阅读说明：本技术 一种颗粒流微观力学参数反演方法 (Particle flow micromechanics parameter inversion method ) 是由 王敏 万文 赵延林 于 2021-06-29 设计创作，主要内容包括：本发明公开了一种颗粒流微观力学参数反演方法,首先通过物理实验获取岩石宏观力学参数,然后基于物理宏观力学参数反演出颗粒流微观力学参数,接着通过数值模拟计算获取数值模拟宏观力学参数,最后采用模拟退火算法调整颗粒流微观力学参数,使得数值模拟宏观力学参数与物理实验宏观力学参数不断接近,当数值模拟宏观力学参数与物理实验宏观力学参数之间的误差小于10%时,则此时对应的颗粒流微观力学参数则是所需要确定的微观力学参数。本发明操作简便,微观力学参数调整过程中无主观性,收敛速度较好,适用于颗粒流数值模拟计算领域。(The invention discloses a particle flow micromechanics parameter inversion method, which comprises the steps of firstly obtaining rock micromechanics parameters through a physical experiment, then performing particle flow micromechanics parameters based on the physical micromechanics parameters, then obtaining numerical simulation macroscopic mechanics parameters through numerical simulation calculation, and finally adjusting the particle flow micromechanics parameters by adopting a simulated annealing algorithm, so that the numerical simulation macroscopic mechanics parameters are continuously close to the physical experiment macroscopic mechanics parameters, and when the error between the numerical simulation macroscopic mechanics parameters and the physical experiment macroscopic mechanics parameters is less than 10%, the corresponding particle flow micromechanics parameters are the micromechanics parameters to be determined. The method is simple and convenient to operate, has no subjective property in the adjustment process of the micromechanics parameters, has good convergence rate, and is suitable for the field of particle flow numerical simulation calculation.)

1. A particle flow micromechanics parameter inversion method is characterized by comprising the following steps:

the method comprises the following steps: obtaining macroscopic mechanical parameters of a rock physical experiment through a physical experiment;

step two: inverting the particle flow micro-mechanical parameters based on the physical macro-mechanical parameters;

step three: obtaining numerical simulation macroscopic mechanical parameters through numerical simulation calculation;

step four: and adjusting the particle flow micromechanics parameters by adopting a simulated annealing algorithm, so that the numerical simulation calculation macroscopic mechanical parameters are continuously close to the physical experiment macroscopic mechanical parameters, and when the error between the numerical simulation macroscopic mechanical parameters and the physical experiment macroscopic mechanical parameters is less than 10%, the corresponding particle flow micromechanics parameters are the micromechanics parameters to be determined.

2. The particle flow micromechanics parameter inversion method of claim 1, wherein in the first step, the physical experiment micromechanics parameters comprise uniaxial compressive strength UCS_experimentalElastic modulus E_experimentalPoisson's ratio v_experimental。

3. The particle flow micromechanics parameter inversion method of claim 2, wherein in the second step, the particle flow micromechanics parameters comprise particle density p, and a ratio R of a maximum radius to a minimum radius of the particles_max/R_minMinimum radius of particle R_minParticle contact stiffness E_cRatio k of normal stiffness to tangential stiffness of the particles_n/k_sParallel connection stiffnessParallel connection normal stiffness to tangential stiffness ratioAverage value sigma of particle friction coefficient mu and parallel connection normal stiffness_c-meanParallel connected normal stiffness variance σ_c-stdParallel connected shear stiffness mean τ_c-meanParallel connection shear stiffness variance τ_c-std。

4. Root of herbaceous plantThe inversion method of particle flow micromechanics parameters of claim 3, characterized in that in the third step, the numerical simulation calculation is carried out on the flow micromechanics parameters obtained in the second step, and numerical simulation macro-mechanics parameters corresponding to the group of micromechanics parameters, namely the uniaxial compressive strength UCS, are obtained_numericalElastic modulus E_numericalPoisson's ratio v_numerical。

