap to prevent ingression are investigated analytically and with CFD. Recommendations are given for targets that turbine designers should work toward in reducing the adverse effects of the midpassage gap. A method to estimate the effect of gap flow is presented, so that for any machine the significance of the gap may be assessed.

Abstract The viewing angle for optical aerothermal measurements on turbine surfaces is often limited by the turbine structure, requiring the optical system to have a large depth of field (DoF). Although the DoF can be increased by decreasing the lens aperture, this approach is impractical as a large aperture is essential to maintain an acceptable signal-to-noise ratio (SNR). To solve these problems in the optical aerothermal measurements of film-cooled gas turbine blades, an approach combining the focal-sweep method and three-dimensional (3D) reconstruction is proposed. The focal-sweep method is used to obtain all-in-focus images at an inclined viewing angle, following which the two-dimensional image is restored through 3D reconstruction. Thus, 3D point clouds with both a large DoF and high SNR can be produced. The developed method was validated via flat-plate film cooling experiments using pressure-sensitive paint at three blowing ratios of 0.4, 0.8, and 1.2, as well as three viewing angles. The measured adiabatic effectiveness contours demonstrate that the proposed method can produce all-in-focus measurements at highly inclined viewing angles, albeit at the price of slightly higher noise. In flat-plate experiments, the maximum relative difference is measured to be 6% between results obtained by conventional method at normal view and the proposed method at highly inclined view. Furthermore, the proposed method was applied to the turbine blade cascade film cooling experiment at a highly inclined viewing angle, and successfully reconstructed the 3D point cloud of the cooling effectiveness at the curved turbine blade surface.