Method and device for predicting elasticity modulus of nuclear power plant equipment coating

Abstract

The invention belongs to the technical field of nuclear power, and particularly relates to a method and device for predicting the elasticity modulus of a nuclear power plant equipment coating. According to the method, the multiple coating models are obtained by reconstructing the multiple spraying state coatings obtained in the actual operation environment, the corresponding relation between the porosity and the elastic modulus is obtained according to the stress field distribution of the coating models, and the corresponding relation between the porosity and the elastic modulus constant is further obtained through the elastic modulus calculation model; therefore, the elastic modulus of other coatings can be predicted by means of the calculation model of the elastic modulus. The technical problem that the existing porous material elastic modulus acquisition needs experimental cutting or has a single numerical sample structure is solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of nuclear power, and particularly relates to a method and device for predicting the elastic modulus of a coating of a nuclear power plant device. BACKGROUND

[0002] Rotating machinery of a nuclear power plant is in an extremely harsh service environment, and the thermal barrier coating coated on the surface of the rotating machinery will be subjected to heat, force impact and chemical corrosion of a high-temperature (for example, 250-290 DEG C) fluid for a long time, and the microstructure thereof will produce irreversible changes, and in severe cases, coating peeling failure will occur. These irreversible microstructure changes will cause the thermal performance of the coating to degrade, resulting in a decrease in the heat insulation performance of the coating, and thus shortening the service life thereof, and ultimately leading to damage of a turbine high-temperature blade.

[0003] The elastic modulus is an important test parameter of the mechanical performance of the coating, and the elastic modulus of the coating reflects the elastic performance of the coating due to force, and depends on the atomic structure and microstructure inside the coating, and is an important parameter affecting the dynamic characteristics of the composite structure of the coating. At present, the identification and research of the elastic modulus of the coating are mostly concentrated in the range of experimental measurement or comparison between macroscopic simulation analysis and experimental results. The elastic modulus of the coating is closely related to the microstructure thereof. Related research methods mostly establish empirical formulas for the identification of the elastic modulus by taking porosity as the only microstructure variable parameter, and the selection of parameters has a certain subjective dependence. However, pores in the actual coating exist in various irregular shapes, and defects in the coating also change to different degrees with the increase of service time, which brings great trouble to the quantitative description. Therefore, it is necessary to find a simple elastic modulus identification method based on microstructure characteristic parameters. SUMMARY

[0004] In order to overcome the problems in the related art, a method and device for predicting the elastic modulus of a coating of a nuclear power plant device are provided.

[0005] According to an aspect of an embodiment of the present disclosure, a method for predicting the elastic modulus of a coating of a nuclear power plant device is provided, and the method comprises the following steps:

[0006] Step 1, for a plurality of sprayed coatings obtained from an actual operating environment, determining a sintered ceramic layer numerical reconstruction structure corresponding to a true structure of each sprayed coating;

[0007] Step 2, performing numerical reconstruction on each sintered ceramic layer, converting the numerical reconstruction into a vector graph after binary image processing, and performing grid division on the vector graph to form a coating model;

[0008] Step 3, boundary condition setting is performed for each coating model; a fixed displacement boundary is adopted on the left side of each coating model in the form of formula one, and a fixed displacement boundary is adopted on the left side of the bottom edge of each coating model, so that each coating model does not have rigid body displacement in the calculation process; the upper and lower boundaries of the coating model are set as free boundaries, and the surface force received is 0; an average displacement constraint is applied to the right boundary of the coating model, the strain of the coating model is set, the elastic modulus and Poisson's ratio of the dense coating material are given, and the distribution of the coating stress field of each coating model is obtained; the right boundary support reaction force of each coating model is extracted by using formula two to determine the effective elastic modulus E of each coating model eff ;

[0009]

[0010] In the formula, ∑F is the resultant force of the support reaction force F of each node on the right boundary, Nx is the length of the coating finite element calculation model in the load direction, and A is the cross-sectional area of the coating finite element calculation model in the load direction;

[0011] Step 4, the corresponding relationship between the porosity and the elastic modulus constant of each coating model is determined by using formula three;