5. The particle flow micromechanics parameter inversion method of claim 4, wherein in the fourth step, the specific steps of adjusting the particle flow micromechanics parameters by adopting the simulated annealing algorithm are as follows:

step 1: initializing a simulated annealing algorithm hyper-parameter: temperature, cooling coefficient Decay, Markov chain length Markov and data jitter factor Stepfactor, wherein Markov chain length Markov represents the cycle times under each Temperature condition, and the Iteration time Iteration is 0;

during initial numerical simulation calculation, the micromechanics parameters are as follows: the particle density, the ratio of the maximum radius to the minimum radius of the particles, the particle contact stiffness, the ratio of the normal stiffness to the tangential stiffness of the particles, the parallel connection stiffness, the ratio of the normal stiffness to the tangential stiffness of the parallel connection, the particle friction coefficient, the average value of the normal stiffness of the parallel connection, the variance of the normal stiffness of the parallel connection, the average value of the shear stiffness of the parallel connection, and the maximum value and the minimum value of the variance of the shear stiffness of the parallel connection are respectively rho_max、R_max-max/R_min-max、R_min-max、E_c-max、k_n-max/k_s-max、μ_max、σ_c-mean-max、σ_c-std-max、τ_c-mean-max、τ_c-std-maxAnd ρ_min、R_max-min/R_min-min、R_min-min、E_c-min、k_n-min/k_s-min、μ_min、σ_c-mean-min、σ_c-std-min、τ_c-mean-min、τ_c-std-minThen selecting a random value rho between the maximum value and the minimum value of the micromechanics parameter_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-preAs an initial value of the micromechanics parameter, the group of micromechanics parameters is adopted to develop a numerical simulation uniaxial compression experiment, and numerical simulation macroscopic mechanics parameters with the uniaxial compression strength, the elastic modulus and the Poisson ratio of UCS as UCS are obtained_{numerical-pre}、E_{numerical-pre}、v_{numerical-pre}；

Calculating the maximum value Judge of the relative error between the numerical simulation macroscopic mechanical parameters and the macroscopic mechanical parameters obtained by physical tests_pre，Judge_preNamely, as a discrimination value, expressed as:

simultaneously, the initial micromechanics parameters are regarded as the optimal micromechanics parameters:

the corresponding discrimination value is also the current optimal relative error value:

Judge_best＝Judge_pre (3)

step 2: based on previous sets of micro-mechanical parameters Determining the values of the next set of micromechanics parameters, which are calculated according to the following formula:

in the above formula, rand is a random number between-1 and 1, based on the previous micromechanical parameter set by formula (4) Generating a current set of micromechanical parameters Adopting the current micromechanics parameter set to develop a uniaxial compression numerical simulation experiment to obtain corresponding numerical simulation model macroscopic mechanical parameters: compressive strength of single axis UCS_{numerical-next}Elastic modulus E_{numerical-next}And poisson's ratio v_{numerical-next}And calculating the value Judge of the discriminant function_next：

And step 3: if calculated Judge_next<Judge_bestThen, updating the optimal micromechanics parameter set and the optimal target value:

Judge_best＝Judge_next (7)

if Judge_next>Judge_bestThen the optimal micromechanics parameter set does not need to be updated;

and 4, step 4: if calculated Judge_next<Judge_preUpdating the previous particle flow micromechanics parameters and the previous judgment function values:

Judge_pre＝Judge_next (9)

when Judge_next≥Judge_preFirst, a probability value p is calculated₁，p₁Expressed as:

then a random number p of 0 to 1 is generated₂If p is₁>p₂Updating the previous particle flow micromechanics parameters and the previous judgment function values, namely executing the formula (8) and the formula (9), and otherwise, not executing any operation;

and 5: updating Iteration times, wherein operation is operation +1, and when the operation > Markov: the operation is 0, and the current Temperature drop is Temperature × Decay; after step 5 is executed, step 2 is executed again, and the process is circulated.

6. The particle flow micro-mechanical parameter inversion method of claim 5, wherein in the fourth step, the termination condition of the numerical simulation calculation is a macro-mechanical parameter obtained by the numerical simulation calculation and a macro obtained by a physical experimentThe difference value relative error of the mechanical parameters is less than 10 percent; in the numerical simulation calculation process, once iteration is performed on Judge_bestMaking a judgment when Judge_bestStopping calculation when the concentration is less than or equal to 10 percent, wherein Judge_bestExpressed as:

in the formula, UCS_{numerical-best}，E_{numerical-best}，v_{numerical-best}Represents the optimal combination of uniaxial compressive strength, elastic modulus and poisson's ratio in the calculation process.

Technical Field

The invention relates to a particle flow micromechanics parameter inversion method.

