A solid engine finite element analysis method based on CT image fusion modeling

Abstract

The present application belongs to the technical field of solid engine nondestructive testing and finite element analysis, and relates to a solid engine finite element analysis method based on CT image fusion modeling, which comprises the following steps: step S1: performing CT step-by-step scanning on key positions of a defect high-occurrence area of a solid engine to be tested to obtain real CT images of the defect part; step S2: constructing a cross-section model of a part to be simulated, simulating CT data of an unscanned area, and generating a simulated CT image; step S3: fusing the real CT image and the simulated CT image to generate a triangular facet mesh model; step S4: importing the triangular facet mesh model into 3-matic software to generate a volume mesh model that can be used for finite element analysis; and step S5: importing the volume mesh model into Abaqus finite element analysis software to perform health state evaluation. The present application fuses the simulated CT image and the real CT image, and effectively reduces the detection cost and technical difficulty.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing and finite element analysis technology for solid rocket motors, specifically to a finite element analysis method for solid rocket motors based on CT image fusion modeling. Background Technology

[0002] Solid rocket motors, as the core power unit of aircraft, are prone to defects such as debonding and cracking of their internal propellant during solidification, storage, and use, which seriously affect the safety and reliability of the engine. Industrial CT inspection has become an important means of identifying internal defects in solid rocket motors, but there are still significant shortcomings in CT image processing and subsequent structural modeling.

[0003] In the prior art, a segmentation method for CT images of solid rocket motors is disclosed. By removing artifacts, filtering, and using multi-threshold segmentation, the automatic extraction of the shell, propellant, star holes, and defects is achieved, thereby improving the efficiency and accuracy of CT image interpretation. A finite element mesh model generation method and system for solid rocket motor shells is also disclosed. Through axial compression load analysis, cross-sectional mesh construction, and rotation sweeping, high-precision finite element modeling suitable for anisotropic material shells is achieved.

[0004] The above-mentioned technical solutions have made progress in CT image segmentation and idealized shell modeling, but the following technical problems still exist:

[0005] Due to the long axial dimensions of large solid rocket motors, incomplete industrial CT scans lead to missing data; existing finite element modeling is mostly based on ideal geometric models, ignoring actual defects, resulting in insufficient evaluation accuracy due to deviations from actual working conditions.

[0006] In view of this, it is very necessary to provide a finite element analysis method for solid rocket motors based on CT image fusion modeling to solve the above-mentioned defects in the prior art. Summary of the Invention

[0007] The purpose of this invention is to solve the technical problems in the prior art of large solid rocket motors, such as data loss due to incomplete CT scans; and the insufficient evaluation accuracy caused by existing finite element modeling, which is mostly based on ideal geometric models, ignores actual defects, and deviates from actual working conditions. This invention provides a finite element analysis method for solid rocket motors based on CT image fusion modeling to solve the technical problems existing in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] This invention provides a finite element analysis method for solid rocket motors based on CT image fusion modeling, comprising the following steps:

[0010] Step S1: Perform stepwise CT scans on key areas of the solid rocket motor under test to obtain real CT images of the defective parts.

[0011] Step S2: Construct a cross-sectional model of the part to be simulated, simulate the CT data of the unscanned area, and generate simulated CT images;

[0012] Step S3: Fuse the real CT image and the simulated CT image to generate a triangular mesh model;

[0013] Step S4: Import the triangular mesh model into the 3-matic software to generate a volume mesh model that can be used for finite element analysis;

[0014] Step S5: Import the volume mesh model into Abaqus finite element analysis software to conduct a health status assessment.

[0015] The beneficial effects of this invention are as follows:

[0016] This invention obtains real CT images of areas prone to defects by performing stepwise CT scans on key parts of the solid rocket motor under test, which is difficult to perform a complete CT scan on large solid rocket motors. This effectively avoids the problem of missing key defect data caused by incomplete scanning, improves the completeness and relevance of defect information acquisition, and solves the technical problem of missing data due to incomplete CT scans.

[0017] This invention constructs a cross-sectional model of the part to be simulated, simulates the CT data of the unscanned area, and generates simulated CT images. This enables the unscanned structural areas to obtain a continuous and complete internal structural description, making up for the deficiency that local measured CT images cannot cover the overall structure, and ensuring the integrity of the overall engine modeling data.