[0012] E eff =E m+p+c =E m exp(-b·a·p (pore) )·c Formula three

[0013] In the formula, Em is the elastic modulus of the dense skeleton material, p (pore) is the porosity, b is the elastic modulus constant, a is the shape influence factor considering the size of the pores, and c is the shape influence factor considering the size of the cracks, and a and c are usually 0.5-1; the effective elastic modulus of each coating model can be obtained according to the accurate calculation formula of formula two, and on this basis, the porosity corresponding to the elastic modulus constant of the coating model can be obtained according to formula three;

[0014] Step 5, fitting processing is performed according to the elastic modulus constant corresponding to each porosity to obtain the corresponding relationship between the porosity and the elastic modulus constant as shown in formula four;

[0015] b=8(p (pore) ) 0.2 Formula four

[0016] Step 6, for the coating whose elastic modulus needs to be predicted, the effective elastic modulus of the coating is predicted according to the porosity of the coating and formula three and formula four.

[0017] In a possible implementation, in step 1, for a plurality of sprayed-state coating structures obtained from an actual running environment, edge features of each pore of the real structure of the sprayed-state coating are extracted, and a plurality of pores are randomly selected therefrom, the pore size is reduced along the edge of each randomly selected pore and / or at a position where the pore diameter is less than a preset threshold, and a sintered-state ceramic layer numerical reconstruction structure is output.

[0018] In a possible implementation, step 1 further includes:

[0019] Step 11, extracting a ceramic layer image from a SEM image of the real sprayed-state thermal barrier coating;

[0020] Step 12, statistically determining the porosity and pore size distribution of the ceramic layer image;

[0021] Step 13, arranging growth nuclei according to the number of pores of the ceramic layer image;

[0022] Step 14, generating a reconstructed coating according to the porosity and pore size distribution of the ceramic layer;

[0023] Step 15, determining whether the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image is beyond an allowable error range;

[0024] Step 16, if the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image is beyond the allowable error range, repeating steps 14 to 15; if the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image is not beyond the allowable error range, outputting the reconstructed coating as the real structure of the sprayed-state coating.

[0025] In a possible implementation, in step 2, for pores in the coating model whose shape irregularity exceeds a preset threshold, separate placement is performed, so that the grid division near the pores is uniform and the size of each grid is smaller than that of other regions of the coating model, so that the grid near the irregular pores is sufficiently refined and uniform, and after the placement is completed, CPS4R 4-node quadrilateral bilinear plane stress elements are used in the grid division, and the reduced integration is also used.

[0026] In a possible implementation, in step 3, a fixed displacement boundary is used for the position of the left side of the bottom edge of each coating model, so that each coating model does not have a rigid body displacement during the calculation process.

[0027] u(x,y)| x＝0 =0, v(x,y)| x＝0,y＝0 =0 Equation One

[0028] In the formula, u(x, y) represents the displacement of the mesoscopic strain tensor in the horizontal direction; and v(x, y) represents the displacement of the mesoscopic strain tensor in the vertical direction.

[0029] The upper and lower boundaries of the coating model are set as free boundaries, and the surface force received is 0. An average displacement constraint μm is applied to the right boundary of the coating model, so that the strain of the coating model is 0.005. Given that the elastic modulus of the dense coating material is 210 GPa and the Poisson's ratio is 0.3, the distribution of the coating stress field of each coating model is obtained.

[0030] According to another aspect of the embodiments of the present disclosure, a device for predicting the elastic modulus of a coating of a nuclear power plant equipment is provided, and the device comprises:

[0031] The first determining module is configured to determine, for each sprayed coating obtained from an actual operating environment, a sintered ceramic layer numerical reconstruction structure corresponding to a real structure of the sprayed coating;

[0032] The second determining module is configured to perform numerical reconstruction on each sintered ceramic layer, convert the sintered ceramic layer into a vector graph after binary image processing, and perform grid division on the vector graph to form a coating model;

[0033] The boundary setting module is configured to set a boundary condition for each coating model. A fixed displacement boundary is adopted on the left side of each coating model, and a fixed displacement boundary is adopted on the left side of the bottom edge of each coating model, so that no rigid body displacement occurs in each coating model during calculation. The upper and lower boundaries of the coating model are set as free boundaries, and the surface force received is 0. An average displacement constraint is applied to the right boundary of the coating model, the strain of the coating model is set, the elastic modulus and the Poisson's ratio of the dense coating material are given, and the distribution of the coating stress field of each coating model is obtained. The right boundary reaction force of each coating model is extracted by using Formula II to determine the effective elastic modulus E of each coating model. eff ;

[0034]

[0035] In the formula, ∑F is the resultant force of the right boundary node reaction force F, Nx is the length of the coating finite element calculation model in the load direction, and A is the cross-sectional area of the coating finite element calculation model in the load direction.