Background

Before carrying out the particle flow numerical simulation calculation, determining the corresponding micromechanics parameters of the particle flow numerical simulation model, wherein the micromechanics parameters cannot be selected at will, and in order to make the mathematical model corresponding to the selected micromechanics parameters more appropriate to engineering practice, the related mechanical parameters of the rock are generally obtained through a physical test: uniaxial compressive strength, elastic modulus and Poisson's ratio, and then adjusting the particle flow micromechanics parameters to enable the particle flow micromechanics parameters to numerically simulate the corresponding macroscopic mechanics parameters of the model: the uniaxial compressive strength, the elastic model and the Poisson ratio are close to the macroscopic mechanical parameters of the physical test, and when the error between the macroscopic mechanical parameters obtained by the numerical simulation experiment and the macroscopic mechanical parameters of the physical test is small, the corresponding microscopic mechanical parameters are the required microscopic mechanical parameters. At present, a trial and error method is generally adopted to continuously adjust the micromechanics parameters of the particle flow so that the micromechanics parameters of the numerical simulation calculation are continuously close to the micromechanics parameters of the physical experiment, and when the error amount is less than a certain value, the debugging is stopped. The adoption of the trial-and-error method mainly depends on the mastery level of a tester on the particle flow software, certain blindness exists in the debugging process, and the obtained micro-mechanical parameters have great contingency.

Disclosure of Invention

In order to solve the technical problems, the invention provides a particle flow micromechanics parameter inversion method with simple algorithm and good convergence rate.

The technical scheme for solving the problems is as follows: a particle flow micromechanics parameter inversion method comprises the following steps:

the method comprises the following steps: obtaining macroscopic mechanical parameters of a rock physical experiment through a physical experiment;

step two: inverting the particle flow micro-mechanical parameters based on the physical macro-mechanical parameters;

step three: obtaining numerical simulation macroscopic mechanical parameters through numerical simulation calculation;

step four: and adjusting the particle flow micromechanics parameters by adopting a simulated annealing algorithm, so that the numerical simulation calculation macroscopic mechanical parameters are continuously close to the physical experiment macroscopic mechanical parameters, and when the error between the numerical simulation macroscopic mechanical parameters and the physical experiment macroscopic mechanical parameters is less than 10%, the corresponding particle flow micromechanics parameters are the micromechanics parameters to be determined.

In the particle flow micromechanics parameter inversion method, in the first step, the physical experiment macroscopic mechanics parameters comprise uniaxial compressive strength UCS_experimentalElastic modulus E_experimentalPoisson's ratio v_experimental。

In the second step, the particle flow micromechanics parameters include particle density rho and the ratio R of the maximum radius to the minimum radius of the particles_max/R_minMinimum radius of particle R_minParticle contact stiffness E_cRatio k of normal stiffness to tangential stiffness of the particles_n/k_sParallel connection stiffnessParallel connection normal stiffness to tangential stiffness ratioAverage value sigma of particle friction coefficient mu and parallel connection normal stiffness_c-meanParallel connected normal stiffness variance σ_c-stdParallel connected shear stiffness mean τ_c-meanParallel connection shear stiffness variance τ_c-std。

In the third step, the flow micromechanics parameters obtained in the second step are subjected to numerical simulation calculation, and numerical simulation macro mechanics parameters corresponding to the group of micromechanics parameters, namely the uniaxial compressive strength UCS, are obtained_numericalElastic modulus E_numericalPoisson's ratio v_numerical。

In the fourth step, the specific step of adjusting the particle flow micromechanics parameters by using the simulated annealing algorithm is as follows:

step 1: initializing a simulated annealing algorithm hyper-parameter: temperature, cooling coefficient Decay, Markov chain length Markov and data jitter factor Stepfactor, wherein Markov chain length Markov represents the cycle times under each Temperature condition, and the Iteration time Iteration is 0;

