CN112632814A - Method for constructing three-dimensional model of recycled concrete based on ellipsoid random aggregate - Google Patents
Method for constructing three-dimensional model of recycled concrete based on ellipsoid random aggregate Download PDFInfo
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Abstract
A method for constructing a recycled concrete three-dimensional model based on ellipsoid random aggregate comprises the following steps: step one, compiling a script algorithm for establishing an ellipsoid aggregate model by utilizing Python programming; introducing a random aggregate particle size function into a Python script algorithm according to a statistical simulation theory, and randomly generating a random aggregate particle size of 5-25 mm; thirdly, a statistical test method is utilized to program the aggregate random putting function theory into a Python script algorithm, so that the random putting of the ellipsoid aggregate in the model space is realized; establishing an ellipsoid recycled aggregate and ellipsoid natural aggregate mesoscopic structure model by utilizing a Python language, establishing a three-dimensional composite material frame model and defining the attributes of the model composite material; writing an Abaqus post-processing analysis programming algorithm, and combining the whole modeling and simulation analysis process to realize the automatic generation of the whole simulation test process; the method has the characteristics of simplicity, rapidness and high efficiency.
Description
Technical Field
The invention belongs to the technical field of recycled concrete three-dimensional model construction, and particularly relates to a method for constructing a recycled concrete three-dimensional model based on ellipsoid random aggregates.
Background
With the rapid development of the urbanization process in China, a large amount of waste concrete becomes problem garbage which needs to be treated urgently. The waste concrete can be recycled to generate recycled concrete, and the recycled material can be used in road engineering, low-rise buildings and other engineering. The application of the recycled concrete to the actual engineering is an important way for solving the construction waste, thereby realizing the aim of green sustainable development of resources.
In recent years, researchers have conducted experimental studies on the macroscopic mechanical properties of recycled concrete, such as "split bond strength" and "compressive strength". The macroscopic mechanical property of concrete is necessarily connected with the internal damage development of concrete. The experimental research has certain limitation on the research of the mesoscopic structure of the recycled concrete aggregate, and the computer simulation method provides a lot of convenience for the research of the mesoscopic structure of the recycled concrete aggregate. Research on the mesoscopic mechanical properties of recycled concrete based on mesoscopic models has become a key topic in the field of recycled concrete research nowadays, such as: korean et al establishes a three-dimensional aggregate model based on an improved Fractal Brownian Motion (FBM) and a random midpoint displacement method. The real recycled concrete meso-finite element analysis method is provided by combining the digital image processing and the geometric vector conversion technology with the finite element mesh automatic generation method. The Xiaojianzhuang and the like establish a recycled concrete coarse aggregate mesoscopic structure model on the basis of a digital image processing technology. The Matlab is used by Mikaihua to generate a concrete three-dimensional spherical random microscopic aggregate structure model. Guoreqi proposes a mixing method for generating a concrete mesoscopic model containing high volume fraction ratio spherical aggregate based on Fortran language and ANSYS simulation software. A method for dividing a concrete model into grids is provided for soldiers and the like based on Visual C + +. The Chinese traditional medicine has the advantages that a program containing convex polyhedral aggregate is compiled, and the generation of a random convex polygonal aggregate model is realized. Analysis shows that students mainly use digital image processing technology and Matlab, Fortran and other languages to carry out microscopic structure numerical simulation research on recycled concrete, and some research results are obtained, but the research results have defects, such as: the digital image processing technology has higher difficulty, high cost, large processing information amount and low practicability; the development efficiency of the languages such as Matlab and Fortran is low, the execution efficiency of large-scale data operation is low, the advantages of cross-platform portability, expandability and the like are not provided, the languages are difficult to be directly applied to simulation software such as Abaqus and the like for simulation modeling and analysis processing, and certain difficulty is brought to the study on the performance of the microstructure of the recycled concrete. The students realize the establishment of the three-phase mesoscopic structure three-dimensional aggregate models of the concrete such as spheres, convex polyhedrons and the like, but the five-phase or six-phase recycled aggregate three-dimensional mesoscopic structure aggregate models in the recycled concrete field are not related basically.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for constructing a recycled concrete three-dimensional model based on ellipsoid random aggregate, which can be used for establishing an ellipsoid aggregate model and meeting the simulation test requirements of any ellipsoid aggregate model and has the characteristics of simplicity, rapidness and high efficiency.