[0036] The constant calculation module is configured to determine the corresponding relationship between the porosity and the elastic modulus constant of each coating model by using Formula III.

[0037] E eff = E m+p+c = E m exp(-b·a·p (pore) )·c Formula III

[0038] wherein Em is the elastic modulus of the dense matrix material, p is the porosity, b is the elastic modulus constant, a is a shape factor considering the size of the pores, and c is a shape factor considering the size of the cracks, and a and c are usually 0.5-1; the effective elastic modulus of each coating model can be obtained according to the accurate calculation formula of Formula II, and on this basis, the elastic modulus constant corresponding to the porosity of the coating model can be obtained according to Formula III. (pore) is the porosity, b is the elastic modulus constant, a is a shape factor considering the size of the pores, and c is a shape factor considering the size of the cracks, and a and c are usually 0.5-1; the effective elastic modulus of each coating model can be obtained according to the accurate calculation formula of Formula II, and on this basis, the elastic modulus constant corresponding to the porosity of the coating model can be obtained according to Formula III.

[0039] a fitting module configured to perform fitting processing according to the elastic modulus constant corresponding to each porosity to obtain a corresponding relationship between the porosity and the elastic modulus constant as shown in Formula IV.

[0040] b = 8(p (pore) ) 0.2 Formula IV

[0041] a prediction module configured to, for a coating whose elastic modulus needs to be predicted, predict the effective elastic modulus of the coating according to the porosity of the coating and Formula III and Formula IV.

[0042] According to another aspect of the embodiments of the present disclosure, a device for predicting the elastic modulus of a coating of a nuclear power plant equipment is provided, and the device comprises:

[0043] a processor;

[0044] a memory for storing processor-executable instructions;

[0045] wherein the processor is configured to perform the above method.

[0046] According to another aspect of the embodiments of the present disclosure, a nonvolatile computer-readable storage medium having computer program instructions stored thereon is provided, and the computer program instructions are executed by a processor to implement the above method.

[0047] The present disclosure has the beneficial effect that: the present disclosure reconstructs a plurality of sprayed coatings obtained in an actual operating environment to obtain a plurality of coating models, obtains a corresponding relationship between the porosity and the elastic modulus according to the stress field distribution of the coating models, and further obtains a corresponding relationship between the porosity and the elastic modulus constant through a calculation model of the elastic modulus, so that the elastic modulus of other coatings can be predicted by means of the calculation model of the elastic modulus. The technical problems that the elastic modulus of a porous material needs to be obtained through experiments or that the structure of a numerical sample is single are solved. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart of a method for predicting the elastic modulus of a coating of a nuclear power plant equipment according to an embodiment of the present disclosure.

[0049] Figure 2is a schematic diagram of establishing a coating model according to an example of the present disclosure.

[0050] Figure 3 is a block diagram of a device coating elastic modulus prediction device according to an example of the present disclosure. DETAILED DESCRIPTION

[0051] The present disclosure will be further described below in conjunction with the accompanying drawings and specific examples.

[0052] Unless otherwise defined, technical and scientific terms used in the present disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs; the terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure; the term "comprising" and any variation thereof in the present disclosure is intended to cover not excluding; it is apparent that the embodiments described in the present disclosure are only a part of the embodiments of the present disclosure, not all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0053] In the present disclosure, "embodiment" means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present disclosure. The phrase appears at various places in the specification does not necessarily refer to the same embodiment, nor is it independent or alternative to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0054] Figure 1 is a flowchart of a device coating elastic modulus prediction method according to an example of the present disclosure. The method can be executed by a terminal device, which can be a server, a desktop computer, a notebook computer, etc., and the type of terminal device is not limited by the embodiments of the present disclosure. As shown in Figure 1 the method includes:

[0055] Step 1, for a plurality of sprayed coatings obtained from an actual operating environment, determine a sintered ceramic layer numerical reconstruction structure corresponding to a real structure of each sprayed coating.