during initial numerical simulation calculation, the micromechanics parameters are as follows: the particle density, the ratio of the maximum radius to the minimum radius of the particles, the particle contact stiffness, the ratio of the normal stiffness to the tangential stiffness of the particles, the parallel connection stiffness, the ratio of the normal stiffness to the tangential stiffness of the parallel connection, the particle friction coefficient, the average value of the normal stiffness of the parallel connection, the variance of the normal stiffness of the parallel connection, the average value of the shear stiffness of the parallel connection, and the maximum value and the minimum value of the variance of the shear stiffness of the parallel connection are respectively rho_max、R_max-max/R_min-max、R_min-max、E_c-max、k_n-max/k_s-max、μ_max、σ_c-mean-max、σ_c-std-max、τ_c-mean-max、τ_c-std-maxAnd ρ_min、R_max-min/R_min-min、R_min-min、E_c-min、k_n-min/k_s-min、μ_min、σ_c-mean-min、σ_c-std-min、τ_c-mean-min、τ_c-std-minThen selecting a random value rho between the maximum value and the minimum value of the micromechanics parameter_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-preAs an initial value of the micromechanics parameter, the group of micromechanics parameters is adopted to develop a numerical simulation uniaxial compression experiment, and numerical simulation macroscopic mechanics parameters with the uniaxial compression strength, the elastic modulus and the Poisson ratio of UCS as UCS are obtained_{numerical-pre}、E_{numerical-pre}、v_{numerical-pre}；

Calculating the maximum value Judge of the relative error between the numerical simulation macroscopic mechanical parameters and the macroscopic mechanical parameters obtained by physical tests_pre，Judge_preNamely, as a discrimination value, expressed as:

simultaneously, the initial micromechanics parameters are regarded as the optimal micromechanics parameters:

the corresponding discrimination value is also the current optimal relative error value:

Judge_best＝Judge_pre (3)

step 2: based on a previous set of micromechanical parameters (p)_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-pre) Determining the values of the next set of micromechanics parameters, which are calculated according to the following formula:

in the above formula, rand is a random number between-1 and 1, based on the previous micromechanical parameter set (ρ) by formula (4)_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-pre) Generating a current set of micromechanical parameters (p)_next、R_max-next/R_min-next、R_min-next、E_c-next、k_n-next/k_s-next、μ_next、σ_c-mean-next、σ_c-std-next、τ_c-mean-next、τ_c-std-next) Adopting the current micromechanics parameter set to develop a uniaxial compression numerical simulation experiment to obtain corresponding numerical simulation model macroscopic mechanics parameters: compressive strength of single axis UCS_{numerical-next}Elastic modulus E_{numerical-next}And poisson's ratio v_{numerical-next}And calculating the value Judge of the discriminant function_next：

And step 3: if calculated Judge_next<Judge_bestThen, updating the optimal micromechanics parameter set and the optimal target value:

Judge_best＝Judge_next (7)

if Judge_next>Judge_bestThen the optimal micromechanics parameter set does not need to be updated;

and 4, step 4: if calculated Judge_next<Judge_preUpdating the previous particle flow micromechanics parameters and the previous judgment function values:

Judge_pre＝Judge_next (9)

when Judge_next≥Judge_preFirst, a probability value p is calculated₁，p₁Expressed as:

then a random number p of 0 to 1 is generated₂If p is₁>p₂Updating the previous particle flow micromechanics parameters and the previous judgment function values, namely executing the formula (8) and the formula (9), and otherwise, not executing any operation;

and 5: updating Iteration times, wherein operation is operation +1, and when the operation > Markov: the operation is 0, and the current Temperature drop is Temperature × Decay; after step 5 is executed, step 2 is executed again, and the process is circulated.

In the fourth step, the termination condition of the numerical simulation calculation is that the relative difference error between the macroscopic mechanical parameters obtained by the numerical simulation calculation and the macroscopic mechanical parameters obtained by the physical experiment is less than 10%; in the numerical simulation calculation process, once iteration is performed on Judge_bestMaking a judgment when Judge_bestStopping calculation when the concentration is less than or equal to 10 percent, wherein Judge_bestExpressed as:

in the formula, UCS_{numerical-best}，E_{numerical-best}，v_{numerical-best}Represents the optimal combination of uniaxial compressive strength, elastic modulus and poisson's ratio in the calculation process.

The invention has the beneficial effects that: the method comprises the steps of firstly obtaining rock macroscopic mechanical parameters through a physical experiment, then inversing particle flow microscopic mechanical parameters based on the physical macroscopic mechanical parameters, then obtaining numerical simulation macroscopic mechanical parameters through numerical simulation calculation, and finally adjusting the particle flow microscopic mechanical parameters by adopting a simulated annealing algorithm, so that the numerical simulation macroscopic mechanical parameters are continuously close to the physical experiment macroscopic mechanical parameters, and when the error between the numerical simulation macroscopic mechanical parameters and the physical experiment macroscopic mechanical parameters is less than 10%, the corresponding particle flow microscopic mechanical parameters are the microscopic mechanical parameters to be determined. The method is simple and convenient to operate, has no subjective property in the adjustment process of the micromechanics parameters, has good convergence rate, and is suitable for the field of particle flow numerical simulation calculation.

