CN-115577651-B - Turbine guide vane average air film cooling efficiency prediction method and system

Abstract

The invention provides a turbine guide vane average air film cooling efficiency prediction method and system, which are characterized in that main influence parameters affecting air film cooling efficiency are firstly extracted as independent variables, turbine blade average air film cooling efficiency is used as independent variables, calculation schemes of different structures and different working conditions are constructed, numerical simulation is carried out to obtain corresponding average air film cooling efficiency under each working condition, then a simulation result obtained by the numerical simulation and independent variable parameters are utilized to generate a corresponding database, finally an obtained database and a formula for predicting guide vane average air film cooling efficiency is utilized to be fitted and established, prediction errors are analyzed, and the predicted blade average air film cooling efficiency is obtained through geometric parameters and pneumatic parameters of turbine guide vanes, so that the method can be used for predicting the turbine guide vane average air film cooling efficiency of a gas turbine.

Inventors

• DONG PING
• ZHOU XU
• LI TAO
• NIU XIYING
• LIN HONGFEI
• HAN RUI
• YU ZIJIE
• ZHANG YUXUAN

Assignees

• 哈尔滨工程大学

Dates

Publication Date

20260505

Application Date

20220929

Claims (5)

1. 1. The utility model provides a turbine stator average air film cooling efficiency prediction system which characterized in that: The system comprises a model building module, a working condition building module, a simulation calculation module and a function fitting module; The model building module is used for extracting geometric parameters affecting the average air film cooling efficiency of the turbine guide vane, constructing different guide vane models according to the extracted geometric parameters, and carrying out grid division on the obtained guide vane models to calculate a required calculation domain, wherein the geometric parameters affecting the average air film cooling efficiency of the turbine guide vane comprise the diameter d 1 ,d 2 of an air film hole, the lateral distance s 1 ,s 2 of the air film hole and the distance l 1 ,l 2 of the air film hole from the tail end of a blade; The working condition establishing module is used for extracting aerodynamic parameters influencing the average air film cooling efficiency of the turbine guide vane, and establishing boundary conditions of a calculation domain of the module according to the aerodynamic influence parameter setting model of the average air film cooling efficiency so as to construct different calculation working conditions, wherein the aerodynamic parameters influencing the average air film cooling efficiency of the turbine guide vane comprise jet hole outlet Reynolds number Re jet,1 ,Re jet,2 , blowing ratio M and density ratio DR; the simulation calculation module is used for carrying out numerical simulation calculation on the calculation working conditions constructed by the working condition establishment module to obtain a gas film cooling efficiency distribution field under the corresponding working conditions, and calculating to obtain corresponding average gas film cooling efficiency; the function fitting module is used for taking the geometric parameters extracted by the model building module and the pneumatic parameters extracted by the working condition building module as inputs, taking the average air film cooling efficiency obtained by numerical simulation of the simulation calculation module as output, obtaining the values of the parameters in the function expression through function fitting, and substituting the obtained values into an empirical formula to obtain the empirical formula for predicting the average air film cooling efficiency of the turbine guide vane; The function expression in the function fitting module is as follows: In the formula, For average film cooling efficiency, A, B, C, D, E, F, G, H, I are undetermined coefficients/indices, the function fitting software used is 1stOpt, , , , , , M, DR is an independent variable, dependent variable 。
2. 2. A prediction method of a turbine vane average film cooling efficiency prediction system according to claim 1, characterized by: The method specifically comprises the following steps: Firstly, extracting geometric parameters affecting the average air film cooling efficiency of turbine guide vanes, constructing different guide vane models according to the extracted geometric parameters, and carrying out grid division on the obtained guide vane models to calculate a required calculation domain, wherein the geometric parameters affecting the average air film cooling efficiency of the turbine guide vanes comprise the diameter d 1 ,d 2 of an air film hole, the lateral distance s 1 ,s 2 of the air film hole and the distance l 1 ,l 2 of the air film hole from the tail end of a blade; Extracting aerodynamic parameters influencing the average air film cooling efficiency of the turbine guide vane, and setting boundary conditions of the first calculation domain according to the aerodynamic influence parameters of the average air film cooling efficiency so as to construct different calculation working conditions, wherein the aerodynamic parameters influencing the average air film cooling efficiency of the turbine guide vane comprise jet hole outlet Reynolds numbers Re jet,1 ,Re jet,2 , blowing ratios M and density ratios DR; Performing numerical simulation calculation on the calculated working conditions of the second structure to obtain a gas film cooling efficiency distribution field under the corresponding working conditions, and calculating to obtain corresponding average gas film cooling efficiency; Step four, taking the geometric parameters extracted in the step one and the pneumatic parameters extracted in the step two as inputs, taking the average air film cooling efficiency obtained in the numerical simulation of the step three as output, obtaining the values of the parameters in the function expression through a function fitting module, and substituting the obtained values into an empirical formula to obtain the empirical formula for predicting the average air film cooling efficiency of the turbine guide vane; The function expression in the fourth step is: In the formula, For average film cooling efficiency, A, B, C, D, E, F, G, H, I are undetermined coefficients/indices, the function fitting software used is 1stOpt, , , , , , M, DR is an independent variable, dependent variable 。
3. 3. The method according to claim 2, wherein in step three, And carrying out numerical simulation calculation through ANSYS CFX, and obtaining corresponding average air film cooling efficiency through CFD-POST calculation.
4. 4. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of claim 2 or 3 when executing the computer program.
5. 5. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the method of claim 2 or 3.