[0056] As an example of the present embodiment, as the service time of the device in a high-temperature harsh environment increases, the internal porosity of the sprayed coating of the device gradually decreases and the morphology is spheroidized. At the same time, the wedge angle region between the layers of the coating is prone to form a sintering neck. For a plurality of sprayed coatings obtained from an actual operating environment, extract the edge features of each porosity of the real structure of each sprayed coating, and randomly select a plurality of porosities therefrom, reduce the porosity size along the edge of each randomly selected porosity and / or at the porosity size less than a preset threshold, and output a sintered ceramic layer numerical reconstruction structure.

[0057] In a possible implementation, step 1 further comprises:

[0058] Step 11, extracting the ceramic layer image from the SEM image of the real as-sprayed thermal barrier coating;

[0059] Step 12, statistically determining the porosity and pore size distribution of the ceramic layer image;

[0060] Step 13, arranging the growth nucleus according to the number of pores of the ceramic layer image;

[0061] Step 14, generating a reconstructed coating according to the porosity and pore size distribution of the ceramic layer;

[0062] Step 15, determining whether the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image is beyond the allowable error range;

[0063] Step 16, if the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image is beyond the allowable error range, repeating steps 14 to 15; if the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image is not beyond the allowable error range, outputting the reconstructed coating as the as-sprayed coating real structure.

[0064] Step 2, performing numerical reconstruction structure on each sintered ceramic layer, converting the binary image processing into a vector graph, and performing grid division on the vector graph to form a coating model.

[0065] In step 2, the numerical reconstruction structure of each sintered ceramic layer output is obtained by numerical reconstruction to obtain a coating binary image, and the coating binary image is processed by image processing (for example, bitmap conversion, contour extraction, vectorization processing) and then converted into a vector graph suitable for ABAQUS finite element calculation in CorelDraw drawing tool (as shown in Figure 2 Then, the vector graph is subjected to grid division to form a coating model. Since the density of grid cells directly affects the efficiency and accuracy of the operation, too many grid numbers often increase the operation time, and too few grid numbers make the simulation result inaccurate. Based on this point, an appropriate size of the unit is defined, and the accuracy and convergence of the model are checked. The corresponding elastic modulus calculation of the porous medium composite material builds a digital prediction model:

[0066] In a possible implementation, in step 2, for the pores in the coating model whose shape irregularity exceeds a preset threshold, separate layout is performed, so that the grid division near the pores is uniform and the size of a single grid is smaller than that of other regions of the coating model, so that the grid near the irregular pores is sufficiently refined and uniform, because the stress distribution near the pores is often complex. After the layout is completed, the unit shape and unit type are selected. In the grid division, CPS4R 4-node quadrilateral bilinear plane stress elements are adopted, and reduced integration is adopted, so that the grid still has sufficient calculation accuracy under the external condition of large deformation.

[0067] Step 3, as shown in Figure 3 , boundary condition setting is performed on each coating model. In the form of formula one, a fixed displacement boundary is adopted on the left side of each coating model, and a fixed displacement boundary is adopted on the left side of the bottom edge of each coating model, so that each coating model does not have rigid body displacement in the calculation process.

[0068] u(x,y)| x＝0 =0, v(x,y)| x＝0,y＝0 =0 Formula one

[0069] In the formula, u(x,y) represents the displacement of the mesoscopic strain tensor in the horizontal direction; and v(x,y) represents the displacement of the mesoscopic strain tensor in the vertical direction.

[0070] To ensure that the coating model meets the actual situation, that is, the upper and lower boundaries of the thermal barrier coating can be freely deformed during the load application process, the upper and lower boundaries of the coating model are set as free boundaries, and the surface force received is 0. The average displacement constraint μm is applied to the right boundary of the coating model, so that the strain of the coating model is 0.005. Given that the elastic modulus of the dense coating material is 210 GPa and the Poisson's ratio is 0.3, an analysis job is created and submitted, and the distribution of the coating stress field of each coating model is obtained; the right boundary reaction force of each coating model is extracted by using formula two to determine the effective elastic modulus E eff of each coating model.

[0071]

[0072] In the formula, ∑F is the resultant force of the reaction force F of each node on the right boundary, Nx is the length of the coating finite element calculation model in the load direction, and A is the cross-sectional area of the coating finite element calculation model in the load direction.

