A turbine blade non-uniform temperature field debugging method based on multi-physical field coupling simulation optimization

Abstract

This invention relates to a method for debugging a non-uniform temperature field of turbine blades based on multi-physics field coupled simulation optimization. The steps are as follows: (1) Establish a three-dimensional model of the turbine blade and determine the working temperature range of the outer wall of the blade according to the temperature test requirements of the turbine blade; (2) Perform parametric modeling of the electromagnetic coil, analyze the sensitivity of the coil's geometric parameters, and select design variables; (3) Sample the design variables, calculate the outer wall temperature of the test section of the blade at each sampling point, repeat the calculation, and generate a proxy model from all sampling points and simulation results; (4) Select the parameter group with the smallest error with the test temperature of the dangerous point within the parameter group that meets the temperature range requirements of the test point, and use it as the design parameters of the electromagnetic coil; (5) Use the parameters in step (4) to manufacture the electromagnetic coil using 3D printing technology. This invention saves a lot of test costs, improves the safety of test operations, and improves test efficiency.

Description

Technical Field

[0001] This invention belongs to the field of aerospace engine technology, specifically relating to a method for adjusting the non-uniform temperature field of turbine blades based on multi-physics field coupled simulation optimization. Background Technology

[0002] When operating under high temperature and high pressure, turbine blades are prone to failure due to a combination of low-cycle fatigue, thermal fatigue, and creep-induced thermomechanical fatigue, high-cycle fatigue caused by vibration, oxidation, and damage from foreign objects. For single-crystal blades, the primary failure mode is thermomechanical fatigue, making the assessment of their thermomechanical fatigue life crucial.

[0003] Currently, the thermomechanical fatigue testing technology for single-crystal blades using electromagnetic induction heating requires manual adjustment of the coil shape based on experimental experience, which is not only highly dangerous but also inefficient. This invention can determine the coil design parameters that satisfy the temperature field distribution at the test section through multiphysics numerical simulation combined with adaptive optimization, saving significant experimental costs.

[0004] The existing literature, "Wang Rongqiao, Jing Fulei, Hu Dianyin. Thermomechanical fatigue testing technology for single-crystal turbine blades [J]. Journal of Aerospace Power, 2013, 28(02):252-258," explores a method for adjusting the temperature field of turbine blades that requires heating the blades with coils of different shapes and gradually improving the coil shape, resulting in low experimental efficiency. This invention can improve experimental efficiency by using multiphysics numerical simulation combined with adaptive optimization to determine the coil design parameters that satisfy the temperature field distribution of the test section. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a method for debugging the non-uniform temperature field of turbine blades based on multi-physics field coupled simulation optimization, which saves test costs and improves the efficiency of thermomechanical fatigue testing of aero-engine turbine blades.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for adjusting the non-uniform temperature field of turbine blades based on multiphysics coupling simulation optimization includes the following steps:

[0008] Step (1) Establish a three-dimensional model of the blade based on the actual geometric configuration of the turbine blade of an aero-engine. Consider the actual service environment of the turbine blade, clarify the requirements for the non-uniform temperature field distribution of the blade, and select the working temperature of the key point from the test section as the test point temperature.

[0009] Step (2) Based on the cross-sectional shape of the test, perform a fully constrained parametric modeling of the electromagnetic coil, take the magnetic flux density distribution of the blade as the design target, conduct sensitivity analysis on the design parameters of the electromagnetic coil, determine the design variables of the electromagnetic coil, and establish a three-dimensional model of the electromagnetic coil.