The technical scheme adopted by the invention is as follows: a method for constructing a recycled concrete three-dimensional model based on ellipsoid random aggregate comprises the following steps:
step one, compiling a script algorithm for establishing an ellipsoid aggregate model by utilizing Python programming;
introducing a random aggregate particle size function into a Python script algorithm according to a statistical simulation theory, and randomly generating a random aggregate particle size of 5-25 mm;
thirdly, a statistical test method is utilized to program the aggregate random putting function theory into a Python script algorithm, so that the random putting of the ellipsoid aggregate in the model space is realized;
establishing an ellipsoid recycled aggregate and ellipsoid natural aggregate mesoscopic structure model by utilizing a Python language, establishing a three-dimensional composite material frame model and defining the attributes of the model composite material;
and fifthly, writing an Abaqus post-processing analysis programming algorithm, and combining the whole modeling and simulation analysis process to realize the automatic generation of the whole simulation test process.
The first step specifically comprises the following steps:
1) defining the probability of the particle size distribution of the aggregate,
2) defining an aggregate type and an aggregate parameter type, firstly defining an aggregate type,
3) inputting basic information of aggregate parameters of the ellipsoid, including the aggregate percentage of the outermost ring, the replacement rate of recycled aggregate, the size of a model, the particle size range, the interface thickness of aggregate and old mortar, the thickness of old mortar, the interface thickness of old mortar and new mortar, the interface thickness of natural aggregate and new mortar, the ratio of the short axis to the long axis of the ellipsoid, the ratio of the middle axis to the short axis, the size of a grid and an applied load value.
The second step specifically comprises the following steps:
1) a random particle size function is built in, and when the sum of the accumulated sum and the maximum aggregate volume of the grade is less than the aggregate volume sum of the grade, aggregate is continuously added;
2) when the aggregate particle size is generated, prompting to output the generated aggregate particle size;
3) the proportion of the aggregates containing the interface layer is output, and when the proportion of the aggregates containing the external interface layer is 50%, the total proportion of the aggregates can reach 85% at most, so that the aggregates cannot be completely embedded.
The third step specifically comprises the following steps:
1) storing the particle size location;
2) appointing a limited putting position, reducing the calculated amount, dividing the space into uniform small lattices, and only putting the aggregate on the lattices, so that the delivery rate can be greatly improved, and the central position of the lattices can be obtained in a circulating manner;
3) recording the number of aggregates which fail to be put and the particle size of the aggregates;
4) feeding aggregate once from large to small, recording the number of times of trying to feed, copying the position of trying to feed, and recording success or failure of feeding, wherein if the feeding fails, the default is that the feeding failure numerical value is 0;
5) in order to avoid that the two aggregates are too close, setting an aggregate gap of 0.5mm, and when the random throwing times are less than 1000 times, positions which can be thrown in the tryPosition exist, and the throwing fails, continuing to throw; obtaining the selected position from the tryPosition, subtracting the selected point from the tryPosition value, avoiding repeated selection, and accumulating the number of attempts by 1;
6) when the distance boundary of the throwing position is smaller than rd, wherein rd represents the minimum tolerance between the two aggregates, the throwing directly fails, the next round of circulation is started if the throwing fails, and otherwise, the throwing is assumed to be successful;
7) calculating the distance between two successfully thrown aggregates, if the distance between two ellipsoidal aggregates is smaller than the sum of the distance between the spherical centers of the two aggregates and the minimum tolerance (namely 2 times the thickness of the outer rubber layer and 0.5mm), the throwing fails, exiting the cycle of distance judgment and entering the next attempt of the position of the aggregate; when the releasing is successful, recording the releasing position;
the fourth step specifically comprises the following steps:
1) circularly generating ellipsoid components, planing the ellipsoid components, defining material properties, and assembling the planed ellipsoid components to an assembly body;
2) randomly rotating the aggregate angle and translating to a specified position;
3) creating a square outer frame for containing aggregate and giving material properties;
4) all the ellipsoidal aggregates are combined, hundreds of ellipsoidal aggregates are combined at the same time, the program running speed is slow, so that the ellipsoidal aggregates can be combined in batches, at most 50 aggregates are combined in each batch, and finally, the ellipsoidal aggregates are combined for the second time, so that the combining speed can be improved;
5) and combining all parts of the ellipsoidal aggregate, defining material properties of the parts, defining materials at all positions of the small ellipsoidal parts before combination, and continuously retaining the defined aggregate part materials after combination, so that only new slurry materials need to be defined.