[0018] This invention generates a triangular mesh model by fusing real CT images with simulated CT images. This results in a modeling result that simultaneously includes the real defect morphology and complete structural features, avoiding the problem of existing finite element modeling that is based on ideal geometric models and ignores actual defects. This improves the consistency between the model and the actual structural state.

[0019] This invention achieves an effective connection between CT images and finite element analysis models by converting the fused triangular mesh model into a volume mesh model that can be used for finite element analysis. This enables the volume mesh to accurately reflect the internal structure and defect distribution of the engine, providing a reliable geometric basis for subsequent analysis.

[0020] This invention imports a solid mesh model into finite element analysis software for health status assessment, enabling the assessment process to be carried out based on a model containing real defect information and complete structural features. This reduces the deviation between finite element analysis results and actual operating conditions, thereby improving the accuracy of health status assessment for large solid rocket motors.

[0021] This invention fuses simulated CT images with real CT images, allowing complete data to be obtained without a full-size scan, effectively reducing detection costs and technical difficulties.

[0022] This invention preserves the geometric deviations and actual defect morphologies during the manufacturing and service of solid rocket motors in the modeling process, enabling accurate evaluation of individual solid rocket motors.

[0023] This invention organically combines real CT scans of high-defect areas, CT data simulation of unscanned areas, and fusion modeling of the two. It ensures the accurate representation of key defects while maintaining the continuity of the overall structure, thus solving the problem of incomplete CT scans and idealized finite element modeling in large solid rocket motors. This improves the reliability and engineering applicability of structural analysis and health status assessment.

[0024] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0026] Figure 1 This is an architecture diagram of a finite element analysis method for solid rocket motors based on CT image fusion modeling;

[0027] Figure 2 This is a flowchart of a finite element analysis method for solid rocket motors based on CT image fusion modeling. Detailed Implementation

[0028] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following implementation methods.

[0029] Example:

[0030] like Figure 1 and Figure 2As shown in the figure, this embodiment provides a finite element analysis method for solid rocket motors based on CT image fusion modeling, which includes the following steps:

[0031] Step S1: Perform stepwise CT scans on key areas of the solid rocket motor under test to obtain real CT images of the defective parts.

[0032] Step S2: Construct a cross-sectional model of the part to be simulated, simulate the CT data of the unscanned area, and generate simulated CT images;

[0033] Step S3: Fuse the real CT image and the simulated CT image to generate a triangular mesh model;

[0034] Step S4: Import the triangular mesh model into the 3-matic software to generate a volume mesh model that can be used for finite element analysis;

[0035] Step S5: Import the volume mesh model into Abaqus finite element analysis software to conduct a health status assessment.

[0036] In step S1: Based on the structural form, dimensional parameters, and defect distribution patterns of the solid rocket motor to be evaluated, high-incidence areas of defects such as debonding and cracking inside the solid rocket motor are identified as key scanning areas for CT inspection. For the selected high-incidence defect areas, industrial CT equipment is used to perform stepwise scanning of the key areas to acquire realistic CT image data that reflects the morphology and location relationship of the defects.

[0037] When using industrial CT scanners, the scanning method and scanning range should be selected appropriately based on the outer diameter of the solid rocket motor. The scanning method can be either a full-scale scanning method or a partial scanning method, depending on the inspection requirements. Full-scale scanning is suitable for situations where the scanning range covers the entire cross-section of the solid rocket motor but the axial length is limited, while partial scanning is suitable for situations where the scanning range is smaller than the diameter of the solid rocket motor and focuses on covering areas with a high incidence of defects.

[0038] When using a holistic scanning method, the mounting plane of the igniter on the front end of the solid rocket motor housing is used as the starting reference point for axial scanning in CT inspection. If the front end does not have an igniter or related mounting structure, the skirt end face of the front end of the solid rocket motor housing is used as the starting reference point for scanning. Simultaneously, the starting point of the first quadrant of the solid rocket motor inspection is defined as the starting point for circumferential scanning in the axial direction, and the counterclockwise direction is used as the positive scanning direction, thus unifying the spatial reference coordinates of the CT images.

[0039] Under the constraints of scanning method and scanning range, axial stepwise CT detection scanning is performed on the high-incidence defect area of ​​solid rocket motor head that is prone to debonding and cracking. By controlling the scanning step distance at each axial position, CT cross-sectional images of the corresponding positions are gradually acquired to form a real CT image reflecting the internal structural characteristics of the defect area, providing a reliable data foundation for subsequent CT image supplementation, modeling and finite element analysis.