Drawings

FIG. 1 is a flow chart of the present invention.

FIG. 2 is a flow chart of the present invention for adjusting the micromechanics parameters of a particle stream using a simulated annealing algorithm.

Detailed Description

The invention is further described below with reference to the accompanying drawings and examples.

As shown in fig. 1, a method for inverting micromechanics parameters of a particle flow includes the following steps:

the method comprises the following steps: obtaining macroscopic mechanical parameters of petrophysical experiment through physical experiment, wherein the macroscopic mechanical parameters of physical experiment comprise uniaxial compressive strength UCS_experimentalElastic modulus E_experimentalPoisson's ratio v_experimental。

Step two: inverting the particle flow micromechanics parameters based on the physical and macroscopic mechanics parameters, wherein the particle flow micromechanics parameters comprise particle density rho and the ratio R of the maximum radius to the minimum radius of the particles_max/R_minMinimum radius of particle R_minParticle contact stiffness E_cRatio k of normal stiffness to tangential stiffness of the particles_n/k_sParallel connection stiffnessParallel connection normal stiffness to tangential stiffness ratioAverage value sigma of particle friction coefficient mu and parallel connection normal stiffness_c-meanParallel connection methodVariance σ of directional stiffness_c-stdParallel connected shear stiffness mean τ_c-meanParallel connection shear stiffness variance τ_c-std。

Step three: carrying out numerical simulation calculation on the flow micromechanics parameters obtained in the step two, and obtaining numerical simulation macro mechanics parameters corresponding to the group of micromechanics parameters, namely the uniaxial compressive strength UCS_numericalElastic modulus E_numericalPoisson's ratio v_numerical。

Step four: and adjusting the particle flow micromechanics parameters by adopting a simulated annealing algorithm, so that the numerical simulation calculation macroscopic mechanical parameters are continuously close to the physical experiment macroscopic mechanical parameters, and when the error between the numerical simulation macroscopic mechanical parameters and the physical experiment macroscopic mechanical parameters is less than 10%, the corresponding particle flow micromechanics parameters are the micromechanics parameters to be determined.

The specific steps of adjusting the micromechanics parameters of the particle flow by adopting a simulated annealing algorithm are as follows:

step 1: initializing a simulated annealing algorithm hyper-parameter: temperature, cooling coefficient Decay, Markov chain length Markov and data jitter factor Stepfactor, wherein Markov chain length Markov represents the cycle times under each Temperature condition, and the Iteration time Iteration is 0;

during initial numerical simulation calculation, the micromechanics parameters are as follows: the particle density, the ratio of the maximum radius to the minimum radius of the particles, the particle contact stiffness, the ratio of the normal stiffness to the tangential stiffness of the particles, the parallel connection stiffness, the ratio of the normal stiffness to the tangential stiffness of the parallel connection, the particle friction coefficient, the average value of the normal stiffness of the parallel connection, the variance of the normal stiffness of the parallel connection, the average value of the shear stiffness of the parallel connection, and the maximum value and the minimum value of the variance of the shear stiffness of the parallel connection are respectively rho_max、R_max-max/R_min-max、R_min-max、E_c-max、k_n-max/k_s-max、μ_max、σ_c-mean-max、σ_c-std-max、τ_c-mean-max、τ_c-std-maxAnd ρ_min、R_max-min/R_min-min、R_min-min、E_c-min、k_n-min/k_s-min、μ_min、σ_c-mean-min、σ_c-std-min、τ_c-mean-min、τ_c-std-minThen selecting a random value rho between the maximum value and the minimum value of the micromechanics parameter_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-preAs an initial value of the micromechanics parameter, the group of micromechanics parameters is adopted to develop a numerical simulation uniaxial compression experiment, and numerical simulation macroscopic mechanics parameters with the uniaxial compression strength, the elastic modulus and the Poisson ratio of UCS as UCS are obtained_{numerical-pre}、E_{numerical-pre}、v_{numerical-pre}；