[0010] Step (3) Use the three-dimensional model of the blade established in step (1) and the three-dimensional model of the electromagnetic coil established in step (2) to establish a finite element model for calculating the non-uniform temperature field of the turbine blade, and stipulate that the plane where the centroid of the coil is located coincides with the test section of the blade; perform Latin hypercube sampling on the electromagnetic coil design variables determined in step (2), and then carry out magnetic field / solid heat transfer coupling simulation calculation on the sampling points to obtain the outer wall temperature of the test point of the test section; establish an automatic optimization calculation framework for the above process, and repeat the simulation calculation multiple times to obtain an initial sample set; use the geometric design variables, coil / blade relative angle, and coil centroid coordinates as input variables and the outer wall temperature of the test point as output variables to construct an adaptive sampling proxy model;

[0011] Step (4) Among all parameter groups that meet the temperature range of the test point, select the parameter group with the smallest error with the test temperature of the danger point as the design parameters of the electromagnetic induction coil.

[0012] Step (5) Use the electromagnetic coil design parameters determined in step (4) to manufacture the electromagnetic induction coil using 3D printing technology.

[0013] Furthermore, in step (1), the engine field test results are combined with finite element analysis, and the part of the turbine blade most prone to failure is used as the test section.

[0014] Furthermore, the electromagnetic coil design variables in step (2) include the great circle radius. ,radian small circle radius ,radian small circle radius ,radian Transition section radius ,radian ,radius ,radian Relative angle between coil and blade And the centroid coordinates of the coil.

[0015] The advantages of this invention compared to existing technologies are as follows: This invention introduces the finite element method for debugging in non-uniform temperature fields. Through multiphysics numerical simulation combined with adaptive optimization, it determines the coil design parameters that satisfy the temperature field distribution of the test section, saving significant experimental costs, improving experimental safety, and increasing experimental efficiency. Currently, no related technologies have been reported, and this invention fills a gap in relevant research. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of a turbine blade non-uniform temperature field debugging method based on multi-physics field coupling simulation optimization according to the present invention.

[0017] Figure 2a , Figure 2b This is a schematic diagram showing the location of the test points for the turbine blades according to an embodiment of the present invention; wherein, Figure 2a This is a schematic diagram showing the distribution of specific assessment points; Figure 2b A frontal schematic diagram showing the distribution of specific assessment points;

[0018] Figure 3 This is a schematic diagram of a three-dimensional model of a turbine blade and coil assembly according to an embodiment of the present invention;

[0019] Figure 4 This is a temperature field cloud diagram of the test section of the turbine blade after induction heating, according to an embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram of the temperature simulation results at the turbine blade test point in an embodiment of the present invention.

[0021] Figure 6 This is a schematic diagram showing the selection result of the electromagnetic induction coil radius in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0023] like Figure 1 As shown, the present invention provides a method for debugging the non-uniform temperature field of turbine blades based on multiphysics coupled simulation optimization. This method determines the electromagnetic coil design parameters that satisfy the temperature field distribution of the test section through multiphysics numerical simulation combined with adaptive optimization. The implementation steps are as follows:

[0024] Step 1: Determine the three-dimensional structure of the turbine blades. Establish a three-dimensional model of the turbine blades based on the actual turbine blades of the engine. According to the actual service environment of the high-pressure turbine blades of the engine, determine the non-uniform temperature field distribution range of the turbine blades. Using the engine's field test results combined with finite element analysis, the part of the turbine blade most prone to failure is used as the test evaluation section. The operating temperature of the key part is selected from the evaluation section as the evaluation point temperature.

[0025] Step 2: Based on the shape of the test section, perform a fully constrained parametric modeling of the electromagnetic coil. Taking the magnetic flux density distribution of the blade as the design objective, conduct a sensitivity analysis on the geometric design parameters of the electromagnetic coil, and select geometric design variables such as the radius of the circle. ,radian small circle radius ,radian small circle radius ,radian Transition section radius ,radian ,radius ,radian Relative angle between coil and blade The centroid coordinates (x, y, z) of the coil are used as optimization variables to establish a three-dimensional model of the electromagnetic coil.

