CN116913432A - Reconstruction method of propellant simulated filling model based on polygonal grid - Google Patents
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Abstract
The invention provides a reconstruction method of a propellant simulated filling model based on polygonal grids, which comprises the steps of scanning a propellant test piece, acquiring color images with sections at different heights inside, importing the color images into FIJI software, converting all the color images into gray images, and processing the gray images to obtain a three-dimensional real microscopic stretching model and a three-dimensional simulated filling stretching model; and carrying out polyhedral mesh division on the three-dimensional real microscopic stretching models and the three-dimensional simulated filling stretching models of all gray images, extracting surface meshes to obtain a polygonal discretized two-dimensional model, and adapting to the mesh unit types required by different numerical simulation means.
Description
Technical Field
The invention particularly relates to a reconstruction method of a propellant simulated filling model based on polygonal units, and belongs to the technical field of the microscopic mechanical properties of solid propellants.
Background
The composite solid propellant is a composite material which takes a high molecular polymer adhesive as a matrix and solid oxidant particles (AP particles) and metal fuel particles (Al particles) as inclusions. The development of the novel solid missile weapon brings higher requirements on the mechanical properties of the propellant, and the microscopic characteristic parameters such as the particle content, the particle size grading, the interface characteristics and the like of the propellant greatly influence the macroscopic mechanical properties of the propellant. At present, the development of the solid propellant in China mainly depends on an empirical or semi-empirical method, and optimization design of the mesoscopic characteristic parameters influencing the performance of the solid propellant cannot be completely realized, so that the method has extremely important significance for the numerical simulation technology of the mesoscopic structural model. The bottleneck problem faced by the new generation of solid engine in China is hopeful to be solved through the fine design of the solid propellant.
In current propellant micromechanics analysis, numerical simulation is mainly based on particle filling models generated by filling algorithms. Although a geometric model of the microscopic scale of the propellant can be conveniently generated through a particle filling algorithm, the filling model cannot effectively reflect the true microscopic structure of the propellant, and a plurality of unavoidable defects exist in numerical simulation. The real microscopic morphology of the propellant can be obtained by combining an X-CT experiment and a digital image processing technology, so that a real microscopic model of the propellant is built, and the simulation is more in line with the actual situation. Because of the long experimental period of X-CT, high cost and the limitation of aggregate filling, the real microscopic model cannot be applied in large scale in engineering practice.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a reconstruction method of a propellant simulated filling model based on polygonal units, which is used for verifying the effectiveness of the propellant simulated filling model.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the invention provides a reconstruction method of a propellant simulated filling model based on polygonal grids, which comprises the following steps:
scanning a propellant test piece, acquiring color images of sections with different heights inside, importing the color images into FIJI software, and converting all the color images into gray images;
processing each gray level image to obtain a three-dimensional real mesoscopic stretching model and a three-dimensional simulated filling stretching model;
performing polyhedral mesh division on the three-dimensional real mesoscopic stretching models and the three-dimensional simulated filling stretching models of all gray images, extracting surface meshes of the three-dimensional real mesoscopic stretching models and the three-dimensional simulated filling stretching models, and obtaining a polygonal discretization two-dimensional model;
the processing of each gray image comprises the following steps:
preprocessing the gray level image and dividing the components to obtain binarized images of each component,
performing morphological optimization and edge detection on the binarized images of each component to obtain the geometric coordinates of each component phase particle;
vectorizing the geometric coordinates of each component phase particle to construct a three-dimensional real microscopic stretching model of the propellant;
and carrying out connected domain analysis on the binarized images of each component to obtain equivalent diameter distribution of different particle ROIs, and generating a three-dimensional simulated filling and stretching model by combining a molecular dynamics algorithm.
Further, the propellant test piece is scanned, a plurality of color images in JPG or BMP format are obtained, and the color images imported into FIJI software are adjusted to 8-bit type gray images according to the scanning sequence.
Further, the composition segmentation includes:
and carrying out component segmentation on the preprocessed gray-scale image by adopting a TrainableWekasegment plug-in the FIJI software through a machine learning algorithm to obtain a binary image of each component.
Further, the performing morphological optimization and edge detection on the binarized image of each component to obtain the geometric coordinates of each component phase particle includes:
based on MATLAB software, performing morphological optimization on each component binary image by utilizing function strel definition construction elements to obtain each component phase particle;
smoothing each component phase grain boundary by using functions imonod and imdilate;
and extracting geometrical coordinates of the particle outline corresponding to the actual image size from the smoothed component phase particle boundaries by using functions edge, bwlabel and regionprops.