[0073] Step 4, the corresponding relationship between the porosity and the elastic modulus constant of each coating model is determined by using formula three.

[0074] E eff =E m+p+c =E m exp(-b·a·p (pore) )·c Formula three

[0075] wherein Em is the elastic modulus of the dense matrix material, p is the porosity, b is the elastic modulus constant, a is a shape factor considering the size of the pores, and c is a shape factor considering the size of the cracks, and a and c are usually 0.5-1. (pore) According to the accurate calculation formula of Formula II, the effective elastic modulus of each coating model can be obtained, and on this basis, the elastic modulus constant corresponding to the porosity of the coating model can be obtained according to Formula III.

[0076] Step 5: According to the elastic modulus constant corresponding to each porosity, fitting processing is performed to obtain the corresponding relationship between the porosity and the elastic modulus constant as shown in Formula IV.

[0077] b = 8(p (pore) ) 0.2 Formula IV

[0078] Step 6: For a coating whose elastic modulus needs to be predicted, the effective elastic modulus of the coating is predicted according to the porosity of the coating and Formula III and Formula IV.

[0079] In previous studies, the pores in the coating are mostly regarded as regular spheres, and the value of b is 4.1±1.8. However, the pores in the real coating are not regular spheres, and there are also cracks, so the calculated elastic modulus of the coating is much higher than the actual elastic modulus. In view of the above problems, Formula III of the present disclosure regards the coating as a three-phase (matrix, pore and crack) composite system, thereby obtaining an empirical formula that is closer to the actual measured value.

[0080] In one possible implementation, the atlas of a and c can be obtained at the same time. Researchers can quickly obtain the elastic modulus value of the thermal barrier coating under different structural characteristics by querying the atlas.

[0081] In one possible implementation, a device for predicting the elastic modulus of a coating of a nuclear power plant equipment is provided, and the device comprises:

[0082] A first determination module is configured to determine, for a plurality of sprayed coatings obtained from an actual operating environment, a sintered ceramic layer value reconstruction structure corresponding to the real structure of each sprayed coating.

[0083] A second determination module is configured to perform numerical reconstruction on each sintered ceramic layer, convert the numerical reconstruction into a vector graph after binary image processing, and perform grid division on the vector graph to form a coating model.

[0084] The boundary setting module is configured to set boundary conditions for each coating model; a fixed displacement boundary is adopted on the left side of each coating model by using formula one, and a fixed displacement boundary is adopted on the left side of the bottom edge of each coating model, so that each coating model does not have rigid body displacement in the calculation process; the upper and lower boundaries of the coating model are set as free boundaries, and the surface force received is 0; an average displacement constraint is applied to the right boundary of the coating model, the strain of the coating model is set, the elastic modulus and Poisson's ratio of the dense coating material are given, and the distribution of the coating stress field of each coating model is obtained; the right boundary support reaction force of each coating model is extracted by using formula two to determine the effective elastic modulus E of each coating model eff .

[0085]

[0086] In the formula, ∑F is the resultant force of the support reaction force F of each node on the right boundary, Nx is the length of the coating finite element calculation model in the load direction, and A is the cross-sectional area of the coating finite element calculation model in the load direction;

[0087] The constant calculation module is configured to determine the corresponding relationship between the porosity and the elastic modulus constant of each coating model by using formula three;

[0088] E eff = E m+p+c = E m exp(-b·a·p (pore) )·c Formula three

[0089] In the formula, Em is the elastic modulus of the dense skeleton material, p (pore) is the porosity, b is the elastic modulus constant, a is the shape influence factor considering the size of the pores, and c is the shape influence factor considering the size of the cracks, and a and c are usually 0.5-1; the effective elastic modulus of each coating model can be obtained according to the accurate calculation formula of formula two, and on this basis, the porosity corresponding to the elastic modulus constant of the coating model can be obtained according to formula three;

[0090] The fitting module is configured to perform fitting processing on the elastic modulus constant corresponding to each porosity to obtain the corresponding relationship between the porosity and the elastic modulus constant as shown in formula four;

[0091] b = 8(p (pore) ) 0.2 Formula four

[0092] The prediction module is configured to predict the effective elastic modulus of a coating that needs to be predicted according to the porosity of the coating and formula three and formula four.

[0093] The description of the above device has been described in detail in the description of the above method, and will not be repeated here.

