CN112257312A - Turbine blade material micro flaky particle group erosion model parametric modeling method - Google Patents
Turbine blade material micro flaky particle group erosion model parametric modeling method Download PDFInfo
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Abstract
The invention discloses a turbine blade material micro flaky particle group erosion model parametric modeling method, which comprises the following steps: 1. acquiring actual operation condition data of a turbomachine flow channel, performing an accelerated erosion test on a blade material based on the actual service condition parameters, and establishing a turbine blade material erosion rate database and a particle rebound database; 2. acquiring stress strain data of the turbine blade material through a hydraulic servo testing machine; 3. acquiring the geometric characteristics and parameter distribution characteristics of the erosion object particle sample mica iron oxide through a super-depth-of-field microscope; 4. according to the microscopic morphology characteristics of the particles and the post-loading characteristic curve of the blade, a three-dimensional finite element erosion model of the blade material of the fine flaky particle group impact turbine machine is built through a Python script and a grid discrete method based on an ABAQUS commercial software platform. The method can be used for quantitative prediction of erosion damage distribution and rule of turbine machinery blade materials by factors such as particle roundness, particle initial attitude and particle size distribution.
Description
Technical Field
The invention relates to the technical field of computer aided design, in particular to a parameterized modeling method for a superfine flaky particle group erosion model of a turbine blade material.
Background
Erosion and abrasion phenomena generated by impact of fine solid particles on the surface of a material are widely existed in a cascade runner in the turbomachinery, so that the wall surface of the runner is rough, the profile of a blade is changed, and even the running safety and the economical efficiency of equipment are threatened. Therefore, it is very important to study the erosion failure mechanism of the blade material.
Microscopic observation shows that the real morphology of the fine solid particles is irregular and polygonal, from the particle size, the shape of the particles and the orientation relation between the particles and the surface can cause completely different particle impact behaviors (forward rotation, backward rotation and the like), different erosion and abrasion effects are generated on blade materials, the mechanism is very complex, quantitative prediction is difficult to achieve by a general method, the interaction between the fine particles and high-speed airflow is difficult to research by an experimental method, high requirements are provided for a test technology, and the time resolution and the space resolution of the existing high-speed photographic equipment are difficult to meet, but other effective test means are lacked.
With the continuous development of computer-aided technology, the research on the erosion mechanism from the material microscopic level through a finite element method based on the nonlinear dynamic characteristic in the process of impacting the wall surface of the target material by particles becomes a main idea for solving the problem of erosion damage. Many students such as EiTobgy prove the necessity of establishing a multi-particle erosion model to accurately predict erosion behaviors by counting erosion rates of particles after impacting a target material, and Wang YF, Takaffoli, Liu ZG and the like respectively establish three-dimensional erosion models of spherical particle groups impacting metal ductile materials such as Ti-6Al-4V alloy, Cu alloy and the like, indicate key factors influencing material damage, and indicate directions for optimizing the erosion resistance of blade materials. Summarizing the literature, the existing models mostly aim at erosion particles with ideal shapes and have large sizes; the particle size is usually small in the actual erosion process, and the particles are in irregular flaky shapes; in addition, the particles are generally assumed to have the same size and shape, the inconsistency of the shape and the size in a particle sample in the actual erosion process is ignored, most of the impact targets adopt Ti-6Al-4V alloy or Cu alloy, the erosion characteristics of the turbine blade material are rarely related, and the erosion damage mechanism of the turbine mechanical blade material cannot be effectively disclosed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a parameterized modeling method of a turbine mechanical blade material erosion model for resisting fine flaky particle groups.
The invention is realized by adopting the following technical scheme:
a turbine blade material micro flaky particle group erosion model parameterization modeling method comprises the following steps:
(1) and acquiring actual operation condition data of the turbine mechanical flow passage, performing an accelerated erosion test on the blade material based on the actual service condition parameters, and establishing a turbine blade material erosion rate database and a particle rebound database.
(2) And acquiring stress strain data of the turbine blade material through a hydraulic servo testing machine.