[0040] In step S2: For solid rocket motor parts that have not undergone actual industrial CT scanning, a solid rocket motor cross-sectional model of the part to be simulated is constructed based on the structural design drawings of a large solid rocket motor. Combined with the acquired real CT images, the gray values ​​corresponding to the shell, propellant, and air background are statistically analyzed, and the gray values ​​are assigned to the corresponding areas in the solid rocket motor cross-sectional model. The gray values ​​in the cross-sectional model are used to characterize the density characteristics of each component of the solid rocket motor. The solid rocket motor cross-sectional model is simulated and scanned according to the scanning parameters used in real industrial CT scanning to generate simulated CT image data of the unscanned areas.

[0041] The generated simulated CT images must simultaneously meet the following conditions:

[0042] First, it can accurately reflect the internal structural relationship between the solid rocket motor casing, the propellant charge, and the atmospheric background;

[0043] Secondly, the spatial resolution of the image is consistent with the real CT image obtained in step S1;

[0044] Third, the grayscale distribution of each component matches the actual CT image.

[0045] For CT data in unscanned areas, due to factors such as the large axial length of solid rocket motors and the high cost of industrial CT equipment, it is difficult to achieve full-size continuous scanning. Therefore, simulation is used to supplement the unscanned areas. Based on the scanning characteristics of translation and rotation during industrial CT imaging of large solid rocket motors, the CT imaging process of unscanned areas is simulated to generate simulated CT images that are consistent with real CT images in terms of resolution, grayscale distribution, and structural features.

[0046] The simulation scanning process involves establishing a cross-sectional model of the solid rocket motor based on the structural design drawings of the solid rocket motor, and combining the acquired real CT images to perform statistical analysis on the grayscale values ​​corresponding to the casing, propellant, and air background. The obtained grayscale value information is then assigned to the corresponding areas in the solid rocket motor cross-sectional model, so that the grayscale values ​​can be used to characterize the density characteristics of each component of the solid rocket motor.

[0047] Based on the principles of industrial CT imaging, grayscale values ​​are mapped to ray attenuation coefficients according to Beer's Law. Combined with scanning parameters used in actual CT scans, a simulated scan of a solid rocket motor cross-section model is performed. These parameters include translation step size, rotation angle, and scan range. By simulating the translational and rotational scanning process of rays within the solid rocket motor, projection chord diagram data is generated. A filtered back-projection algorithm is used to reconstruct the projection chord diagram, resulting in a simulated CT image consistent with the actual CT image. This completes the supplementation of CT data for unscanned areas, providing a complete data foundation for subsequent 3D modeling and finite element analysis.

[0048] Beer's Law describes the exponential decay of rays as they propagate through matter. The specific formula is as follows: , It is the intensity of the incident rays. It is the transmission intensity after passing through the thickness. The linear attenuation coefficient of the material. The thickness through which the ray passes.

[0049] In CT imaging, the grayscale value G essentially reflects the attenuation characteristics of rays in the material. To achieve physical consistency in simulation, the grayscale value needs to be mapped to an attenuation coefficient μ. A commonly used linear mapping formula is: ;in , These represent the minimum and maximum grayscale values ​​of the cross-sectional model, respectively. , These are the attenuation coefficients for air and the shell material, respectively. Through mapping, the grayscale value of each pixel can be converted into the corresponding physical attenuation coefficient, making the attenuation process of X-rays in the simulated scan consistent with that of a real CT scan.

[0050] The process of generating projection chord map data is as follows: During the simulation, a simulated ray passes through the cross-sectional model at a specified angle. The attenuation of each pixel is accumulated according to Beer's Law to calculate the ray transmission intensity. Repeating the calculation for rays at different rotation angles and axial translation positions generates projection chord maps covering the entire cross-sectional area. Each projection chord map reflects the total attenuation of the ray as it penetrates the model, equivalent to the projection information of a real CT scan image at that angle.

[0051] The reconstruction process of the projection chord map is as follows: After obtaining the projection chord map, the Filtered BackProjection (FBP) algorithm is used for reconstruction; each projection is subjected to one-dimensional filtering to enhance high-frequency information and suppress blur; according to the corresponding scanning angle, the filtered projection is backprojected back onto the cross-sectional plane, and the complete cross-sectional grayscale distribution is obtained by accumulating and superimposing the projections of all angles. After iterative and interpolation processing, a simulated CT image that is highly consistent with the real CT image in terms of spatial resolution, grayscale distribution, and structural features can be reconstructed.