[0094] Figure 3 is a device diagram of a prediction of a nuclear power plant equipment coating elastic modulus according to an embodiment of the present disclosure. For example, device 1900 can be provided as a server. Referring to Figure 3 , device 1900 includes a processing assembly 1922, which further includes one or more processors, and a memory resource represented by memory 1932 for storing instructions, such as an application program, executable by the processing assembly 1922. The application program stored in the memory 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing assembly 1922 is configured to execute the instructions to perform the above method.

[0095] Device 1900 can also include a power supply assembly 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input output (I / O) interface 1958. Device 1900 can operate based on an operating system stored in memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

[0096] In exemplary embodiments, a non-transitory computer readable storage medium, such as memory 1932 including computer program instructions, is also provided, which can be executed by the processing assembly 1922 of the device 1900 to complete the above method.

[0097] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0098] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0099] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0100] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0101] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0102] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other data storage device. When the computer readable program instructions are loaded into the computer and other programmable data processing apparatus, a series of operational steps are implemented that provide processes such that the instructions which execute on the computer or other programmable data processing apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0103] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0104] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0105] Embodiments of the present disclosure have been described above, and the description is intended to be illustrative of the embodiments and not restrictive. Many modifications and variations of the described embodiments are possible and are within the scope of the disclosure. The selection of terms is intended to best describe the principles of the embodiments, practical application, or improvement over the technology in the art, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting the elastic modulus of coatings for nuclear power plant equipment, characterized in that: The method comprises: Step 1: for multiple sprayed coatings obtained from an actual operating environment, determine the numerically reconstructed structure of the sintered ceramic layer corresponding to the actual structure of each sprayed coating; Step 2: numerically reconstruct the structure of each sintered ceramic layer, convert it into a vector diagram after binary image processing, and mesh the vector diagram to form a coating model; Step 3, set the boundary conditions for each coating model; use the form of formula 1 to adopt a fixed displacement boundary on the left side of each coating model, and use a fixed displacement boundary on the left side of the bottom edge of each coating model, so that each coating model does not undergo rigid body displacement during the calculation process; set the upper and lower boundaries of the coating model as free boundaries, and the surface force received is 0; apply an average displacement constraint to the right boundary of the coating model, set the strain of the coating model, and give the elastic modulus and Poisson's ratio of the dense coating material to obtain the distribution of the coating stress field of each coating model; use formula 2 to extract the right boundary support reaction of each coating model to determine the effective elastic modulus E of each coating model eff ; Where ∑F is the resultant force of the support reaction F at each node on the right boundary, Nx is the length of the coating finite element calculation model along the load direction, and A is the cross-sectional area of ​​the coating finite element calculation model along the load direction; Step 4, using Equation 3 to determine the corresponding relationship between the porosity and elastic modulus constant of each coating model; E eff =E m+p+c =E m exp(-b·a·p (pore) )·c Formula 3 Where, Em is the elastic modulus of the dense skeleton material, p (pore) is the porosity, b is the elastic modulus constant, a is the shape influence factor considering the pore size, and c is the shape influence factor considering the crack size. a and c are usually 0.5 to 1. The effective elastic modulus of each coating model can be obtained by the precise calculation formula of formula 2. On this basis, the elastic modulus constant corresponding to the porosity of the coating model can be obtained according to formula 3. Step 5: Perform fitting processing based on the elastic modulus constant corresponding to each porosity to obtain the corresponding relationship between the porosity and the elastic modulus constant as shown in Formula 4; b=8(p (pore) ) 0.2 Formula 4 Step 6: For the coating whose elastic modulus needs to be predicted, predict the effective elastic modulus of the coating based on the porosity of the coating and equations 3 and 4.

2. The method according to claim 1, characterized in that In step 1, for multiple sprayed coatings obtained from the actual operating environment, the edge features of each pore in the real structure of each sprayed coating are extracted, and multiple pores are randomly selected from them. The pore size is reduced along the edges of each randomly selected pore and / or the pore size is less than a preset threshold, and the numerical reconstructed structure of the sintered ceramic layer is output.