Description

Turbine guide vane average air film cooling efficiency prediction method and system Technical Field The invention belongs to the technical field of impeller machinery, and particularly relates to a method and a system for predicting average air film cooling efficiency of turbine guide vanes. Background The gas turbine is widely applied to the fields of ships, aerospace, power generation in power plants, chemical industry, metallurgical industry, energy and power engineering and the like, and is known as a bright bead on an industrial crown. As a sign of national defense, industry and technological strength, advanced world countries use it as a strategic industry for preferential development. The gas turbine, which is one of the most core components in the gas turbine, has the function of driving the compressor in addition to generating work. The current temperature of the advanced aero-engine before the turbine reaches about 2000K, which is higher than the temperature resistance limit of the metal material of the turbine blade, so that the advanced cooling technology is needed. Film cooling is an effective cooling mode. The method comprises the steps of introducing compressor gas with lower temperature from a compressor, injecting the introduced gas into main flow channel gas from gas film holes formed on the surface of a turbine blade, bending the cooling gas flow with lower temperature relative to the main flow to the movement direction of the main flow under the combined action of the gas pressure of the main flow and flow shear stress generated by Newton's internal friction law control, attaching the cooling gas flow to a certain area on the surface of the blade, isolating the surface of the blade from the main flow gas, and blocking direct convection heat exchange between the main flow gas and the surface of the blade, so that the surface of the turbine blade is protected. The hydrodynamic parameters affecting the cooling performance of the film include blowing ratio, momentum ratio, density ratio, turbulence of the main stream and Mach number of the main stream, and the geometric parameters include downstream distance from the film holes, hole spacing, kong Qingjiao, hole compound angle, length-diameter ratio, film hole diameter, shape, film hole arrangement form and the like. Therefore, the film cooling efficiency is a result of multi-parameter coupling influence, and the accurate and comprehensive prediction of the film cooling efficiency is a very difficult and complex task, and a large amount of work is required. The method has very important practical significance in accurately predicting the average film cooling efficiency in a certain range and with certain precision. Currently commonly used film cooling prediction methods include empirical formulas, neural networks, and Computational Fluid Dynamics (CFD). Among them, the empirical formula method is most studied and optimized because of early start. However, the application range of the empirical formula is narrow, only some parameters with larger influence are researched, and larger deviation easily occurs to the film cooling prediction result of the real turbine blade. Along with the recent strong fire of artificial intelligence, the neural network method is also applied to the prediction of the film cooling efficiency by researchers, and shows a better prediction effect. But neural network methods require a large number of data sets to train, and gathering enough data sets to train the neural network is also a difficult problem. The CFD method has great advantages compared with experimental research in economic cost, so the CFD method has rapid development, but the three-dimensional numerical simulation of the complex cooling structure requires great grid quantity, and the grid division workload and the calculation time brought by the CFD method bring great barriers to the design of the cooling structure. Finding a more efficient and accurate film cooling efficiency prediction method is not easy. Disclosure of Invention The invention provides a turbine guide vane average air film cooling efficiency prediction method and system, which are based on an empirical formula for predicting air film cooling efficiency summarized by the former, and the empirical formula is improved by combining a numerical simulation result to obtain a more accurate calculation formula for predicting average air film cooling efficiency, wherein the predicted average air film cooling efficiency of a blade can be obtained through geometric parameters and aerodynamic parameters of a turbine guide vane. The invention is realized by the following technical scheme: turbine guide vane average air film cooling efficiency prediction system: The system comprises a model building module, a working condition building module, a simulation calculation module and a function fitting module; the model building module is used for extracting geometric parameters affecting the average ai