[0026] Step 3: Using the 3D turbine blade model established in Step 1 and the 3D electromagnetic coil model established in Step 2, a finite element model for calculating the non-uniform temperature field of the turbine blade's thermomechanical fatigue testing section is established. The plane containing the coil centroid is specified to coincide with the blade testing section and be parallel to the xy plane. Latin hypercube sampling is performed on the electromagnetic coil geometric design variables, coil / blade relative angle, coil centroid coordinates, and other parameters determined in Step 2. Then, magnetic field / solid heat transfer coupled simulation calculations are conducted on the sampling points to obtain the non-uniform temperature field distribution results of the blade testing section. An automatic optimization calculation framework is established for the above process, and the simulation is repeated 10,000 times to obtain an initial sample set. Using the electromagnetic coil geometric design variables, coil / blade relative angle, and coil centroid coordinates as input variables, and the outer wall temperature of the testing point as the output variable, a high-efficiency adaptive sampling surrogate model is constructed, and the regression coefficients, regression vectors, and correlation matrices of the surrogate model are obtained.

[0027] Step 4: Among all parameter sets that meet the temperature range of the outer wall surface at the test point, select the parameter set with the smallest temperature error with the test point at the trailing edge of the blade as the design parameters for the electromagnetic induction coil.

[0028] Step 5: Using the design parameters of the electromagnetic induction coil determined in Step 4, the electromagnetic induction coil is fabricated using 3D printing technology.

[0029] The technical solution of a turbine blade non-uniform temperature field tuning method based on multiphysics coupling simulation optimization, according to the present invention, will be further described below with reference to the accompanying drawings and examples. The specific implementation process is as follows:

[0030] Step 1: The test object was determined to be the second-stage turbine blade of a certain type of aero-engine. A 3D model of the turbine blade was created proportionally using UG software based on the actual turbine blade of the engine. According to the actual service environment of the high-pressure turbine blade, the section of the blade to be tested was selected as the middle section of the turbine blade. The non-uniform temperature field distribution range of the turbine blade was determined. Using the engine's field test results combined with finite element analysis, the part of the turbine blade most prone to failure was selected as the test section. The operating temperature of key parts was selected from the test section as the test point (i.e., the measurement point temperature). The specific distribution of the test points is as follows: Figure 2a , Figure 2b As shown in Table 1, the temperature range of the assessment points is fixed at (0,0,0) in this example.

[0031] Table 1

[0032]

[0033] Step 2: Based on the cross-sectional shape of the test section, perform a fully constrained parametric model of the electromagnetic coil. Taking the magnetic flux density distribution of the blade as the design target, conduct a sensitivity analysis on the electromagnetic coil design parameters. In this example, geometric design variables such as the great circle radius of the electromagnetic coil are selected. ,radian small circle radius ,radian small circle radius ,radian Transition section radius ,radian ,radius ,radian and the relative angle between the coil and the blade The centroid coordinates (x, y, z) of the coil are used as optimization variables for the electromagnetic coil, and a three-dimensional model of the coil is established using SolidWorks software.

[0034] Step 3: Based on the obtained 3D models of the turbine blade and coil, perform finite element calculations of the non-uniform temperature field at the turbine blade's thermomechanical fatigue testing section. The plane containing the coil's centroid is specified to coincide with the blade's testing section and be parallel to the xy-plane. Import the established 3D turbine blade model and coil model as parts into the COMSOL multiphysics coupling simulation software to establish a finite element model for calculating the non-uniform temperature field at the turbine blade's thermomechanical fatigue testing section, as follows... Figure 3As shown. Based on this, a multiphysics module, namely electromagnetic and heat transfer physics, is added. The same convective and radiative heat transfer conditions are applied to both the inner and outer surfaces of the blade, with the ambient temperature set to 20℃. Latin hypercube sampling is performed on the electromagnetic coil geometric design variables, coil / blade relative angle, and coil centroid coordinates determined in the second step. Then, magnetic field / solid heat transfer coupled simulation calculations are conducted on the sampling points to obtain the non-uniform temperature field distribution results of the blade's test section. An automatic optimization calculation framework is established for the above process, and the simulation is repeated 10,000 times to obtain an initial sample set. Using the electromagnetic coil geometric design variables, coil / blade relative angle, and coil centroid coordinates as input variables, and the outer wall temperature of the test point as the output variable, a high-efficiency adaptive sampling surrogate model is constructed, and the regression coefficients, regression vector, and correlation matrix of the surrogate model are obtained.