(3) Acquiring the geometric morphology and size distribution characteristics of the erodent particle sample mica iron oxide through a super-field-depth microscope, wherein the geometric morphology and size distribution characteristics comprise: particle equivalent diameter distribution, projected area distribution, roundness distribution and thickness distribution; and acquiring the position coordinates of each particle in the N particles and exporting the position coordinates as an excel file based on the real microscopic morphology and distribution characteristics of the particles.
(4) The establishment of the turbine machinery blade material erosion finite element model is based on an ABAQUS6.14-2 commercial software platform, in specific operation, as a reading and storage function module of an excel file of a particle group position coordinate is not contained in a Python standard module, data reading and writing operations on the excel file can be realized by manually adding Xlrd and Xlwt modules by installing a third party library, meanwhile, a blade target material is set to be a cuboid Part which can at least cover all particle impact areas in the length and width directions and has the thickness not less than the sum of the outer diameters of two largest particles, a Python script statement rapid modeling and grid discretization method is adopted, and the turbine machinery blade material three-dimensional finite element erosion model is finally established.
(5) Setting particle incidence speed V consistent with actual operation condition0And angle of incidence beta0And counting the number of failure units of the blade target material and the particle rebound velocity V through post-processing2Then calculating to obtain the erosion rate and the particle speed recovery coefficient of the blade target, comparing the erosion rate and the particle speed recovery coefficient with the actually measured target erosion rate and the particle speed recovery coefficient in the step (1), and adjusting the blade material damage model and the contact model penalty function rigidity in the numerical simulation pretreatment to enable the erosion rate and the particle speed recovery coefficient to be identical when the erosion rate and the particle speed recovery coefficient are different, so as to verify the correctness and the validity of the numerical model; the corrected finite element model can realize quantitative prediction of erosion damage rules and erosion mechanisms of turbine machinery blade materials by factors such as particle roundness, particle initial attitude and particle size distribution.
In the step (1) of the method, the actual operation condition data of the turbine mechanical flow passage comprises flow rate, pressure and temperature of the inlet and the outlet of the flow passage, mechanical property parameters of the blade and erosion particles, erosion distribution on the surface of the blade and weight loss.
Carrying out an accelerated erosion test on the blade material based on the actual service working condition of the turbine machine, and establishing an erosion rate database and a particle rebound database of the blade material of the turbine machine. In the process, the erosion speed, the impact angle and the air flow temperature parameters in the particle-target erosion test system are required to be ensured to be consistent with the parameters in the actual turbine service environment; on the basis of the above, different particle incidence speeds V are carried out0Different incident angle beta0And (5) carrying out an accelerated erosion experiment, and recording the steady-state erosion rate of the material and the particle velocity recovery coefficient. Weighing the mass of the test target material before and after erosion by an electronic balance to obtain the steady-state erosion rate of the material under different working conditions; by passingAnd the PIV performs image processing on the particle impact and rebound motion tracks to obtain particle rebound data.
In the step (2) of the method, stress strain data of the blade material at several typical service temperatures are obtained through a hydraulic servo testing machine, and the stress strain value obtained in the test is taken as engineering stress strain data and needs to be converted into real stress strain data available for an ABAQUS platform.
In the step (3) and the step (4) of the method, the particle distribution characteristics are obtained by writing MATLAB codes to obtain position coordinates of all particles in a particle group, the position coordinates are exported to be excel files, and a Python script statement rapid modeling and grid discretization method is adopted to establish an erosion finite element model of the blade material of the turbine machinery on an ABAQUS6.14-2 commercial software platform. In the specific operation, as the read and storage function module of the excel file of the particle cluster corner position coordinates is not contained in the Python standard module, the read and write data operation of the excel file can be realized by installing a third party library and manually adding Xlrd and Xlwt modules, and finally the rapid modeling and grid division of the finite element erosion model are realized.