Calculating the maximum value Judge of the relative error between the numerical simulation macroscopic mechanical parameters and the macroscopic mechanical parameters obtained by physical tests_pre，Judge_preNamely, as a discrimination value, expressed as:

simultaneously, the initial micromechanics parameters are regarded as the optimal micromechanics parameters:

the corresponding discrimination value is also the current optimal relative error value:

Judge_best＝Judge_pre (3)

step 2: based on a previous set of micromechanical parameters (p)_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-pre) Determining the values of the next set of micromechanics parameters, which are calculated according to the following formula:

in the above formula, rand is a random number between-1 and 1, based on the previous micromechanical parameter set (ρ) by formula (4)_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-pre) Generating a current set of micromechanical parameters (p)_next、R_max-next/R_min-next、R_min-next、E_c-next、k_n-next/k_s-next、μ_next、σ_c-mean-next、σ_c-std-next、τ_c-mean-next、τ_c-std-next) Adopting the current micromechanics parameter set to develop a uniaxial compression numerical simulation experiment to obtain corresponding numerical simulation model macroscopic mechanics parameters: compressive strength of single axis UCS_{numerical-next}Elastic modulus E_{numerical-next}And poisson's ratio v_{numerical-next}And calculating the value Judge of the discriminant function_next：

And step 3: if calculated Judge_next<Judge_bestThen, updating the optimal micromechanics parameter set and the optimal target value:

Judge_best＝Judge_next (7)

if Judge_next>Judge_bestThen the optimal micromechanics parameter set does not need to be updated;

and 4, step 4: if calculated Judge_next<Judge_preUpdating the previous particle flow micromechanics parameters and the previous judgment function values:

Judge_pre＝Judge_next (9)

when Judge_next≥Judge_preFirst, a probability value p is calculated₁，p₁Expressed as:

then a random number p of 0 to 1 is generated₂If p is₁>p₂Updating the previous particle flow micromechanics parameters and the previous judgment function values, namely executing the formula (8) and the formula (9), and otherwise, not executing any operation;

and 5: updating Iteration times, wherein operation is operation +1, and when the operation > Markov: the operation is 0, and the current Temperature drop is Temperature × Decay; after step 5 is executed, step 2 is executed again, and the process is circulated.

The termination condition of numerical simulation calculation is that the relative error of the difference between the macroscopic mechanical parameters obtained by numerical simulation calculation and the macroscopic mechanical parameters obtained by physical experiments is less than 10 percent; in the numerical simulation calculation process, once iteration is performed on Judge_bestMaking a judgment when Judge_bestStopping calculation when the concentration is less than or equal to 5%, wherein Judge_bestExpressed as:

in the formula, UCS_{numerical-best}，E_{numerical-best}，v_{numerical-best}Represents the optimal combination of uniaxial compressive strength, elastic modulus and poisson's ratio in the calculation process.

Examples

A particle flow micromechanics parameter inversion method comprises the steps of developing a uniaxial compression experiment based on particle flow micromechanics parameters to obtain numerical simulation model macroscopic mechanical parameters, adjusting the particle flow micromechanics parameters by adopting a simulated annealing algorithm according to the difference value of the numerical simulation macroscopic mechanical parameters and the physical experiment macroscopic mechanical parameters, and simulating and calculating termination conditions by numerical values; the steps of combining the specific examples are as follows:

step 1: obtaining uniaxial compressive strength UCS of rock through indoor experiment_exprimentalElastic modulus E_experimentalPoisson's ratio v_experimentalRespectively as follows: 37MPa, 23.3GPa and 0.17. Meanwhile, the over-parameter Temperature of the simulated annealing algorithm is determined to be 100, the Temperature reduction coefficient Decay is determined to be 0.99, the Markov chain length Markov (the number of cycles under each Temperature condition) is determined to be 10000, and the data jitter factor Stepfactor is determined to be 0.02. And determining micromechanics parameters (particle density rho, ratio R of maximum radius to minimum radius of particles)_max/R_minMinimum radius of particle R_minParticle contact stiffness E_cRatio k of normal stiffness to tangential stiffness of the particles_n/k_sParallel connection stiffnessParallel connection normal stiffness to tangential stiffness ratioAverage value sigma of particle friction coefficient mu and parallel connection normal stiffness_c-meanParallel connected normal stiffness variance σ_c-stdParallel connected shear stiffness mean τ_c-meanParallel connection shear stiffness variance τ_c-std) Respectively are: (1000,1,0.1e-3,1e9,0.1,1e9,0.1,0.1,1e6,1e6,1e6,1e6) and (5000,10,10e-3,1000e9,10,1000e9,10, 1000e6,100e6,1000e6,100e 6).