[0035] Step 4: Among all parameter sets that meet the requirement of a ±10℃ temperature deviation on the outer wall of the test point, select the parameter set with the smallest temperature error relative to the test point at the blade trailing edge as the geometric parameters for the electromagnetic induction coil design. For example, in this case, the great circle radius... =2.86cm, radius of the great circle arc =221.75°, radius of the first small circle =0.66cm, radius of the first small circle arc =174.83°, radius of the second smallest circle =0.78cm, radius of the second smallest circle arc =96.16°, transition section radius =2.13cm, transition section radius arc =37.97°, radius =2.36cm, radians =21.57°, coil / blade relative angle The centroid coordinates of the coil are (0.01, 0.014, 0). The simulation results of the non-uniform temperature field at the test section are as follows: Figure 4 As shown, the temperature simulation results at the assessment points are as follows: Figure 5 As shown, the corresponding electromagnetic coil design radius parameters are selected as follows: Figure 6 As shown;

[0036] Step 5: Using the electromagnetic coil geometric parameters determined in Step 4, the electromagnetic induction coil is fabricated using 3D printing technology.

[0037] The above embodiments are provided merely for the purpose of describing the present invention and are not intended to limit the scope of the invention. The scope of the invention is defined by the appended claims. Various equivalent substitutions and modifications made without departing from the spirit and principles of the invention should be covered within the scope of the invention.

Claims

1. A method for adjusting non-uniform temperature field of turbine blade based on multi-physical field coupling simulation optimization, characterized in that, The steps include the following: Step (1) Establish a three-dimensional model of the blade based on the actual geometric configuration of the turbine blade of an aero-engine. Consider the actual service environment of the turbine blade, clarify the requirements for the non-uniform temperature field distribution of the blade, and select the working temperature of the key point from the test section as the test point temperature. Step (2) Based on the cross-sectional shape of the test, perform a fully constrained parametric modeling of the electromagnetic coil, take the magnetic flux density distribution of the blade as the design target, conduct sensitivity analysis on the design parameters of the electromagnetic coil, determine the design variables of the electromagnetic coil, and establish a three-dimensional model of the electromagnetic coil. Step (3) Use the three-dimensional blade model established in step (1) and the three-dimensional electromagnetic coil model established in step (2) to establish a finite element model for calculating the non-uniform temperature field of the turbine blade; perform Latin hypercube sampling on the electromagnetic coil design variables determined in step (2), and then carry out magnetic field / solid heat transfer coupling simulation calculation on the sampling points to obtain the outer wall temperature of the test point of the test section; establish an automatic optimization calculation framework for the above process, and repeat the simulation calculation multiple times to obtain the initial sample set; Using geometric design variables, coil / blade relative angle, and coil centroid coordinates as input variables and outer wall temperature at the test point as output variable, an adaptive sampling surrogate model is constructed. Step (4) Among all parameter groups that meet the temperature range of the test point, select the parameter group with the smallest error with the test temperature of the danger point as the design parameters of the electromagnetic induction coil. Step (5) Use the electromagnetic coil design parameters determined in step (4) to manufacture the electromagnetic induction coil using 3D printing technology.

2. The non-uniform temperature field adjustment method for turbine blades based on multi-physical field coupling simulation optimization according to claim 1, characterized in that: In step (1), the engine field test results are combined with finite element analysis, and the part of the turbine blade most prone to failure is used as the test section.

3. The method for adjusting the non-uniform temperature field of turbine blades based on multiphysics coupling simulation optimization according to claim 1, characterized in that: The electromagnetic coil design variables in step (2) include the great circle radius. radius of the great circle radian Radius of the first small circle Radius of the first small circle arc The radius of the second smallest circle The radius of the second smallest circle arc Transition section radius Transition section radius arc Relative angle between coil and blade And the centroid coordinates of the coil.

Images