The turbine blade material micro flaky particle group erosion model parametric modeling method elaborately generates a flaky particle group with the appearance and the size distribution basically consistent with the observation result under a microscope by MATLAB programming based on the blade material accelerated erosion test data result under the guidance of the actual service environment and the operating condition parameters of a turbine machine and the microscopic appearance and the size distribution of oxide skin particles actually measured in an erosion test during specific operation, and in addition, obtains the real stress strain data of the blade material through a hydraulic servo testing machine, further establishes a particle group erosion model of the turbine machine blade material on an ABAQUS6.14-2 commercial software platform through a Python script rapid modeling and grid discretization method, and can realize the quantitative prediction of the erosion damage of the blade material and the influence rule of each main factor on the erosion characteristic under the condition of the erosion of the superfine particle group, effective guidance is provided for further understanding of the erosion mechanism of the blade material and process optimization of the erosion-resistant blade material, and good social and economic benefits are achieved.
Drawings
FIG. 1 shows a flow chart of the method of the present invention.
Figure 2 shows a schematic of the particle impact-bounce process.
FIG. 3 shows the 1Cr12W1MoV true stress-strain curve for a blade material at 566 deg.C.
FIG. 4 shows the geometric morphology of mica iron oxide particles observed under an ultra-depth-of-field microscope.
Fig. 5 shows a flowchart of the steps for parametric modeling of a population of fine plate-like particles.
FIG. 6 shows the erosion profile of the blade material calculated based on the modified finite element model.
Detailed Description
The following detailed description of specific embodiments of the invention refers to the accompanying drawings.
A parametric modeling method for a turbine blade material micro flaky particle group erosion model is shown in figure 1, and comprises the following steps:
(1) and acquiring actual operation condition data of the turbine mechanical flow passage, performing an accelerated erosion test on the blade material based on the actual service condition parameters, and establishing a turbine blade material erosion rate database and a particle rebound database.
The actual operation condition data of the turbomachine flow passage comprises flow rate, pressure and temperature of the flow passage inlet and outlet, mechanical property parameters of the blade and erosion particles, erosion distribution on the surface of the blade and weight loss.
Carrying out blade material accelerated erosion test based on the actual service working condition of the turbine machinery, establishing a turbine blade material erosion rate database and a particle rebound database, and ensuring that erosion speed, impact angle and air flow temperature parameters in a particle-target material erosion test system are consistent with parameters in the actual turbine service environment in the test process; sequentially carrying out different particle incidence speeds V0Different incident angle beta0And (5) carrying out an accelerated erosion experiment, and recording the steady-state erosion rate of the material and the particle velocity recovery coefficient. In the test process, the mass of the test target material before and after erosion is weighed and recorded by an electronic balance, so that the steady erosion rate of the material under different working conditions is obtained; particle alignment by PIVPerforming image processing on the sub-impact and rebound motion tracks to obtain particle rebound data, namely particle velocity recovery coefficients which are respectively particle tangential velocity recovery coefficients eTAnd normal velocity recovery coefficient eNThe specific calculation formula is as follows:
wherein, V0TAnd V0NRespectively, particle incident velocity V0Of the tangential and normal components, V2TAnd V2NRespectively, the particle rebound velocity V2The tangential and normal components of (a). Beta is a0And beta2The incident angle and the rebound angle of the particles are respectively corresponding to the included angles between the incident direction and the rebound direction of the particles and the surface of the target material, and a schematic diagram of the particle impact-rebound process is shown in fig. 2.
(2) And obtaining the after-loading characteristic of the blade material at several typical service temperatures, namely an engineering stress-strain curve, on a hydraulic servo testing machine. Note that the stress-strain value obtained in the test is engineering stress-strain data, which needs to be converted into real stress-strain data for ABAQUS finite element analysis, and the relation between the real stress and strain and the engineering stress and strain is:
ε＝ln(1+εen) (3)
σ＝σen(1+εen) (4)
where ε is true strain, σ is true stress, εenFor engineering strain, σenIs an engineering stress.
FIG. 3 shows the true stress-strain curve of the transformed blade material 1Cr12W1 MoV.