The fifth step specifically comprises the following steps:
1) grid division, wherein the same part is divided by adopting the two units, the connected interfaces are not matched, a program can be automatically replaced by tie, the definition is more accurate than manual definition, the new slurry part is subjected to tetrahedral grid division, and the type of grid unit and the size of a global grid are defined;
2) searching all outer boundaries, setting the size of the grids on the outer boundaries to be twice of that of the inner boundaries, then carrying out grid division and deleting redundant components;
3) establishing an analysis step and modifying output;
4) creating a reference point, useful for coupling the constraint and the output; outputting a reference point process; modifying the control parameters; a reference point coupling constraint;
5) fixing and constraining; loading constraints; creating a task; and saving the model.
The invention has the beneficial effects that:
1) the method can realize the automatic generation of the ellipsoidal composite aggregate through a Python programming algorithm, can automatically finish the intrusion judgment of the ellipsoidal aggregate, the random generation of the aggregate particle size and the random placement of the aggregate position, and establishes the ellipsoidal random aggregate model.
2) The invention can establish any desired substitution rate of the ellipsoid recycled aggregate and the volume percentage of the ellipsoid total aggregate, and the total aggregate percentage can reach 85 percent. Can meet the requirements of simulation tests of any ellipsoid aggregate model.
3) The algorithm can run the algorithm script through the Abaqus simulation software, an ellipsoid three-dimensional composite material random aggregate model can be established in the Abaqus without any other intermediary software, and the ellipsoid aggregate mesoscopic structure simulation test analysis and calculation are directly carried out, so that the method is simple, fast and efficient.
Drawings
FIG. 1(a) is a schematic diagram of an ellipsoid recycled aggregate constructed by Python according to the present invention.
FIG. 1(b) is a schematic diagram of an ellipsoidal natural aggregate constructed by using Python according to the present invention.
FIG. 2 is a schematic diagram of determining the position of an ellipsoid.
FIG. 3 is a flow chart of the algorithm of the present invention.
FIG. 4(a) is a model diagram of a calculation of 40% aggregate content.
FIG. 4(b) is a diagram showing a calculation model of the aggregate content of 60%.
FIG. 4(c) is a diagram showing a calculation model of the aggregate content of 70%.
FIG. 4(d) is a model diagram of the calculation of the aggregate content of 85%.
FIG. 5(a) is a model diagram showing the calculation of the replacement ratio of recycled aggregate of 50%.
FIG. 5(b) is a model diagram showing the calculation of the replacement ratio of recycled aggregate of 70%.
FIG. 5(c) is a calculation model diagram showing a recycled aggregate substitution rate of 90%.
FIG. 6(a) is a diagram of a computational model for creating a reference point and coupling.
Fig. 6(b) is a computational model diagram of a divided grid.
Fig. 6(c) is a calculation model diagram of the applied load.
Fig. 7 is a total stress cloud.
Fig. 8 is a stress cloud of S11.
Fig. 9 is a stress cloud of S22.
Fig. 10 is a stress cloud of S33.
Fig. 11(a) is a graph of single-pressure initial damage.
Fig. 11(b) is a development view of a single-pressure injury.
Fig. 11(c) is a graph showing aggravation of single-pressure injury.
Fig. 11(d) is a single-pressure damage completion diagram.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
A method for constructing a three-dimensional model of recycled concrete based on ellipsoid random aggregate is provided, which aims to solve the following problems: and (5) building an ellipsoid aggregate model.
The technical scheme for realizing the first purpose of the invention is as follows: the Python programming statement originally has rich and powerful databases and can realize comprehensive data processing functions, and the Python has the advantages of portability, expandability, interpretability, object-oriented and flexible memory management, is sensitive, convenient and quick in embeddability, and can be directly embedded into Abaqus simulation software to establish a model. Therefore, a Python language algorithm can be self-compiled to solve the problem of defining the distribution probability of the aggregate particle size; the major axis, the middle axis and the minor axis of the ellipsoid aggregate; and aggregate parameter information such as the aggregate rotation angle and the aggregate category and the like, so as to generate the ellipsoidal aggregate.
The Python statement can realize the establishment of an ellipsoid aggregate model, the ratio of a short axis to a long axis of an ellipsoid aggregate is defined to be 0.3-0.8 through a programming algorithm, the ratio of the short axis to a middle axis is 1, the range of the particle size long axis is 5-25 mm, the rotation angle of the long axis of the aggregate relative to an X positive axis coordinate is 0-pi, the rotation angle of the middle axis of the aggregate relative to a Z positive axis coordinate is 0-pi, the thickness of an interface area of the aggregate and old mortar is defined to be 0.5mm, the thickness of an old mortar area is defined to be 1mm, the thickness of an interface area of old mortar and new mortar is 0.4mm, and the thickness of.
