Investment casting low pressure turbine blade

Considering the influence of turbine blade investment casting process accuracy, the objective fact that the real shape of Aeroengine Blades deviates from the design shape and has an uncertain impact on blade performance has attracted the attention of industry and academia in the last century. In order to improve the progressiveness of the engine, the high load turbine blade design technology has been widely used. However, with the increase of load, the influence of geometric deviation on the complex flow and aerodynamic, structural, aerodynamic and thermal performance of highly loaded blades also increases. In order to consider the influence of geometric deviation in the design and improve the robustness of turbine blade performance, deeply understand the source and statistical characteristics of geometric deviation and reveal its uncertainty influence mechanism on aerodynamic performance, it has become an international research hotspot in recent ten years.

The primary work of geometric deviation uncertainty research is to reveal the statistical characteristics of geometric uncertainty of blades, which needs a large number of turbine blade data as support. Although the modeling method based on autocorrelation function has been widely used in the related research of geometric uncertainty, it is still doubtful whether the modeling method can accurately reflect the statistical characteristics of geometric deviation of turbine blades. In the 21st century, the research of compressor blade uncertainty based on turbine blade data has been carried out. Garzon of MIT and others were the first to use computational fluid dynamics and Monte Carlo simulation (MCS) to study the uncertain effect of geometric deviation of 100 sets of machined blades on the aerodynamic performance of turbine blade profile. Lange et al. Used experimental and numerical methods to study the uncertain effect of blade geometric deviation on the aerodynamic performance of multistage compressors. Schnell et al. [4] studied the influence of turbine blade geometric deviation on the uncertainty of fan aerodynamic performance, unsteady action between rows, and structural performance. In China, Gao Limin, Yu Xianjun, etc. used 100 sets of turbine blades to study the geometric uncertainty of compressor blade profile, and analyzed the influence of geometric deviation on aerodynamic performance.

Up to now, few studies have been carried out on the geometric deviation of real turbine blades. Nilsson generated a batch of turbine blades that meet the requirements according to turbine blade tolerance requirements of GKN aerospace, and carried out aerodynamic uncertainty research. In recent years, Zou Zhengping and others have used the geometric data of 50 sets of turbine blades with a total of 200 sections to study the influence of geometric deviation on aerodynamic performance. Zhang Weihao and others used numerical simulation methods to study the influence of the change of installation angle on the performance of turbine blades and turbine performance in the environment of the whole machine. Song Liming et al. Used Kriging model to study the uncertainty of turbine blade slot width, inlet turbulence and flow angle on the end wall gas thermal performance. In addition, song Liming and Luo Jiaqi used modeling method to express the geometric deviation of turbine blades and carried out aerodynamic uncertainty research. Low pressure turbine blades are formed by investment casting. Due to the advantages of high precision and low surface roughness, investment casting has been widely used in the casting of aeroengine low-pressure turbine blades. Investment casting is a major investment casting method. Its process involves wax mold pressing, dewaxing, pouring, cleaning and polishing, preliminary inspection, heat treatment, final inspection and other main links. Due to many processes and long cycle, the shape of turbine blade will be affected by many factors such as equipment, process and materials, such as mold shrinkage, investment mold deformation, linear change during heating and cooling, alloy shrinkage and deformation during solidification, which will cause changes in turbine blade geometry. In addition, the geometric deformation of the turbine blade is the largest after pouring, and the reduction of temperature leads to shrinkage deformation, bending and torsion deformation at the same time. Even if the displacement field compensation method can effectively improve the geometric accuracy of turbine blades, the geometric deviation is difficult to be completely eliminated. Blade profile measurement is an effective method to evaluate the precision of investment casting process. Non contact optical measurement has the advantages of high precision and simple process, and has been developed rapidly in recent years. Because of its high accuracy and early development, contact measurement technology is still widely used in turbine blade geometric deviation measurement. Contact measurement requires special fixtures to clamp and locate the blades and establish the measurement coordinate system. Due to the complexity of surface process accuracy testing, the test results often contain systematic errors that the measurement coordinate system does not coincide with the design coordinate system. Therefore, the measurement errors of the measured points include position error and profile error, mainly including blade profile error, blade torsion error, bending error, etc. When the coordinate system error has a great impact, the matching accuracy of the real blade and the designed blade will be reduced, which will have a great impact on the geometric accuracy evaluation of the blade. Therefore, it is necessary to consider the influence of the main errors when matching the blades. Only by separating all kinds of errors from the comprehensive errors can we accurately study the statistical characteristics of all kinds of errors, and it can also be used to determine whether the geometric deviation of the measured blades meets the accuracy requirements. Because the influence of coordinate system error on all measured points is similar, that is, it will not change the spatial relative position between measured points, which is called overall geometric deviation in the study.

Because the investment casting process of turbine blade is quite different from that of compressor blade, and the research on the source and statistical characteristics of geometric deviation is less carried out, we will focus on the statistical characteristics of geometric deviation of precision cast low-pressure turbine blade. The significance of this study is to guide how to improve the accuracy of contact measurement technology by understanding the statistical characteristics of position error of precision cast blades of low-pressure turbine; Through understanding the statistical characteristics of profile error and its influence on the qualified rate of turbine blade, it provides guidance for the improvement of key process of blade casting; In addition, it can also provide guidance for the calibration of profile tolerance by evaluating the multi-objective impact of profile error on aerodynamic uncertainty and investment casting cost. Firstly, the geometric deviation characteristics of thousands of blades (hereinafter referred to as real blades) are analyzed, and the main characteristics of geometric deviation are determined by principal com ponent analysis (PCA). Then, in order to analyze the statistical characteristics of different blade errors, a blade geometric deviation decomposition method based on quaternion method is emphatically introduced to extract the overall geometric deviation and blade profile error. Through the comparative analysis of the profile error between the corrected blade and the real blade, the qualification rate of the low-pressure turbine blade is evaluated, and the practicability of the geometric deviation decomposition method is verified; At the same time, the establishment of statistical models of various errors can provide guidance for the later related geometric deviation uncertainty impact research.

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