[0052] By supplementing CT image data for unscanned areas through simulation, complete CT data acquisition can be achieved without performing full-size continuous CT scans on the solid rocket motor, reducing the cost and implementation difficulty of industrial CT inspection. The simulated CT images maintain consistency with real CT images in resolution and grayscale distribution, providing a continuous and unified data foundation for subsequent 3D modeling and finite element analysis, thereby improving the accuracy and reliability of the overall modeling and evaluation results.

[0053] In step S3: the real CT image and the simulated CT image are fused to form a continuous CT image sequence along the axis of the solid rocket motor. The CT image sequence is imported into the three-dimensional reconstruction environment in axial order. Three-dimensional voxels are constructed by corresponding pixels in two adjacent CT images. The moving cubes algorithm is used to draw the surfaces of the target structure and generate a triangular mesh model composed of triangular facets.

[0054] The Moving Cubes algorithm is a 3D isosurface extraction method based on volume data, widely used in 3D reconstruction of volume data such as CT and MRI. This algorithm uses voxels (cubes) in a regular 3D mesh as the basic processing unit. By analyzing the relationship between the scalar values ​​at each vertex of the voxel, such as CT grayscale values, and a given isosurface threshold, it determines the topological morphology of the isosurface within the voxel, thereby generating triangular patches that constitute the 3D surface on a voxel-by-voxel basis.

[0055] The resulting 3D image is a closed grayscale isosurface. The surface rendering process involves treating a sequence of CT images arranged axially as 3D volume data. Corresponding pixels in two adjacent CT images constitute a voxel, and each voxel contains eight vertices. Based on the grayscale distribution characteristics of different internal structures of the solid rocket motor in CT images, the grayscale value range of the shell, propellant charge, and other modeling structures in 2D CT images is selected as a threshold. For each voxel, the grayscale value of each of the eight vertices is checked against the threshold range, and the corresponding vertex's attribute value is determined. If it falls within the threshold range, the attribute value of the corresponding vertex is set to 1; otherwise, it is set to 0. Based on the distribution of attribute values ​​at each vertex of the voxel, it is determined whether the grayscale isosurface passes through the voxel.

[0056] When the attribute values ​​of the vertices of a volume element are not completely consistent, it indicates that the isosurface intersects with the volume element. Within the volume element, interpolation calculations determine the intersection points of the isosurface and each edge of the volume element. Following the topological connection rules of the moving cube algorithm, the intersection points are connected to the isosurface to form triangular patches. By repeating the above judgment and interpolation connection process for all volume elements, all triangular patches that meet the conditions are generated and connected sequentially, resulting in a closed triangular patch mesh model that reflects the true geometric shape of the solid rocket motor casing, propellant loading, and other modeling structures. This provides a basic geometric model for subsequent volume mesh reconstruction and finite element analysis.

[0057] The moving cube algorithm is used to generate a closed triangular mesh model, which can accurately depict the actual shape of the shell, propellant and defect boundaries, realize the geometric continuous expression of the overall structure of the solid rocket motor, and avoid the discontinuity and inconsistency between real data and simulation data.

[0058] In step S4: the generated triangular facet mesh model is imported into 3-matic software for volume mesh reconstruction. Volume mesh reconstruction includes preprocessing, iterative optimization, and volume mesh filling. Specifically: because the triangular facet mesh model contains many voids, it is first preprocessed to eliminate CT image noise and irregularities caused by the face rendering algorithm. Then, iterative optimization is performed to generate an optimized facet mesh model. The volume mesh generation tool in 3-matic software is used to fill the optimized facet mesh model with tetrahedral elements to generate a volume mesh model that meets the requirements of finite element analysis for element quality and computational stability. This results in a volume mesh model that can be used for finite element analysis, ensuring that the generated volume mesh model maintains geometric consistency while also meeting the requirements of finite element analysis for element quality and computational stability.

[0059] The preprocessing includes filling holes in the triangular mesh model, smoothing and optimizing local discontinuities, sharp regions, and rough meshes after filling, and removing redundant or distorted triangular patches to improve the continuity and geometric quality of the overall mesh.

[0060] The iterative optimization involves multiple rounds of quality assessment and local correction of the triangular mesh model, progressively adjusting the element size, shape, and distribution of the triangular mesh. Target element sizes are set based on geometric dimensions and analysis accuracy requirements; excessively large or dense patches are subdivided or merged to ensure a smooth transition between adjacent element sizes. Inferior triangles with excessively high aspect ratios or abnormal angles are eliminated through node translation and edge swapping, improving element shape quality. The mesh density in different regions is progressively adjusted based on structural complexity and curvature variations. This process is iterated repeatedly until the surface mesh maintains geometric consistency while meeting the requirements for element quality and computational stability in volume mesh generation and finite element analysis.