The schematic diagrams of the ellipsoid natural aggregate and the ellipsoid recycled aggregate established by using Python are the microscopical structures shown in figures 1(a) to (b).
The second purpose of the invention is to solve the following problems: an aggregate feeding method and an ellipsoid aggregate invasion judgment problem.
The technical scheme for realizing the second purpose of the invention is as follows: after an ellipsoid aggregate model is generated by using Python language, the generated aggregate particle size and aggregate are recorded in a particle size library, then the model space size of 60 multiplied by 60mm3 is defined, the model space is divided into uniform small lattices, then the aggregate particle size is thrown in from large to small, and the ellipsoid aggregate invasion judgment is needed in the throwing process.
Compared with the intrusion discrimination principle of the spherical random aggregate model and the convex polyhedral random aggregate model, the intrusion discrimination of the ellipsoidal random aggregate model is difficult to perform. The method has to satisfy the discrimination condition that two ellipsoidal aggregates cannot be superposed and the discrimination condition that the aggregates and the boundary plane of the model do not intersect, and the two discrimination conditions are either absent or not.
Judging the position relation between two ellipsoid aggregates firstly thinks of judging by using an ellipsoid equation algebraic method, namely:
first, several algebraic formulas are defined to determine the distance between two ellipsoids:
1) introducing two non-zero vectors of x and y, and calculating the included angle between the two vectors
Theta (x, y) is more than or equal to 0 and less than or equal to pi; in the formula, xTIs the transposed vector of the non-zero vector x, and theta is the angle between the vectors x and y.
2) Assuming that an ellipsoid is a sphere with a sphere center as an algebraic number y and a radius as an algebraic number beta, defining the sphere as B (y; beta) is equal to { x: | y-x | | is less than or equal to beta };
3)xkand ykCalculate the point, x, for the kth iterationk+1And yk+1Calculating points for the (k + 1) th iteration;
the distance between the two ellipsoids is determined according to the following idea:
two ellipsoid equations are defined: ellipsoid EA：
Ellipsoid EB：
Then in ellipsoid EAAnd EBRandomly taking two point column vectors { x }on the sphere interfacekAnd { y }kAnd d (E)A,EB)＝limk→∞||xk-ykL, where xkAnd ykFor the k-th iteration, d is the ellipsoid EAAnd EBThe distance between two points on the sphere interface of (1).
As shown in FIG. 2, the distance determination method is to use iterative calculation to determine item by item, for example, at the k-th iterative calculation, at xkDot sum ykAssuming an inscribed sphere A (c) at each point1，r1) With inscribed sphere B (c)2，r2) And the sphere A is internally tangent with an ellipsoid EAAt xkEllipsoid inscribed in sphere BBIn yk. Connecting two ball centers c1And c2Is line segment c1c2Then judging the condition c1c2∈EA∪EBIf the conditions are met, the two ellipsoids are intersected or tangent, and the two ellipsoids are overlapped; otherwise, making a phase separation judgment.
Judging that the ellipsoid aggregate and the model boundary do not intersect, namely judging the distance relationship between the ellipsoid and the plane, namely:
firstly, defining an ellipsoid equation and a plane equation: an ellipsoid A:
plane B, Ax + By + Cz + D is 0
The problem of the minimum distance from one point on the ellipsoid A to the plane B can be solved by judging the position relation between the ellipsoid and the plane. Suppose there is a point (x)0,y0,z0) And randomly moving on the ellipsoid A, and judging the minimum distance from the point to the plane B. The equation for this point and the plane is:
its normal vector is
There is a minimum distance d when the normal vector is perpendicular to plane B. When d is less than or equal to 0, the ellipsoid A is intersected or tangent with the plane B; when d is larger than 0, the two are separated to meet the judgment requirement.
In conclusion, the algebraic algorithm principle is complicated and complicated, and the method is difficult to apply to intrusion judgment of an actual ellipsoid aggregate model. In order to realize the position judgment among the ellipsoidal aggregates, firstly, a model space is divided into symmetrical square grids by utilizing Python language, and then a spherical matrix is generated on the basis of the concept method of generating a spherical thinking by predecessors. Then on the basis of the spherical matrix, firstly putting the spherical matrix into the formed model space, and putting the generated ellipsoidal aggregate into the spherical matrix by using the position of the formed spherical matrix, so that the algorithm disclosed by the invention simplifies the position discrimination of the ellipsoid into a position distance discrimination principle among the spherical aggregates, and compared with a primary numerical calculation method, the algorithm principle is convenient, easy, fast and effective, and the algorithm theory can be used for rapidly generating the ellipsoidal random aggregate model through Python sentences.