Further, vectorizing the geometric coordinates of each component phase particle to construct a three-dimensional true mesoscopic stretching model of the propellant, comprising:
based on a Spacelaim module and a Python circulation statement in WORKBENCH software, the geometric coordinates of each component phase particle are connected by utilizing a polygon or spline curve, and a three-dimensional true mesoscopic stretching model of the propellant is constructed by stretching.
Furthermore, the method is based on that a fluent Messaging module in WORKBENCH software performs polyhedral grid division on the three-dimensional real mesoscopic stretching model and the three-dimensional simulated filling stretching model, and extracts surface grids of the three-dimensional real mesoscopic stretching model and the three-dimensional simulated filling stretching model through MATLAB scripts.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a propellant simulated filling model construction method based on polygonal units, which can effectively replace a propellant real microscopic structural model in the prediction of the effective performance of the propellant.
In the reconstruction aspect of a real mesostructure model, the mesostructure model based on the particle outline can be used for carrying out arbitrary grid division, so that the phenomenon of calculation non-convergence caused by the fact that the mesostructure model based on the pixels cannot be reconstructed is avoided, and the effectiveness of mesostructure modeling is improved.
When the gray level image is preprocessed and divided into components, the invention combines with the machine learning algorithm plug-in the FIJI software, thereby avoiding the problem of inaccurate division caused by uneven distribution of the gray level value of the image.
The reconstruction method of the propellant simulated filling model based on the polygonal grid provided by the invention is used for establishing a three-dimensional real microscopic stretching model and a three-dimensional simulated filling stretching model, and reconstructing any polygonal grid unit, so that the discretized grid type of the geometric model is expanded, and the reconstruction method can adapt to the grid unit types required by different numerical simulation means.
Drawings
FIG. 1 is a flow chart of a method of reconstructing a propellant simulated filling model based on a polygonal mesh in accordance with an embodiment of the present invention;
FIG. 2 is a slice of an X-ray CT scan provided by an embodiment of the present invention;
FIG. 3 is a pre-processed micro CT image of a propellant provided by an embodiment of the present invention;
FIG. 4 is a binary image of an Al particle after segmentation based on a machine learning plug-in FIJI software provided by an embodiment of the present invention;
FIG. 5 is a graph providing an AP particle binary image segmented based on a machine learning plugin in FIJI software in accordance with an embodiment of the present invention;
FIG. 6 is a binary image of particle morphology optimization provided by an embodiment of the present invention;
FIG. 7 is a normal distribution of two particle sizes provided by an embodiment of the present invention;
FIG. 8 is a three-dimensional real mesoscopic stretching model image and a three-dimensional simulated filling stretching model image of a propellant constructed based on a SpaceLaim module in WORKBENCH software, wherein (a) is a three-dimensional real mesoscopic stretching model image and (b) is a three-dimensional simulated filling stretching model image;
FIG. 9 shows a three-dimensional real mesostretching model polyhedral meshing image and a three-dimensional simulated filling stretching model polyhedral meshing image of a propellant constructed based on a fluent Meishing module in WORKBENCH software, wherein (a) is a three-dimensional real mesostretching model image and (b) is a three-dimensional simulated filling stretching model image;
fig. 10 is a schematic diagram of a propellant three-dimensional real fine drawing model polygonal mesh division image and a three-dimensional simulated filling drawing model polygonal mesh division image constructed based on a fluent measurement module in WORKBENCH software, in which (a) is a three-dimensional real fine drawing model polygonal mesh division image and (b) is a three-dimensional simulated filling drawing model polygonal mesh division image.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, the method for constructing the propellant simulated filling model based on the polygonal unit specifically comprises the following steps:
scanning a propellant test piece by adopting X-ray CT equipment to obtain color images (total N pieces) with different internal height sections, performing batch reading and importing operations by utilizing FIJI software according to an image scanning sequence, and converting all the color images into gray images;
and (2) selecting an ith micro CT image of the propellant for preprocessing and dividing the components to obtain binary images of each component phase. Carrying out connected domain analysis based on the binary images of each component phase, and counting equivalent diameter distribution of different particle ROIs;
and (3) performing reading and importing operations by using MATLAB software according to the binary image obtained in the step (2). Performing morphological optimization and edge detection on the binary image of the component phase, and mapping the binary image into geometric coordinates of particles of each component phase;
and (4) vectorizing the particle profile of each component phase obtained in the step (3) based on the Spaceclaim module and the secondary development of Python in WORKBENCH software, and constructing a three-dimensional real microscopic stretching model of the propellant. In addition, the statistical information of the equivalent diameter of the particles of each component phase obtained in the step (2) is combined with a molecular dynamics algorithm to generate a corresponding three-dimensional simulated filling and stretching model;
step (5), based on a fluent measurement module in WORKBENCH software, performing polyhedral meshing on the two-propellant three-dimensional stretching microscopic model generated in the step (4), and extracting surface meshes of the geometric model through MATLAB scripts to obtain polygonal meshing of the two-dimensional model;
and (6) processing the rest N-1 propellant micro CT images sequentially through steps (2) - (5) according to the scanning sequence to obtain three-dimensional real micro-stretching models and three-dimensional simulated filling stretching models corresponding to sections with different heights, and carrying out polygonal grid division on the three-dimensional real micro-stretching models and the three-dimensional simulated filling stretching models.