[0061] 3-matic is a 3D modeling and meshing software for engineering analysis, primarily used for geometric processing and volume mesh generation after reconstruction of medical images or industrial CT data. This software can edit, repair, and optimize triangular patch models obtained from CT images or surface rendering algorithms, and supports the generation of high-quality tetrahedral volume meshes, making it widely used in the preprocessing stage of finite element analysis.

[0062] By reconstructing the triangular mesh model into a volume mesh, a volume mesh model that can be used for finite element analysis is generated, which improves the efficiency of finite element modeling and provides a stable and reliable data foundation for the structural response analysis and defect assessment of solid rocket motors.

[0063] In step S5, the generated volume mesh model is imported into the Abaqus finite element analysis software as the calculation model for the solid rocket motor finite element analysis. In the Abaqus finite element analysis software, based on the actual structural composition and material properties of the solid rocket motor, corresponding material parameters are set for different regions in the volume mesh model, including the density, Poisson's ratio, elastic modulus or relaxation modulus, specific heat, and thermal conductivity of the propellant and shell materials, accurately characterizing the physical and mechanical behavior of the solid rocket motor under service conditions.

[0064] After setting the material parameters, based on the structural constraints and actual operating conditions of the solid rocket motor, corresponding boundary and load conditions are applied to the volume mesh model. For example, a fixed constraint is applied to the outer surface of the propellant grain to simulate the stress environment of the solid rocket motor in storage or operation. Finite element analysis is then performed on the volume mesh model to conduct stress distribution simulation analysis, obtaining the mechanical response distribution analysis results of stress and strain in various regions inside the solid rocket motor.

[0065] By analyzing the stress distribution in the defect area in the analysis results, characteristic parameters such as the maximum stress value at the defect location are extracted to quantitatively assess the impact of the defect on the structural integrity and health status of the solid rocket motor, and to evaluate the health status of the solid rocket motor under test. The stress response of the volume mesh model is compared with the expected results or experimental results to verify the effectiveness and accuracy of the volume mesh model. When the analysis results do not meet the evaluation requirements, steps S3-S4 or S5 are returned as needed to adjust the volume mesh model or analysis parameters and then perform finite element analysis again.

[0066] Abaqus is a general-purpose finite element analysis software capable of performing high-precision numerical simulations of the mechanical behavior of complex structures and materials. It can be applied to fields such as structural strength, fracture, contact, thermal analysis, and multiphysics coupling analysis. Abaqus supports various material models and nonlinear analysis methods, making it suitable for analyzing engineering problems involving complex geometries and realistic operating conditions.

[0067] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0068] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0069] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0070] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0071] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0072] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0073] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0074] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0075] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A finite element analysis method for solid rocket motors based on CT image fusion modeling, characterized in that, Includes the following steps: Step S1: Perform stepwise CT scans on key areas of the solid rocket motor under test to obtain real CT images of the defective parts. Step S2: Construct a cross-sectional model of the part to be simulated, simulate the CT data of the unscanned area, and generate simulated CT images; In step S2: For solid rocket motor parts that have not undergone actual industrial CT scanning, a solid rocket motor cross-sectional model of the part to be simulated is constructed based on the structural design drawings of a large solid rocket motor. Combined with the acquired real CT images, the gray values ​​corresponding to the shell, propellant, and air background are statistically analyzed, and the gray values ​​are assigned to the corresponding areas in the solid rocket motor cross-sectional model. The solid rocket motor cross-sectional model is then simulated and scanned according to the scanning parameters used in real industrial CT scanning to generate simulated CT image data of the unscanned areas. Step S3: Fuse the real CT image and the simulated CT image to generate a triangular mesh model; Step S4: Import the triangular mesh model into the 3-matic software to generate a volume mesh model that can be used for finite element analysis; Step S5: Import the volume mesh model into Abaqus finite element analysis software to conduct a health status assessment.

2. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 1, characterized in that, In step S1: Based on the structural form, dimensional parameters, and defect distribution patterns of the solid rocket motor to be evaluated, the high-incidence area of ​​internal defects of the solid rocket motor is determined as the key scanning area for CT detection; for the selected high-incidence area of ​​defects, industrial CT equipment is used to perform stepwise scanning of the key parts to obtain CT image data that can truly reflect the morphology and positional relationship of defects; when using industrial CT for scanning, the scanning method and scanning range are reasonably selected according to the outer diameter of the solid rocket motor.

3. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 1 or 2, characterized in that, The scanning method can be selected as either a whole-body scanning method or a partial scanning method according to the detection requirements. The whole-body scanning method is suitable for situations where the scanning range covers the entire cross-section of the solid rocket motor but the axial length is limited, while the partial scanning method is suitable for situations where the scanning range is smaller than the diameter of the solid rocket motor and focuses on covering areas with high incidence of defects.

4. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 3, characterized in that, The simulated scanning process involves mapping grayscale values ​​to ray attenuation coefficients according to Beer's Law, and combining the scanning parameters used in real CT scans to perform a simulated scan of the solid rocket motor cross-section model. The scanning parameters include translation step size, rotation angle, and scanning range. The simulation process of ray translation and rotation scanning inside the solid rocket motor is used to generate projection chord diagram data. The projection chord diagram is reconstructed using a filtered back projection algorithm to obtain a simulated CT image consistent with the real CT image.

5. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 4, characterized in that, In step S3: the real CT image and the simulated CT image are fused to form a continuous CT image sequence along the axis of the solid rocket motor. The CT image sequence is imported into the three-dimensional reconstruction environment in axial order. Three-dimensional voxels are constructed by corresponding pixels in two adjacent CT images. The moving cube algorithm is used to draw the surface of the target structure and generate a triangular mesh model composed of triangular facets.

6. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 5, characterized in that, The surface drawing process is as follows: based on the grayscale distribution characteristics of different internal structures of the solid rocket motor in CT images, the grayscale value range of the structure to be modeled in the two-dimensional CT image is selected as the threshold; for each of the eight vertices of the voxel, it is determined whether the grayscale value falls within the threshold range, the attribute value of the corresponding vertex is determined, and based on the distribution of the attribute values ​​of each vertex of the voxel, it is determined whether the grayscale isosurface passes through the voxel. When the attribute values ​​of the vertices of a volume element are not completely consistent, the intersection points of the isosurface and each edge of the volume element are determined by interpolation calculation within the volume element. Then, according to the topological connection rules of the moving cube algorithm, the intersection points are connected to the isosurface to form triangular patches. By repeating the above judgment and interpolation connection process for all volume elements, all triangular patches that meet the conditions are generated and connected in sequence to obtain the triangular patch mesh model.

7. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 6, characterized in that, In step S4: the generated triangular facet mesh model is imported into 3-matic software for volume mesh reconstruction. Volume mesh reconstruction includes preprocessing, iterative optimization, and volume mesh filling. Specifically, the triangular facet mesh model is preprocessed and then iteratively optimized to generate an optimized facet mesh model. The volume mesh generation tool in 3-matic software is used to fill the optimized facet mesh model with tetrahedral elements to obtain a volume mesh model that can be used for finite element analysis.

8. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 7, characterized in that, The preprocessing includes filling holes in the triangular mesh model, smoothing and optimizing local discontinuities, sharp regions, and rough meshes after filling, and removing redundant or distorted triangular patches; the iterative optimization involves gradually adjusting the cell size, shape, and distribution of the triangular mesh through multiple rounds of quality assessment and local correction of the triangular mesh model.

9. The finite element analysis method for solid rocket motors based on CT image fusion modeling according to claim 8, characterized in that, In step S5: the generated volume mesh model is imported into Abaqus finite element analysis software as the calculation model for the solid rocket motor finite element analysis. In Abaqus finite element analysis software, corresponding material parameters are set for different regions in the volume mesh model. According to the structural constraint relationship and actual working conditions of the solid rocket motor, corresponding boundary conditions and load conditions are applied to the volume mesh model. Finite element solution is performed on the volume mesh model to perform stress distribution simulation analysis and obtain the mechanical response distribution analysis results of stress and strain in each region inside the solid rocket motor. The health status of the solid rocket motor under test is assessed by analyzing the stress distribution in the defect area of ​​the analysis results; the stress response of the volume mesh model is compared with the expected results or experimental results to verify the effectiveness and accuracy of the volume mesh model; when the analysis results do not meet the evaluation requirements, the process returns to steps S3-S4 or S5 as needed to adjust the volume mesh model or analysis parameters and then perform finite element analysis again.

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