The third purpose of the invention is to solve the following problems: the Abaqus post-processing simulation analysis and the Python modeling algorithm are linked, the automatic modeling and the post-processing analysis of the Abaqus are connected in series, and the automatic generation of the whole process of an Abaqus simulation test is realized by using a Python statement, so that the research of an ellipsoid simulation test can be developed more efficiently and conveniently.
The technical scheme for realizing the third purpose of the invention is as follows: the Python language is between the script language and the system language and is an open source code. The method has the advantages of wide embeddability, portability and the like, and can be operated on a plurality of different system platforms. Therefore, an Abaqus postprocessing analysis algorithm can be written by utilizing Python language, so that the automatic generation of an ellipsoid random aggregate model is combined with the steps of an Abaqus finite element numerical simulation postprocessing program, the automatic generation of the whole process of an ellipsoid recycled aggregate concrete simulation test is realized, and the research development of any ellipsoid aggregate model simulation test is facilitated.
The algorithm of the invention provides a simple and rapid test process processing method for the simulation test technology in the field of recycled concrete research.
A method for constructing a recycled concrete three-dimensional model based on ellipsoid random aggregate comprises the following steps:
step one, compiling a script algorithm for establishing an ellipsoid aggregate model by utilizing Python programming, which comprises the following specific steps:
1) defining the probability of the particle size distribution of the aggregate,
2) defining an aggregate type and an aggregate parameter type, firstly defining an aggregate type,
3) inputting basic information of ellipsoid aggregate parameters, including outermost ring aggregate percentage, recycled aggregate substitution rate, model size, particle size range, aggregate and old mortar interface thickness, old mortar and new mortar interface thickness, natural aggregate and new mortar interface thickness, ellipsoid minor axis and major axis ratio, central axis and minor axis ratio, grid size and applied load value;
step two, introducing a random aggregate particle size function into a Python script algorithm according to a statistical simulation theory, and randomly generating a random aggregate particle size of 5-25 mm, wherein the specific method in the step is as follows:
1) a random particle size function is built in, and when the sum of the accumulated sum and the maximum aggregate volume of the grade is less than the aggregate volume sum of the grade, aggregate is continuously added;
2) when the aggregate particle size is generated, prompting to output the generated aggregate particle size;
3) the aggregate proportion containing the interface layer is output, and when the direct aggregate proportion containing the external interface layer is 50%, the total aggregate proportion can reach 85% at most, so that the aggregate is difficult to be completely embedded;
step three, a statistical test method is utilized to program the aggregate random putting function theory into a Python script algorithm to realize the random putting of the ellipsoid aggregate in the model space, and the specific method of the step is as follows:
1) storing the particle size location;
2) appointing a limited putting position, reducing the calculated amount, dividing the space into uniform small lattices, and only putting the aggregate on the lattices, so that the delivery rate can be greatly improved, and the central position of the lattices can be obtained in a circulating manner;
3) recording the number of aggregates which fail to be put and the particle size of the aggregates;
4) feeding aggregate once from large to small, recording the number of times of trying to feed, copying the position of trying to feed, and recording success or failure of feeding, wherein if the feeding fails, the default is that the feeding failure numerical value is 0;
5) in order to avoid that the two aggregates are too close, setting an aggregate gap of 0.5mm, and when the random throwing times are less than 1000 times, positions which can be thrown in the tryPosition exist, and the throwing fails, continuing to throw; obtaining the selected position from the tryPosition, subtracting the selected point from the tryPosition value, avoiding repeated selection, and accumulating the number of attempts by 1;
6) when the distance boundary of the throwing position is smaller than rd, wherein rd represents the minimum tolerance between the two aggregates, the throwing directly fails, the next round of circulation is started if the throwing fails, and otherwise, the throwing is assumed to be successful;
7) calculating the distance between two successfully thrown aggregates, if the distance between two ellipsoidal aggregates is smaller than the sum of the distance between the spherical centers of the two aggregates and the minimum tolerance (namely 2 times the thickness of the outer rubber layer and 0.5), the throwing fails, exiting the cycle of distance judgment and entering the attempt of the next aggregate position; when the releasing is successful, recording the releasing position;
establishing an ellipsoid recycled aggregate and ellipsoid natural aggregate mesoscopic structure model by utilizing a Python language, establishing a three-dimensional composite material frame model and defining the properties of the model composite material, wherein the specific method comprises the following steps:
1) circularly generating ellipsoid components, planing the ellipsoid components, defining material properties, and assembling the planed ellipsoid components to an assembly body;
2) randomly rotating the aggregate angle and translating to a specified position;
3) creating a square outer frame for containing aggregate and giving material properties;
4) all the ellipsoidal aggregates are combined, hundreds of ellipsoidal aggregates are combined at the same time, the program running speed is slow, so that the ellipsoidal aggregates can be combined in batches, at most 50 aggregates are combined in each batch, and finally, the ellipsoidal aggregates are combined for the second time, so that the combining speed can be improved;
5) and combining all parts of the ellipsoidal aggregate, defining material properties of the parts, defining materials at all positions of the small ellipsoidal parts before combination, and continuously retaining the defined aggregate part materials after combination, so that only new slurry materials need to be defined.