Wherein: in the step (1), an X-ray CT device is adopted to scan a propellant test piece, N propellant CT scanning color images in JPG or BMP format are obtained, and FIJI is used for adjusting the color images into 8-bit type gray images in batches according to the scanning sequence.
In the step (2), the preprocessed propellant micro CT image adopts TrainableWeka Segmentation plug-in FIJI software, and the components are segmented through a machine learning algorithm to obtain initial binary images of the components respectively. And carrying out connected domain measurement on the binary images of different components to obtain the equivalent diameter of the ROI representing the particles, and analyzing the normal distribution of the equivalent diameter.
In the step (3), construction elements with proper shapes and sizes are defined by utilizing a function strel based on MATLAB software, smooth grain boundaries are achieved by utilizing functions imoode and imdilate, and geometric coordinates of grain contours corresponding to actual image sizes are extracted by utilizing functions edge, bwlabel and regionprops.
In the step (4), the particle contour of each component phase obtained in the step (3) is constructed based on the Spaceclaim module and the secondary development of Python in WORKBENCH software, and a three-dimensional real microscopic stretching model of the propellant is obtained through the operations of intersection, merging, stretching and the like. In addition, anotherIn addition, the normal distribution of the equivalent diameter of the particles of each component phase obtained in the step (2) is combined with a molecular dynamics algorithm to generate corresponding three-dimensionalSimulated filling stretching model。
In the step (5), based on the fluent Meshing module in the WORKBENCH, the three-dimensional stretching model of the propellant constructed in the step (4), namely the three-dimensional real mesoscopic stretching model and the three-dimensional simulated filling stretching model, is subjected to polyhedral meshing. The surface grids are extracted to obtain two-dimensional polygonal grid division of two models, and the two-dimensional polygonal grid division is used as a pretreatment part of a numerical simulation means based on polygonal discretization.
The specific reconstruction method is illustrated in connection with the following examples:
as shown in FIG. 1, a propellant simulated filling model construction method based on polygonal units comprises two parts of contents of image recognition and numerical modeling. First, the components are optimally partitioned based on a machine learning algorithm. And secondly, combining MATLAB image processing and edge detection data to generate a three-dimensional real microscopic stretching model and a three-dimensional simulated filling stretching model. Finally, polygon meshing is carried out on the model based on WORKBENCH-FluentMesh.
As shown in FIG. 2, the propellant scans slice images, in this example HTPB propellant was chosen as the example object, and the micro CT apparatus used required a spatial resolution of 1 μm and a scan specimen size of 2mm 4mm. After scanning, the original CT color image file arranged according to a certain scanning sequence can be obtained, and the file format is BMP.
Batch reading and importing operations are carried out according to an image scanning sequence by utilizing FIJI software, and all color images are converted into gray images;
as shown in fig. 3, the ith microct image of the propellant is selected for preprocessing, mainly including image magnification and image enhancement.
As shown in fig. 4 and 5, the trainablewekasegment plug-in FIJI software segments the Al and AP particles by a machine learning algorithm to obtain binary images of the Al and AP particles.
As shown in fig. 6, the MATLAB software is used for reading and importing operations, the function strel is used for defining the construction elements with proper shapes and sizes, the functions imoode and imdilate are used for smoothing the grain boundaries, and the functions edge, bwlabel and regionprops are used for extracting the geometric coordinates of the grain contours corresponding to the actual image sizes.
