CN-122021220-A - High-cold high-altitude freeze thawing side slope risk simulation method based on region identification

CN122021220ACN 122021220 ACN122021220 ACN 122021220ACN-122021220-A

Abstract

The invention relates to the technical field of geotechnical engineering and geological disaster prevention in cold areas, in particular to a high-cold high-altitude freeze thawing side slope risk simulation method based on area identification, which comprises the steps of dispersing side slopes by utilizing an SPH method and initializing particle swarms; calculating physical field evolution indexes, identifying a phase change active region, a shearing damage region and a stable inert region through threshold comparison, solving a heat conduction and moisture migration equation, obtaining temperature field, phase change interface and moisture field data, performing self-adaptive adjustment on particles according to a region identification result, calculating stress response by combining a dynamic freeze thawing constitutive model, solving a momentum equation, updating a motion state, correcting a permeability coefficient, determining a time step according to a Brownian condition, and performing cyclic calculation. According to the invention, the self-adaptive adjustment of the particle resolution is realized through region identification, the precision and the calculation efficiency of the freeze-thawing interface tracking are considered, and the fine simulation of the freeze-thawing side slope multi-field coupling process is realized.

Inventors

ZHOU XINGBO
ZHANG KAI
ZHOU MINGJUN
LIU YONG
ZHENG HAOLEI
Yang Ziru
CHENG JIALIN
CHEN WENLONG
ZHANG YUN

Assignees

水电水利规划设计总院

Dates

Publication Date: 20260512
Application Date: 20260325

Claims (10)