Writing an Abaqus postprocessing analysis programming algorithm, and combining the whole modeling and simulation analysis process to realize the automatic generation of the whole simulation test process, wherein the specific method in the step is as follows:
1) grid division, wherein the same part is divided by adopting the two units, the connected interfaces are not matched, a program can be automatically replaced by tie, the definition is more accurate than manual definition, the new slurry part is subjected to tetrahedral grid division, and the type of grid unit and the size of a global grid are defined;
2) searching all outer boundaries, setting the size of the grids on the outer boundaries to be twice of that of the inner boundaries, then carrying out grid division and deleting redundant components;
3) establishing an analysis step and modifying output;
4) creating a reference point, useful for coupling the constraint and the output; outputting a reference point process; modifying the control parameters; a reference point coupling constraint;
5) fixing and constraining; loading constraints; creating a task; and saving the model.
Namely, the algorithm:
1) algorithm
(1) Introducing a random aggregate particle size function
(2) Defining aggregate classes and aggregate parameter classes
(3) Defining aggregate model parameters, and setting parameter algorithm as follows:
1) and judging whether the sum of the accumulated sum and the maximum aggregate volume of the grade is less than the aggregate volume sum of the grade, if so, continuously increasing the aggregates, otherwise, the aggregate particle size is in proportion to the total aggregates.
2) And outputting aggregate particle size parameters of the ellipsoid aggregate of each level, such as a long axis, a middle axis, a short axis and the like, and recording the aggregate particle size parameters to a total aggregate particle size library.
3) And (3) putting the generated aggregate according to the aggregate particle size from large to small, recording the aggregate particle size position of the put aggregate, and then calculating the position distance of the two ellipsoidal aggregates to judge the aggregate invasion principle. If the aggregate position meets the principle requirement, outputting and recording the aggregate feeding position and the number of aggregate particles; if the requirement is not met, the aggregate particle size position is stored again.
4) And when the total aggregate proportion and the recycled aggregate substitution rate meet the requirement of the test design, finishing the feeding of the aggregates and recording the total aggregate particle number and the aggregate position. And then circulating and generating the aggregate component parts and assembling the aggregate component parts into the generated ellipsoidal aggregate structure, and defining and endowing the aggregate component parts with material properties.
5) And the generated composite aggregate is merged into the model space, and then the residual model space except the total volume occupied by the aggregate which is put into the model space is defined and endowed as new mortar.
6) And after the ellipsoid recycled aggregate model is established, writing and establishing an Abaq us post-processing analysis step by using a Python language and then storing the model.
2) Algorithm design
The algorithm design flow is shown in fig. 3.
2 examples of calculation
The method can quickly generate an ellipsoidal recycled aggregate concrete microscopic structure model, and takes a model with the total aggregate content of 40%, 60%, 70% and 85% as an example (shown in figures 4(a) to (d)), and the aggregate content of the method which can be correspondingly added can reach 85% at most.
Calculation models of recycled concrete having substitution rates of 50%, 70% and 90% when the total aggregate content is 40% are shown in FIGS. 5(a) to (c).
Referring to fig. 5(a) - (c), the invention verifies the rationality and randomness of the ellipsoidal recycled aggregate replacement ratio algorithm from fig. 5(a), 5(b) and 5 (c). As can be seen from fig. 5(a) to 5(b), the number of the ellipsoidal recycled aggregates increases as the replacement ratio of the recycled aggregates increases. And because the establishment of the model is influenced by the random aggregate particle size function and the random aggregate feeding function in the algorithm, the random distribution of the aggregate particle size and the aggregate position in the model space in the graph can be seen, and the accuracy of the ellipsoid random aggregate model established by the algorithm is demonstrated.
