CN110457865B - Discrete element image modeling method based on digital speckle method - Google Patents
Discrete element image modeling method based on digital speckle method Download PDFInfo
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Abstract
The invention discloses a discrete element image modeling method based on a digital speckle method, belongs to the technical field of asphalt mixture digital design, and aims to solve the problem that an existing discrete element model based on an image is lack of local stress condition verification. The modeling method comprises the following steps: firstly, collecting a section image of the asphalt mixture; secondly, importing the section image into digital speckle software to obtain strain field distribution information in the horizontal direction; thirdly, extracting a mortar position strain field; fourthly, establishing a discrete element model of the asphalt mixture indirect tensile test; fifthly, applying the same load parameters as those in the process of the indirect tensile test in the step one to obtain the distribution of the mortar strain field based on discrete element calculation; sixthly, quantifying the difference of the strain field mean values of the discrete element method and the digital speckle real measurement method; and seventhly, taking the strain field calculation error percentage P as an index for evaluating the modeling accuracy of the discrete elements. The invention evaluates the calculation accuracy of the discrete element model based on the image on the microscopic scale, thereby optimizing the modeling of the discrete element image.
Description
Technical Field
The invention belongs to the technical field of asphalt mixture digital design, and particularly relates to a discrete element image modeling method based on a digital speckle method.
Background
The design and optimization of the asphalt mixture material are the key for improving the service performance of the pavement, and are also important ways for prolonging the service life of the pavement and reducing the maintenance cost. At present, the traditional asphalt mixture design method is mainly based on indoor tests, including a Marshall design method and a Superpave design method, and the test-based design method is simple to operate, but the tests consume more manpower and materials, and the data discreteness is large.
In view of the problems of the conventional asphalt mixture design method, the digital design method based on the numerical simulation technology has started to be applied in the field of road engineering, wherein the discrete unit method is widely applied to asphalt mixture mechanical property prediction due to the advantages of the discrete unit method in the bulk material simulation. Although the discrete element method has certain advantages in simulating the mechanical property of the asphalt mixture, the acquisition of numerical simulation parameters and the verification of accuracy are the keys for restricting the popularization and the application of digital design. The traditional accuracy verification method can only verify the consistency of the stress and the real stress-strain curve of the digital test piece macroscopically, but for the current asphalt mixture heterogeneous model, particularly the model established based on the image, the macroscopic test method cannot realize the accuracy verification of the stress condition of the microscopic part. Aiming at the condition that the existing discrete element model based on the image lacks a local stress condition verification method, the invention provides a discrete element image modeling accuracy verification method based on a digital speckle method.
Disclosure of Invention
The invention provides a discrete element image modeling accuracy verification method based on a digital speckle method, aiming at solving the problem that the existing discrete element model based on an image lacks local stress condition verification.
The discrete element image modeling method based on the digital speckle method is realized according to the following steps:
acquiring a section image of the asphalt mixture in an indirect tensile test process through an industrial camera to obtain a section image of the asphalt mixture;
secondly, importing the section image of the asphalt mixture obtained in the first step into digital speckle software, and processing the section image to obtain strain field distribution information in the horizontal direction;
thirdly, extracting a mortar position matrix of the section image of the asphalt mixture in the first step through a digital image processing technology, and extracting a mortar position strain field from the asphalt mixture strain field in the second step according to the mortar position matrix to obtain digital speckle actual measurement mortar position strain field distribution;
fourthly, according to an aggregate position matrix and a mortar position matrix in a section image of the asphalt mixture, constructing mortar particles at positions of mortar pixel points (in a discrete element modeling process), constructing aggregate particles at the positions of the aggregate pixel points, and setting discrete element model parameters, so as to establish a discrete element model of the asphalt mixture indirect tensile test;
applying load parameters which are the same as those in the indirect tensile test process in the step one in the discrete element simulation process, obtaining the position change of mortar particles, and further obtaining the mortar strain field distribution based on discrete element calculation;
sixthly, comparing the mortar strain field mean value calculated based on the discrete elements with the mortar position strain field mean value actually measured by the digital speckles, further quantifying the difference of the two strain field mean values, and calculating the strain field calculation error through a formula (1);
in the formula: p-percentage error of strain field calculation;
εDEM-calculating the strain field mean value for a discrete element at a certain loading moment;
εDIC-digital speckle actual measurement strain field mean value at a certain loading moment;
and seventhly, taking the strain field calculation error percentage P as a mesoscopic evaluation index for evaluating the modeling accuracy of the discrete elements, if the value of P exceeds the modeling error requirement, adjusting the discrete element model parameters in the fourth step until the error percentage P is less than the error requirement, and thus completing the modeling of the discrete element image based on the digital speckle method.
The invention discloses a discrete element image modeling accuracy verification method based on a digital speckle method, which comprises the steps of firstly collecting an asphalt mixture section image in an indirect tensile test process through an industrial camera, further obtaining a position matrix of asphalt mortar and aggregate through a digital image processing technology, respectively obtaining mortar position strain field distribution by adopting the digital speckle method and the discrete element method, and providing a discrete element calculation strain field and a digital speckle actual measurement strain field error percentage, so that the calculation accuracy of a discrete element model based on an image is evaluated on a microscopic scale, and the establishment of the discrete element model is optimized.