Step 2: initial micromechanical parameter (p)_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-pre) The initial micromechanics parameter combination is obtained by directly and randomly selecting the minimum value and the maximum value of the micromechanics parameters. Then, carrying out a numerical uniaxial compression experiment by adopting the initial micromechanics parameter combination to obtain the macroscopic mechanics parameters: the uniaxial compressive strength, the elastic modulus and the Poisson's ratio are respectively UCS_{numerical-pre}、E_{numerical-pre}、v_{numerical-pre}. And (3) calculating an objective function:

simultaneously, the initial micromechanics parameters are regarded as the optimal micromechanics parameters:

the corresponding discrimination value is also set as the optimum discrimination value:

Judge_best＝Judge_pre

and step 3: based on a previous set of micromechanical parameters (p)_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-pre) Determining the values of the next set of micromechanics parameters, which are calculated according to the following formula:

wherein rand is a random number from-1 to 1. The previous micromechanical parameter set (p) can be based on the above formula_pre、R_max-pre/R_min-pre、R_min-pre、E_c-pre、k_n-pre/k_s-pre、μ_pre、σ_c-mean-pre、σ_c-std-pre、τ_c-mean-pre、τ_c-std-pre) Generating a current set of micromechanical parameters (p)_next、R_max-next/R_min-next、R_min-next、E_c-next、k_n-next/k_s-next、μ_next、σ_c-mean-next、σ_c-std-next、τ_c-mean-next、τ_c-std-next) Adopting the current micromechanics parameter set to develop a uniaxial compression numerical simulation experiment to obtain corresponding numerical simulation model macroscopic mechanics parameters: compressive strength of single axis UCS_{numerical-next}Elastic modulus E_{numerical-next}And poisson's ratio v_{numerical-next}. And calculateValue Judge of discriminant function_next：

And 4, step 4: if calculated Judge_next<Judge_bestThen, updating the optimal micromechanics parameter set and the optimal target value:

Judge_best＝Judge_next

if Judge_next>Judge_bestThen the optimal set of micro-mechanics parameters need not be updated.

And 5: if calculated Judge_next<Judge_preUpdating the previous particle flow micromechanics parameters and the previous judgment function values:

Judge_pre＝Judge_next

when Judge_next≥Judge_preFirst, a probability value p is calculated₁，p₁Can be expressed as:

then a random number p of 0 to 1 is generated₂If p is₁>p₂The previous particle flow micromechanics parameters and the previous judgment function values are updated, namely the formula (17) and the formula (18) are executed, otherwise, no operation is executed.

Step 6: updating Iteration times, wherein the Iteration is Iteration +1, and when the Iteration is more than 10000: the operation is 0, and the current Temperature decrease is Temperature × 0.99. After step 6 is executed, step 3 is executed again, and the process is cycled in sequence.

And the termination condition of the numerical simulation calculation is that the numerical simulation calculation is terminated when the relative error between the macroscopic mechanical parameters obtained by the numerical simulation calculation and the macroscopic mechanical parameters obtained by the physical experiment is less than 10 percent. In the numerical simulation calculation process, each iteration is performed on Judge in sequence_bestMaking a judgment when Judge_bestAnd stopping calculation when the concentration is less than or equal to 10 percent. Wherein Judge_bestCan be expressed as:

in the formula, UCS_{numerical-best}，E_{numerical-best}，v_{numerical-best}Represents the optimal combination of uniaxial compressive strength, elastic modulus and poisson's ratio in the calculation process. Obtaining UCS through calculation_{numerical-best}，E_{numerical-best}，v_{numerical-best}35.82MPa, 23.18GPa and 0.16, respectively, wherein the corresponding Judge_best5.8 percent, which is less than 10 percent of the experiment termination condition.

Simultaneously obtaining a corresponding optimal micromechanics parameter set (rho)_best、R_max-best/R_min-best、R_min-best、E_c-best、k_n-best/k_s-best、μ_best、σ_c-mean-best、σ_c-std-best、τ_c-mean-best、τ_c-std-best) Is (2411, 5.54, 0.76e-3, 1.76e9, 0.57, 55.96e9, 2.94,6.28, 77.63e6, 77.76e6, 64.04e6, 33.52e 6).