An ellipsoidal recycled aggregate concrete model Abaqus postprocessing analysis algorithm written by using Python language is embedded into Abaqus simulation software, and with ellipsoidal randomly recycled aggregate concrete with a substitution rate of 70% as an example, postprocessing analysis obtained by embedding the algorithm into the Abaqus software is shown in (a) and (b) of FIG. 5.
Referring to FIG. 6(a), the algorithm of the present invention creates reference points RP-1 and RP-2, then creates a lower coupling constraint on reference point RP-1 and an upper coupling constraint on reference point RP-2.
Referring to fig. 6(b), the algorithm of the present invention uses both C3D20R and C3D15 cells to partition the same part in the model, and tetrahedral meshing of the new slurry portions will not match at the joined interfaces but the program will automatically replace with tie.
Referring to fig. 6(c), when the algorithm of the present invention applies a load to a model specimen, it is first necessary to fix the upper coupling constraint, and then the specimen uniaxial load is applied on the RP-2 reference point.
Simulation analysis was performed using an ellipsoidal randomly recycled aggregate concrete having a substitution rate of 70% as an example, and the analysis results are shown in FIGS. 7-10 and 11(a) to (d).
Referring to fig. 7, the present invention uses fig. 7 to verify the feasibility and efficiency of the algorithm of the present invention. Figure 7 shows the model calculated analysis result total stress distribution at an ellipsoid recycled aggregate content of 70%, where avg 75% is the default probability that the Abaqus software default average threshold is used to perform the averaging variables. The simulation test is fast and efficient in calculation and analysis, and the calculation result is correct, so that the algorithm is true and accurate;
referring to fig. 8, the invention adopts fig. 8 to explain that after the simulation test calculation is completed on the ellipsoid recycled aggregate model with the recycled aggregate content of 70%, an obtained S11 stress cloud chart, namely a stress distribution chart obtained in the X-axis direction, is obtained, and S11 represents the stress in the shell unit surface, so that the accuracy of the algorithm is verified;
referring to fig. 9, the invention adopts fig. 9 to explain that after the simulation test calculation is completed on the ellipsoid recycled aggregate model with the recycled aggregate content of 70%, an obtained S22 stress cloud graph, namely a stress distribution graph obtained in the Y-axis direction is obtained, and S11 represents the stress in the shell unit surface, so that the accuracy of the algorithm is verified;
referring to fig. 10, the invention adopts fig. 10 to explain that after the simulation experiment calculation of the ellipsoid recycled aggregate model with the recycled aggregate content of 70% is completed, an obtained S33 stress cloud chart, namely a stress distribution chart obtained in the Z-axis direction, is obtained, and S33 represents the stress in the normal direction of the shell unit, so as to verify the accuracy of the algorithm.
After a uniaxial compressive displacement load of-0.5 mm is applied to an ellipsoid recycled aggregate concrete calculation model with a substitution rate of 70%, the model forms a test piece model overall damage development process shown in figures 11(a) - (b), the damage graph shows that the model damage is mainly distributed in the middle and the bottom of the test piece, the upper part of the test piece is hardly influenced by the uniaxial load, and the single compressive load applied to the upper part is transmitted to the middle and the bottom of the test piece to generate disturbance on the model test piece, so that the test piece damage is caused. The method completely accords with the damage development process caused by the actual recycled concrete single-pressure experiment, and the correctness and the rationality of the algorithm are proved.
The above calculation proves the feasibility and the practical high efficiency of the algorithm. The invention creates an automatic generation method of a composite material simulation test full process based on an ellipsoid recycled aggregate, and combines a P ython programming modeling algorithm and Abaqus post-processing analysis to perform microscopical structure simulation test analysis and calculation on the ellipsoid composite aggregate. The method can be applied to the field of the research on the mesoscopic aggregate structure of any composite material, and provides a quick and efficient research method for the further detailed development of the mesoscopic structure research of the composite material.
Claims (6)
1. A construction method of a recycled concrete three-dimensional model based on ellipsoid random aggregate is characterized by comprising the following steps:
step one, compiling a script algorithm for establishing an ellipsoid aggregate model by utilizing Python programming;
introducing a random aggregate particle size function into a Python script algorithm according to a statistical simulation theory, and randomly generating a random aggregate particle size of 5-25 mm;
thirdly, a statistical test method is utilized to program the aggregate random putting function theory into a Python script algorithm, so that the random putting of the ellipsoid aggregate in the model space is realized;
establishing an ellipsoid recycled aggregate and ellipsoid natural aggregate mesoscopic structure model by utilizing a Python language, establishing a three-dimensional composite material frame model and defining the attributes of the model composite material;
and fifthly, writing an Abaqus post-processing analysis programming algorithm, and combining the whole modeling and simulation analysis process to realize the automatic generation of the whole simulation test process.