3. The method according to claim 1, characterized in that Step 1 also includes: Step 11, extracting a ceramic layer image from a real SEM image of the sprayed thermal barrier coating; Step 12, statistically determining the porosity and pore size distribution of the ceramic layer image; Step 13, arranging the growth nuclei according to the number of pores in the ceramic layer image; Step 14, generating a reconstructed coating according to the porosity and pore size distribution of the ceramic layer; Step 15, determining whether the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image exceeds an allowable error range; Step 16: If the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image exceeds the allowable error range, repeat steps 14 to 15; if the difference between the porosity and pore size distribution of the reconstructed coating and the porosity and pore size distribution of the ceramic layer image does not exceed the allowable error range, the reconstructed coating is output as the true structure of the sprayed coating.

4. The method according to claim 1, wherein In step 2, the pores in the coating model whose irregular shapes exceed the preset threshold are seeded separately to ensure that the mesh near the pores is uniform and the single mesh size is smaller than the mesh size in other areas of the coating model, so that the mesh near the irregular pores is sufficiently refined and uniform. After the seeding is completed, the CPS4R 4-node quadrilateral bilinear plane stress element is used in the mesh division, and reduced integration is also adopted.

5. The method according to claim 1, wherein In step 3, a fixed displacement boundary is used for the left side of the bottom edge of each coating model so that each coating model does not undergo rigid body displacement during the calculation process; u(x,y)| x＝0 = 0, v(x,y)| x＝0,y＝0 = 0 Equation 1 Where u(x,y) represents the horizontal displacement of the mesoscopic strain tensor; v(x,y) represents the vertical displacement of the mesoscopic strain tensor. The upper and lower boundaries of the coating model are set as free boundaries, with a traction force of 0. A mean displacement constraint of μm is applied to the right boundary of the coating model, resulting in a strain of 0.

005. Given a dense coating material elastic modulus of 210 GPa and a Poisson's ratio of 0.3, the coating stress field distribution for each coating model is obtained.

6. A device for predicting the elastic modulus of coatings for nuclear power plant equipment, characterized in that: The device comprises: The first determination module is used to determine the numerically reconstructed structure of the sintered ceramic layer corresponding to the actual structure of each sprayed coating layer obtained from the actual operating environment; The second determination module is used to numerically reconstruct the structure of each sintered ceramic layer, convert it into a vector diagram after binary image processing, and mesh the vector diagram to form a coating model; The boundary setting module is used to set the boundary conditions for each coating model; a fixed displacement boundary is used on the left side of each coating model in the form of formula 1, and a fixed displacement boundary is used on the left side of the bottom edge of each coating model, so that each coating model does not undergo rigid body displacement during the calculation process; the upper and lower boundaries of the coating model are set as free boundaries, and the surface force received is 0; an average displacement constraint is applied to the right boundary of the coating model, the strain of the coating model is set, and the elastic modulus and Poisson's ratio of the dense coating material are given to obtain the distribution of the coating stress field of each coating model; formula 2 is used to extract the right boundary support reaction of each coating model to determine the effective elastic modulus E of each coating model eff ; Where ∑F is the resultant force of the support reaction F at each node on the right boundary, Nx is the length of the coating finite element calculation model along the load direction, and A is the cross-sectional area of ​​the coating finite element calculation model along the load direction; A constant calculation module is used to determine the corresponding relationship between the porosity and the elastic modulus constant of each coating model using Formula 3; E eff =E m+p+c =E m exp(-b·a·p (pore) )·c Formula 3 Where, Em is the elastic modulus of the dense skeleton material, p (pore) is the porosity, b is the elastic modulus constant, a is the shape influence factor considering the pore size, and c is the shape influence factor considering the crack size. a and c are usually 0.5 to 1. The effective elastic modulus of each coating model can be obtained by the precise calculation formula of formula 2. On this basis, the elastic modulus constant corresponding to the porosity of the coating model can be obtained according to formula 3. A fitting module is used to perform fitting processing based on the elastic modulus constant corresponding to each porosity to obtain the corresponding relationship between the porosity and the elastic modulus constant as shown in Formula 4; b=8(p (pore) ) 0.2 Formula 4 The prediction module is used to predict the effective elastic modulus of a coating for which the elastic modulus needs to be predicted, based on the porosity of the coating and equations 3 and 4.

7. A device for predicting the elastic modulus of coatings for nuclear power plant equipment, characterized in that: The device comprises: processor; a memory for storing processor-executable instructions; The processor is configured to execute the method according to any one of claims 1 to 5.

8. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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