(3) Acquiring the geometric morphology and parameter distribution of the particles under a microscope with super depth of field, and mainly comprising the following steps: particle equivalent diameter distribution, projected area distribution, roundness distribution and thickness distribution. And (3) counting the values of the projection area, the perimeter and the thickness of the particles by using a measure tool in the ultra-depth-of-field microscope to obtain a histogram of the projection area, the equivalent diameter, the roundness and the thickness distribution of the particles. Fig. 4 shows the microscopic morphology of the mica iron oxide particles for test observed under the cohnson super-depth-of-field microscope, and it can be seen from the figure that the thickness of the particles is much smaller than the particle diameter, and the particles are in typical sheet-like morphology. Further, based on the real microscopic morphology and distribution characteristics of the particles, the position coordinates of each of the N particles are obtained and exported to be an excel file, which specifically includes the following steps, as shown in fig. 5.
S1, based on the particle distribution characteristics, giving initial values to the projection areas of N particles, wherein the projection areas are selected to follow the particle projection area distribution, and a random value is selected within the particle equivalent diameter distribution range, the equivalent diameter DcirThe smallest circle into which a given particle can be completely placed is defined, and the calculation formula is as follows:
where S is the area of the planar projection of the particle.
S2, defining the vertex of the projection area of the particle by using four points; randomly selecting the x and y coordinates of three points and the x coordinate of the fourth point to enable the x and y coordinates to be positioned in the boundary determined by S1; the y-coordinate of the fourth vertex is calculated so that the resulting quadrilateral area is within the distribution of the projected areas of the particles, noting that all quadrilaterals are created by connecting four random points in sequence and ensuring that there are no intersections between the lines used to create the projection plane.
S3, solving the roundness value of the quadrangle generated in the step S2 according to the formula (6), adding 1 to the number of the roundness value in the distribution interval, if all the intervals are full, namely the requirement of roundness value distribution is met, discarding the corresponding generated particles, and the roundness value RcThe expression of (a) is:
wherein, S and PpThe more circular the particle, the closer the roundness value is to 1, being the planar projected area and perimeter of the particle.
And repeating the steps S1-S3 until the generated N projection areas can simultaneously meet the distribution requirements of the particle diameter, the projection area and the roundness.
S4, analyzing the particle sample by the super-depth-of-field microscope, finding that there is no correlation between the projected area of the particle and the thickness of the particle, the projected diameter is far larger than the thickness, and the thickness distribution is uniform, therefore, the average value of the thicknesses of N particles is obtained by calculation and used as the thickness value.
And S5, under the condition that the incident particles impact the surface of the target in a random direction, randomly rotating the generated vertex coordinates of each three-dimensional particle on the x axis, the y axis and the z axis to obtain the final position coordinates of the N particles, and exporting the position coordinates to an excel file for constructing a particle group geometric model.
(4) The establishment of the turbine machinery blade material erosion finite element model is based on an ABAQUS6.14-2 commercial software platform, in specific operation, as a reading and storage function module of an excel file of a particle group position coordinate is not contained in a Python standard module, data reading and writing operations on the excel file can be realized by manually adding Xlrd and Xlwt modules by installing a third party library, meanwhile, a blade target material is set to be a cuboid Part which can at least cover all particle impact areas in the length and width directions and has the thickness not less than the sum of the outer diameters of two largest particles, a Python script statement rapid modeling and grid discretization method is adopted, and the turbine machinery blade material three-dimensional finite element erosion model is finally established.
(5) Setting particle incidence speed V consistent with actual operation condition0And angle of incidence beta0Checking the erosion damage appearance of the blade material in the numerical simulation post-treatment of the high-speed impact of the particle group on the turbine blade material, and counting the number of failure units of the blade target material and the particle rebound velocity V through the post-treatment2Calculating the erosion rate and particle velocity recovery coefficient of the blade material, and punching the blade material with the turbine blade material obtained by the testAnd comparing the erosion rate and the particle velocity recovery coefficient, and adjusting the blade material damage model and the contact model penalty function rigidity in the numerical simulation pretreatment to enable the blade material damage model and the contact model to be matched when the erosion rate and the particle velocity recovery coefficient are different, so as to verify the correctness and the validity of the numerical model.