2. The method of claim 1, wherein the first step is performed by:
1) defining the probability of the particle size distribution of the aggregate,
2) defining an aggregate type and an aggregate parameter type, firstly defining an aggregate type,
3) inputting basic information of aggregate parameters of the ellipsoid, including the aggregate percentage of the outermost ring, the replacement rate of recycled aggregate, the size of a model, the particle size range, the interface thickness of aggregate and old mortar, the thickness of old mortar, the interface thickness of old mortar and new mortar, the interface thickness of natural aggregate and new mortar, the ratio of the short axis to the long axis of the ellipsoid, the ratio of the middle axis to the short axis, the size of a grid and an applied load value.
3. The method according to claim 1, wherein the second step is specifically performed by:
1) a random particle size function is built in, and when the sum of the accumulated sum and the maximum aggregate volume of the grade is less than the aggregate volume sum of the grade, aggregate is continuously added;
2) when the aggregate particle size is generated, prompting to output the generated aggregate particle size;
3) the proportion of the aggregates containing the interface layer is output, and when the proportion of the aggregates containing the external interface layer is 50%, the total proportion of the aggregates can reach 85% at most, so that the aggregates cannot be completely embedded.
4. The method according to claim 1, wherein the third step is specifically:
1) storing the particle size location;
2) appointing a limited putting position, reducing the calculated amount, dividing the space into uniform small lattices, and only putting the aggregate on the lattices, so that the delivery rate can be greatly improved, and the central position of the lattices can be obtained in a circulating manner;
3) recording the number of aggregates which fail to be put and the particle size of the aggregates;
4) feeding aggregate once from large to small, recording the number of times of trying to feed, copying the position of trying to feed, and recording success or failure of feeding, wherein if the feeding fails, the default is that the feeding failure numerical value is 0;
5) in order to avoid that the two aggregates are too close, setting an aggregate gap of 0.5mm, and when the random throwing times are less than 1000 times, positions which can be thrown in the tryPosition exist, and the throwing fails, continuing to throw; obtaining the selected position from the tryPosition, subtracting the selected point from the tryPosition value, avoiding repeated selection, and accumulating the number of attempts by 1;
6) when the distance boundary of the throwing position is smaller than rd, wherein rd represents the minimum tolerance between the two aggregates, the throwing directly fails, the next round of circulation is started if the throwing fails, and otherwise, the throwing is assumed to be successful;
7) calculating the distance between two successfully thrown aggregates, if the distance between two ellipsoidal aggregates is smaller than the sum of the distance between the spherical centers of the two aggregates and the minimum tolerance, the throwing fails, the cycle of distance judgment is exited, and the attempt of entering the position of the next aggregate is carried out; when the placement is successful, the location of the placement is recorded.
5. The method according to claim 1, wherein the fourth step is specifically performed by:
1) circularly generating ellipsoid components, planing the ellipsoid components, defining material properties, and assembling the planed ellipsoid components to an assembly body;
2) randomly rotating the aggregate angle and translating to a specified position;
3) creating a square outer frame for containing aggregate and giving material properties;
4) all the ellipsoidal aggregates are combined, hundreds of ellipsoidal aggregates are combined at the same time, the program running speed is slow, so that the ellipsoidal aggregates can be combined in batches, at most 50 aggregates are combined in each batch, and finally, the ellipsoidal aggregates are combined for the second time, so that the combining speed can be improved;
5) and combining all parts of the ellipsoidal aggregate, defining material properties of the parts, defining materials at all positions of the small ellipsoidal parts before combination, and continuously retaining the defined aggregate part materials after combination, so that only new slurry materials need to be defined.
6. The method according to claim 1, wherein the fifth step is specifically performed by:
1) grid division, wherein the same part is divided by adopting the two units, the connected interfaces are not matched, a program can be automatically replaced by tie, the definition is more accurate than manual definition, the new slurry part is subjected to tetrahedral grid division, and the type of grid unit and the size of a global grid are defined;
2) searching all outer boundaries, setting the size of the grids on the outer boundaries to be twice of that of the inner boundaries, then carrying out grid division and deleting redundant components;
3) establishing an analysis step and modifying output;
4) creating a reference point, useful for coupling the constraint and the output; outputting a reference point process; modifying the control parameters; a reference point coupling constraint;
5) fixing and constraining; loading constraints; creating a task; and saving the model.
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