Drawings
FIG. 1 is a cross-sectional image of the asphalt mixture obtained in the first step of the example;
FIG. 2 is a horizontal strain field distribution diagram obtained in step two of the example;
FIG. 3 is a digital speckle measured mortar position strain field distribution diagram obtained in step three of the embodiment;
FIG. 4 is a diagram of a discrete meta-model obtained in step four of the embodiment;
FIG. 5 is a mortar strain field profile based on discrete element calculations in step six of the example;
FIG. 6 is a strain field distribution diagram of the digital speckle measured mortar position corresponding to the discrete element model particle position in step six of the embodiment;
FIG. 7 is a comparison graph of the mortar strain field mean value calculated based on discrete elements and the digital speckle measured mortar position strain field mean value in the sixth example, wherein a represents the mortar strain field mean value calculated based on discrete elements, and ■ represents the digital speckle measured mortar position strain field mean value.
Detailed Description
The first embodiment is as follows: the discrete element image modeling method based on the digital speckle method is implemented according to the following steps:
acquiring a section image of the asphalt mixture in an indirect tensile test process through an industrial camera to obtain a section image of the asphalt mixture;
secondly, importing the section image of the asphalt mixture obtained in the first step into digital speckle software, and processing the section image to obtain strain field distribution information in the horizontal direction;
thirdly, extracting a mortar position matrix of the section image of the asphalt mixture in the first step through a digital image processing technology, and extracting a mortar position strain field from the asphalt mixture strain field in the second step according to the mortar position matrix to obtain digital speckle actual measurement mortar position strain field distribution;
fourthly, according to an aggregate position matrix and a mortar position matrix in a section image of the asphalt mixture, constructing mortar particles at positions of mortar pixel points (in a discrete element modeling process), constructing aggregate particles at the positions of the aggregate pixel points, and setting discrete element model parameters, so as to establish a discrete element model of the asphalt mixture indirect tensile test;
applying load parameters which are the same as those in the indirect tensile test process in the step one in the discrete element simulation process, obtaining the position change of mortar particles, and further obtaining the mortar strain field distribution based on discrete element calculation;
sixthly, comparing the mortar strain field mean value calculated based on the discrete elements with the mortar position strain field mean value actually measured by the digital speckles, further quantifying the difference of the two strain field mean values, and calculating the strain field calculation error through a formula (1);
in the formula: p-percentage error of strain field calculation;
εDEM-calculating the strain field mean value for a discrete element at a certain loading moment;
εDIC-digital speckle actual measurement strain field mean value at a certain loading moment;
and seventhly, taking the strain field calculation error percentage P as a mesoscopic evaluation index for evaluating the modeling accuracy of the discrete elements, if the value of P exceeds the modeling error requirement, adjusting the discrete element model parameters in the fourth step until the error percentage P is less than the error requirement, and thus completing the modeling of the discrete element image based on the digital speckle method.
The second embodiment is as follows: the difference between the present embodiment and the first embodiment is that the sampling frequency of the industrial camera in the first step is 10 sheets/second.
The third concrete implementation mode: the difference between the present embodiment and the first or second embodiment is that the indirect tensile test in the first step is performed according to the test protocol for road engineering asphalt and asphalt mixture (JTG E20-2011).
The fourth concrete implementation mode: the difference between this embodiment and one of the first to third embodiments is that the digital speckle software in step two is VIC-2D.
The fifth concrete implementation mode: the difference between the first embodiment and the fourth embodiment is the discrete element model parameters of the contact rigidity and the viscosity parameters of the asphalt mortar particles in the fourth step.
The sixth specific implementation mode: the difference between this embodiment and one of the first to fifth embodiments is that the discrete element simulation process in the fifth step is simulated by using PFC-2D software.
The seventh embodiment: the difference between this embodiment and one of the first to sixth embodiments is that the modeling error requirement in step seven is lower than 5%, and then the discrete element image modeling is successful.