The erosion morphology of the blade material 1Cr12W1MoV at an incident speed of 210m/s and an incident angle of 24 degrees of the plate-shaped particle group with the average particle diameter of 36.8 mu m is calculated based on the corrected finite element model, and is shown in FIG. 6. The finite element model can realize quantitative prediction of influence rules of factors such as particle size distribution, particle roundness and particle initial attitude on erosion characteristics under the condition of fine particle group impact.
The simulation modeling method for the particle group erosion turbine blade material can perform parametric modeling according to the geometric morphology and the parameter distribution characteristics of the particle sample, and is used for quantitative prediction of erosion damage distribution and rule of the turbine machine blade material by factors such as particle roundness, particle initial attitude and particle size distribution.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent flow transformations that are made by using the contents of the present specification and the drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (4)
1. A turbine blade material micro flaky particle group erosion model parameterization modeling method is characterized by comprising the following steps: the method comprises the following steps:
(1) acquiring actual operation condition data of a turbomachine flow channel, performing an accelerated erosion test on a blade material based on the actual service condition parameters, and establishing a turbine blade material erosion rate database and a particle rebound database;
(2) acquiring stress strain data of the turbine blade material through a hydraulic servo testing machine;
(3) acquiring the geometric morphology and size distribution characteristics of the erodent particle sample mica iron oxide through a super-field-depth microscope, wherein the geometric morphology and size distribution characteristics comprise: particle equivalent diameter distribution, projected area distribution, roundness distribution and thickness distribution; acquiring the position coordinate of each particle in the N particles and exporting the position coordinate as an excel file based on the real microscopic morphology and distribution characteristics of the particles;
(4) establishing a finite element model for the erosion of turbine machinery blade materials based on an ABAQUS6.14-2 commercial software platform, wherein in specific operation, as a reading and storage function module of an excel file of a particle group position coordinate is not contained in a Python standard module, reading and data writing operation on the excel file can be realized by installing a third library and manually adding Xlrd and Xlwt modules, meanwhile, setting a blade target material into a cuboid Part which can at least cover all particle impact areas in the length and width directions and has the thickness not less than the sum of the outer diameters of two largest particles, adopting a Python script statement rapid modeling and grid discretization method, and finally establishing a three-dimensional finite element erosion model for the turbine machinery blade materials;
(5) setting particle incidence speed V consistent with actual operation condition0And angle of incidence beta0And counting the number of failure units of the blade target material and the particle rebound velocity V through post-processing2Then calculating to obtain the erosion rate and the particle speed recovery coefficient of the blade target, comparing the erosion rate and the particle speed recovery coefficient with the actually measured target erosion rate and the particle speed recovery coefficient in the step (1), and adjusting the blade material damage model and the contact model penalty function rigidity in the numerical simulation pretreatment to enable the erosion rate and the particle speed recovery coefficient to be identical when the erosion rate and the particle speed recovery coefficient are different, so as to verify the correctness and the validity of the numerical model; the corrected finite element model can realize quantitative prediction of the erosion damage rule and the erosion mechanism of the turbine mechanical blade material by particle roundness, particle initial attitude and particle size distribution.
2. The parametric modeling method for the erosion model of the fine flaky particle group of the turbine blade material as claimed in claim 1, wherein: step (1): the actual operation condition data of the turbomachine flow passage comprises flow rate, pressure and temperature of the flow passage inlet and outlet, mechanical property parameters of the blade and erosion particles, erosion distribution on the surface of the blade and weight loss.