The method comprises the following steps:
based on MATLAB software, performing morphological optimization on each component binary image by utilizing function strel definition construction elements to obtain each component phase particle;
smoothing each component phase grain boundary by using functions imonod and imdilate;
and extracting geometrical coordinates of the particle outline corresponding to the actual image size from the smoothed component phase particle boundaries by using functions edge, bwlabel and regionprops.
As shown in fig. 7, connected domain analysis is performed based on the binary image of each component phase, and the equivalent diameter distribution of different particle ROIs is counted;
as shown in fig. 8 (a), the particle profile coordinates of each component phase are connected by using a polygon or spline curve based on the secondary development of the SpaceClaim module and Python in the workbelch software, that is, combining macro recording and Python circulation sentences in the SpaceClaim, and a three-dimensional true mesoscopic stretching model of the propellant is constructed through stretching operation.
As shown in fig. 8 (b), a corresponding three-dimensional simulated filling stretch model is generated by combining the statistical information (normal distribution) of the particle equivalent diameters of the component phases with a molecular dynamics algorithm;
as shown in (a) and (b) in fig. 9, based on a fluent measurement module in the WORKBENCH software, performing polyhedral meshing on the three-dimensional stretching microscopic model of the two propellants, merging in-phase areas, and deriving a boundary mesh file;
as shown in fig. 10, grids belonging to the required surface in the boundary grid file are extracted based on MATLAB script to obtain two-dimensional polygonal grid division of two models.
The MATLAB script flow is as follows:
(1) Removing unwanted surface nodes and associated units;
(2) Updating the required surface nodes and the related unit numbers.
Repeating the steps, sequentially processing the rest N-1 propellant micro CT images according to the scanning sequence, and outputting a grid file.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (6)
1. A method for reconstructing a propellant simulated filling model based on polygonal meshes, comprising:
scanning a propellant test piece, acquiring color images of sections with different heights inside, importing the color images into FIJI software, and converting all the color images into gray images;
processing each gray level image to obtain a three-dimensional real mesoscopic stretching model and a three-dimensional simulated filling stretching model;
performing polyhedral mesh division on the three-dimensional real mesoscopic stretching models and the three-dimensional simulated filling stretching models of all gray images, extracting surface meshes of the three-dimensional real mesoscopic stretching models and the three-dimensional simulated filling stretching models, and obtaining a polygonal discretization two-dimensional model;
the processing of each gray image comprises the following steps:
preprocessing the gray level image and dividing the components to obtain binarized images of each component,
performing morphological optimization and edge detection on the binarized images of each component to obtain the geometric coordinates of each component phase particle;
vectorizing the geometric coordinates of each component phase particle to construct a three-dimensional real microscopic stretching model of the propellant;
and carrying out connected domain analysis on the binarized images of each component to obtain equivalent diameter distribution of different particle ROIs, and generating a three-dimensional simulated filling and stretching model by combining a molecular dynamics algorithm.
2. The reconstruction method according to claim 1, wherein the propellant test piece is scanned to obtain a plurality of color images in JPG or BMP format, and the color images imported into FIJI software are adjusted to 8-bit type gray scale images according to the scanning order.
3. The reconstruction method according to claim 1, wherein the component segmentation comprises:
and carrying out component segmentation on the preprocessed gray-scale image by adopting a TrainableWekasegment plug-in the FIJI software through a machine learning algorithm to obtain a binary image of each component.
4. The reconstruction method according to claim 1, wherein the performing morphological optimization and edge detection on the binary image of each component to obtain geometric coordinates of particles of each component phase comprises:
based on MATLAB software, performing morphological optimization on each component binary image by utilizing function strel definition construction elements to obtain each component phase particle;
smoothing each component phase grain boundary by using functions imonod and imdilate;
and extracting geometrical coordinates of the particle outline corresponding to the actual image size from the smoothed component phase particle boundaries by using functions edge, bwlabel and regionprops.
5. The method of claim 1, wherein vectorizing the geometric coordinates of each component phase particle to construct a three-dimensional true mesoscopic stretching model of the propellant comprises:
based on a Spacelaim module and a Python circulation statement in WORKBENCH software, the geometric coordinates of each component phase particle are connected by utilizing a polygon or spline curve, and a three-dimensional true mesoscopic stretching model of the propellant is constructed by stretching.
6. The reconstruction method according to claim 1, wherein the method is based on polyhedral mesh division of a three-dimensional real mesoscopic stretching model and a three-dimensional simulated filling stretching model by a fluent measurement module in the WORKBENCH software, and extraction of surface meshes of the three-dimensional real mesoscopic stretching model and the three-dimensional simulated filling stretching model is performed by a MATLAB script.
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