Example (b): the method for verifying the modeling accuracy of the discrete element image based on the digital speckle method is implemented according to the following steps:
acquiring a section image of the asphalt mixture in an indirect tensile test process by an industrial camera to obtain the section image of the asphalt mixture, wherein the section image is shown in figure 1;
secondly, introducing the section image of the asphalt mixture obtained in the first step into digital speckle software, and processing to obtain distribution information of a strain field in the horizontal direction, wherein the strain field in the horizontal direction is shown in figure 2;
thirdly, extracting a mortar position matrix of the section image of the asphalt mixture in the first step through a digital image processing technology, and extracting a mortar position strain field from the asphalt mixture strain field in the second step according to the mortar position matrix to obtain a mortar position strain field distribution diagram, wherein the mortar position strain field distribution is shown in fig. 3;
fourthly, according to the aggregate position matrix and the mortar position matrix in the cross-section image of the asphalt mixture, in the discrete element modeling process, mortar particles are constructed at the positions of mortar pixels, and aggregate particles are constructed at the positions of the aggregate pixels, so that a discrete element model of the asphalt mixture indirect tensile test is established, wherein the discrete element model is shown in FIG. 4;
fifthly, in the discrete element simulation process, applying the same load parameters as those in the indirect tensile test process in the first step, obtaining the position change of mortar particles, further obtaining mortar strain field distribution based on discrete element calculation, and extracting digital speckle actual measurement mortar position strain field distribution by combining the mortar positions in the discrete elements, as shown in fig. 5 and 6;
sixthly, comparing the average value of the mortar strain field (shown in figure 5) calculated based on the discrete elements with the average value of the mortar position strain field (shown in figure 6) actually measured by the digital speckles (at the same loading moment), further quantifying the difference of the average values of the two strain fields, and calculating the calculation error of the strain field by a formula (1);
in the formula: p-percentage error of strain field calculation;
εDEM-calculating the strain field mean value for a discrete element at a certain loading moment;
εDIC-digital speckle actual measurement strain field mean value at a certain loading moment;
and seventhly, taking the strain field calculation error percentage P as a mesoscopic evaluation index for evaluating the modeling accuracy of the discrete elements, if the value of P exceeds the modeling error requirement, adjusting the discrete element model parameters in the fourth step until the error percentage P is less than the error requirement, and thus completing the modeling of the discrete element image based on the digital speckle method.
In the fourth step of this example, model parameters of aggregate particle contact normal stiffness of 55.5Gpa · m, tangential stiffness of 22.2Gpa · m, mortar particle contact maxwell model normal stiffness of 508.5MPa · m, normal viscosity of 325.8MPa · m · s, tangential stiffness of 169.5MPa · m, tangential viscosity of 108.6MPa · m · s, aggregate particle-mortar particle contact maxwell model normal stiffness of 1007.8MPa · m, normal viscosity of 651.5MPa · m · s, tangential stiffness of 336.4MPa · m, tangential viscosity of 217.2MPa · m · s are set.
In step six of this example,. epsilonDEM＝0.00241、εDIC0.00252 and P4.4%, the modeling error is less than 5%, indicating successful modeling.
Claims (7)
1. A discrete element image modeling method based on a digital speckle method is characterized by comprising the following steps:
acquiring a section image of the asphalt mixture in an indirect tensile test process through an industrial camera to obtain a section image of the asphalt mixture;
secondly, importing the section image of the asphalt mixture obtained in the first step into digital speckle software, and processing the section image to obtain strain field distribution information in the horizontal direction;
thirdly, extracting a mortar position matrix of the section image of the asphalt mixture in the first step through a digital image processing technology, and extracting a mortar position strain field from the asphalt mixture strain field in the second step according to the mortar position matrix to obtain digital speckle actual measurement mortar position strain field distribution;
fourthly, according to the aggregate position matrix and the mortar position matrix in the cross-section image of the asphalt mixture, building mortar particles at the positions of mortar pixel points, building aggregate particles at the positions of the aggregate pixel points, and setting discrete element model parameters, so that a discrete element model of the asphalt mixture indirect tensile test is built;
applying load parameters which are the same as those in the indirect tensile test process in the step one in the discrete element simulation process, obtaining the position change of mortar particles, and further obtaining the mortar strain field distribution based on discrete element calculation;
sixthly, comparing the mortar strain field mean value calculated based on the discrete elements with the mortar position strain field mean value actually measured by the digital speckles, further quantifying the difference of the two strain field mean values, and calculating the strain field calculation error through a formula (1);
in the formula: p-percentage error of strain field calculation;
εDEM-calculating the strain field mean value for a discrete element at a certain loading moment;
εDIC-digital speckle actual measurement strain field mean value at a certain loading moment;
and seventhly, taking the strain field calculation error percentage P as a mesoscopic evaluation index for evaluating the modeling accuracy of the discrete elements, if the value of P exceeds the modeling error requirement, adjusting the discrete element model parameters in the fourth step until the error percentage P is less than the error requirement, and thus completing the modeling of the discrete element image based on the digital speckle method.
2. The method for modeling a discrete element image based on the digital speckle method as claimed in claim 1, wherein the sampling frequency of the industrial camera in the first step is 10 pieces/second.
3. The method for modeling the discrete element image based on the digital speckle method as claimed in claim 1, wherein the indirect tensile test in the first step is performed according to the test procedure for asphalt and asphalt mixtures for road engineering.
4. The method according to claim 1, wherein the digital speckle software in step two is VIC-2D.
5. The method for modeling the discrete element image based on the digital speckle method according to claim 1, wherein the discrete element model parameters in the fourth step are contact stiffness and viscosity parameters of asphalt mortar particles.
6. The method for modeling the discrete element image based on the digital speckle method as claimed in claim 1, wherein the discrete element simulation process in the fifth step is simulated by using PFC-2D software.
7. The method according to claim 1, wherein the modeling error in step seven is required to be lower than 5%, and the discrete element image modeling is successful.
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