3. The parametric modeling method for the erosion model of the fine flaky particle group of the turbine blade material as claimed in claim 2, wherein: in the test process, the erosion speed, the impact angle and the air flow temperature parameters in the particle-target erosion test system are ensured to be consistent with the parameters in the actual turbine service environment; sequentially carrying out different particle incidence speeds V0Different incident angle beta0An accelerated erosion experiment is carried out, and the steady-state erosion rate of the material and the particle speed recovery coefficient are recorded;
in the test process, the mass of the test target material before and after erosion is weighed and recorded by an electronic balance, so that the steady erosion rate of the material under different working conditions is obtained; particle impact and rebound motion tracks are subjected to image processing through the PIV to obtain particle rebound data, namely particle velocity recovery coefficients which are respectively particle tangential velocity recovery coefficients eTAnd normal velocity recovery coefficient eNThe specific calculation formula is as follows:
wherein, V0TAnd V0NRespectively, particle incident velocity V0The tangential and normal components of; v2TAnd V2NRespectively, the particle rebound velocity V2The tangential and normal components of; beta is a0And beta2The angle of incidence and the angle of rebound of the particles are respectively corresponding to the included angles between the incidence and rebound directions of the particles and the surface of the target material.
4. The parametric modeling method for the erosion model of the fine flaky particle group of the turbine blade material as claimed in claim 1, wherein: in the step (3), the method specifically comprises the following steps:
s1, projection surfaces of N particles based on the particle distribution characteristicsThe initial value of the product is selected such that the projected area follows the projected area distribution of the particles and a random value is selected within the range of the equivalent diameter distribution of the particles, the equivalent diameter DcirThe smallest circle into which a given particle can be completely placed is defined, and the calculation formula is as follows:
wherein S is the planar projection area of the particle;
s2, defining the vertex of the projection area of the particle by using four points; randomly selecting the x and y coordinates of three points and the x coordinate of the fourth point to enable the x and y coordinates to be positioned in the boundary determined by S1; calculating the y coordinate of the fourth vertex to ensure that the obtained quadrilateral area is in the distribution range of the particle projection area; all quadrilaterals are created by connecting four random points in sequence and ensuring that there are no intersections between the lines used to create the projection plane;
s3, solving the roundness value of the quadrangle generated in the step S2 according to the formula (6), adding 1 to the number of the roundness value in the distribution interval, if all the intervals are full, namely the requirement of roundness value distribution is met, discarding the corresponding generated particles, and the roundness value RcThe expression of (a) is:
wherein, S and PpThe plane projection area and the perimeter of the particle;
repeating the steps S1-S3 until the N generated projection areas can simultaneously meet the distribution requirements of particle diameter, projection area and roundness;
s4, calculating the average value of the thicknesses of the N particles to be used as a thickness value;
and S5, under the condition that the incident particles impact the surface of the target in a random direction, randomly rotating the generated vertex coordinates of each three-dimensional particle on the x axis, the y axis and the z axis to obtain the final position coordinates of the N particles, and exporting the position coordinates to an excel file for constructing a particle group geometric model.
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| US20090297720A1 (en) * | 2008-05-29 | 2009-12-03 | General Electric Company | Erosion and corrosion resistant coatings, methods and articles |
| DE102010004663A1 (en) * | 2010-01-14 | 2011-07-21 | Siemens Aktiengesellschaft, 80333 | Turbine blade for use in low-pressure stage of steam turbine, has fiber composite material, where fiber composite material has area that is coated with protective layer |
| CN108344652A (en) * | 2018-01-22 | 2018-07-31 | 西安热工研究院有限公司 | A kind of rebounding characteristic test system of subparticle high-speed impact runner wall surface |
| CN111143982A (en) * | 2019-12-19 | 2020-05-12 | 西安交通大学 | Particle erosion resistance optimization method for turbine mechanical blade flow passage structure |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090297720A1 (en) * | 2008-05-29 | 2009-12-03 | General Electric Company | Erosion and corrosion resistant coatings, methods and articles |
| DE102010004663A1 (en) * | 2010-01-14 | 2011-07-21 | Siemens Aktiengesellschaft, 80333 | Turbine blade for use in low-pressure stage of steam turbine, has fiber composite material, where fiber composite material has area that is coated with protective layer |
| CN108344652A (en) * | 2018-01-22 | 2018-07-31 | 西安热工研究院有限公司 | A kind of rebounding characteristic test system of subparticle high-speed impact runner wall surface |
| CN111143982A (en) * | 2019-12-19 | 2020-05-12 | 西安交通大学 | Particle erosion resistance optimization method for turbine mechanical blade flow passage structure |
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