1. The method for simulating the risk of the high-cold high-altitude freeze thawing side slope based on the region identification is characterized by comprising the following steps of: Step S1, discretizing a calculation domain by utilizing a smooth particle fluid dynamics method according to slope geological survey data, and initializing by gravity to obtain a particle swarm carrying mass, position, temperature, volume unfrozen water content, porosity and effective stress tensor; S2, calculating a physical field evolution index in a particle neighborhood according to the current state variable of the particle swarm, and comparing the physical field evolution index with a preset threshold value to obtain a region identification result; s3, solving a heat conduction equation and a moisture migration equation by adopting an SPH discrete format considering a phase-change latent heat source item according to the area identification result and the environmental boundary condition, and obtaining temperature field update data, phase-change interface position data and moisture field distribution data at the current moment; Step S4, according to the temperature field updating data, the variable interface position data and the area identification result, executing a self-adaptive adjustment strategy on particles of the particle swarm, and calculating a frost heaving strain increment and a stress response by combining a dynamic freeze thawing constitutive model to obtain a current particle resolution, a current stress tensor and a current plastic state; S5, according to the current stress tensor and the current plastic state, solving a momentum conservation equation to obtain particle motion data, and correcting a soil body permeability coefficient according to the current plastic state; and step S6, determining the time step of the next calculation time step by utilizing the Brownian condition according to the particle motion data, and circularly executing the steps S2 to S5 until the preset freeze-thawing cycle is completed.
2. The regional identification-based high-cold high-altitude freeze-thaw side slope risk simulation method according to claim 1, wherein the process of step S2 includes: calculating the temperature gradient modulus in the vicinity of the particles based on the temperature state variables carried by the particles, and identifying the region where the particles with the temperature gradient modulus larger than a first threshold value are located as a phase change active region; Based on the effective stress tensor carried by the particles, calculating equivalent plastic strain increment of the particles, and identifying a region where the particles with the equivalent plastic strain increment larger than a second threshold value are located as a shearing damage region; And identifying the area where the particles which do not belong to the phase change active area and the shear damage area are located as a stable inert area.
3. The regional identification-based high-cold high-altitude freeze-thaw side slope risk simulation method according to claim 2, wherein the process of calculating the temperature gradient modulus in the vicinity of the particles comprises: and (3) adopting an SPH kernel function approximation method, calculating to obtain a temperature gradient tensor through a discrete summation format based on the temperature value, the mass, the density and the kernel function gradient vector of the particles and the adjacent particles in the neighborhood of the particles, and taking the norm of the tensor as a temperature gradient modulus.
4. The method for simulating risk of a freeze thawing slope at high cold and high altitude based on regional identification according to claim 3, wherein the process of step S3 comprises: Constructing environment boundary entity particles, and assigning a temperature time course or a water potential state to the environment boundary entity particles according to the environment boundary conditions; according to the region identification result, dynamically assigning corresponding heat conduction coefficients and permeability coefficients to particles in different regions; and accumulating and calculating heat flux and moisture flux between the particles and adjacent particles and environmental boundary entity particles based on the SPH kernel function discrete format, and solving to obtain temperature field update data and moisture field distribution data at the current moment.
5. The regional identification-based high-cold high-altitude freeze-thaw side slope risk simulation method according to claim 4, wherein the process of cumulatively calculating the heat flux and the water flux between the particles and their neighboring particles and the environmental boundary solid particles based on SPH kernel function discrete format comprises: In heat conduction calculation, constructing a discrete equation according to the temperature difference, the heat conductivity coefficient, the mass, the density and the kernel function gradient of the calculated particles and the adjacent particles, introducing a phase change latent heat source item, and solving the temperature change rate, wherein the heat conductivity coefficient adopts dynamic partition assignment according to the particle position and the area identification result; In the calculation of moisture migration, a discrete equation is constructed according to the pore pressure difference, the permeability coefficient and the kernel function gradient of the calculated particles and the adjacent particles, and an anti-singular coefficient is introduced to correct the calculation singularity when the inter-particle distance is too close, so that the moisture flux is obtained by solving.
6. The regional identification-based high-cold high-altitude freeze-thaw side slope risk simulation method according to claim 5, wherein the process of performing an adaptive adjustment strategy on particles of the particle swarm comprises: when the temperature gradient modulus of the particles exceeds a first threshold value, performing particle splitting operation, decomposing single parent particles into a preset number of child particles, inheriting the mass, the speed, the temperature and the effective stress tensor of the parent particles to the child particles based on the mass conservation and momentum conservation principles, and simultaneously, correspondingly reducing the smooth length of the child particles to improve the local calculation resolution; And identifying particles in the stable inert region as the region identification result, and executing particle merging operation when the particle spacing is smaller than a preset merging threshold value and the difference degree of the physical field variables is smaller than a limiting value, so that adjacent particles are polymerized and reconstructed into single coarse particles.
7. The regional identification-based high-cold high-altitude freeze-thawing side slope risk simulation method according to claim 6, wherein the process of calculating the frost heaving strain increment and the stress response by combining the dynamic freeze-thawing constitutive model to obtain the current particle resolution, the current stress tensor and the current plastic state comprises the following steps: According to the temperature change amount and the phase change interface position data in the temperature field updating data, combining the porosity of the soil body and the unfrozen water content change, and calculating by using a frost heaving coefficient to obtain a frost heaving strain increment; calculating a total strain increment according to the particle motion data, and subtracting a thermal strain increment and the frost heaving strain increment from the total strain increment to obtain an effective mechanical strain increment; calculating the effective mechanical strain increment by using an elastic constitutive matrix to obtain a heuristic stress tensor, and dynamically updating cohesive force and internal friction angle parameters in a yield criterion based on the current temperature; And adopting a radial return mapping algorithm to carry out plastic correction on the heuristic stress tensor, and mapping the stress state back to the updated yield surface to obtain the current stress tensor and the current plastic state.
8. The regional identification-based high-cold high-altitude freeze-thaw side slope risk simulation method according to claim 7, wherein the process of step S5 includes: calculating stress divergence items in the particle neighborhood by utilizing an SPH kernel function approximation method according to the current stress tensor, constructing a momentum conservation equation by combining gravity volume force items, and solving to obtain particle acceleration; Integrating the acceleration of the particles by adopting an explicit time integration algorithm, and updating the speed and position data of the particles; and extracting accumulated equivalent plastic strain in the current plastic state, substituting the accumulated equivalent plastic strain into a preset permeability coefficient evolution model, and dynamically correcting the permeability coefficient of the soil body.
9. The regional identification-based high-cold high-altitude freeze-thawing side slope risk simulation method according to claim 8, wherein the process of dynamically correcting the soil permeability coefficient comprises the following steps: Introducing a damage variable to describe the evolution of a soil body microscopic pore structure, and establishing a mapping relation between accumulated equivalent plastic strain and the damage variable; calculating the porosity variation according to the damage variable, and calculating the permeability coefficient correction coefficient at the current moment by using an exponential damage permeability formula; Multiplying the initial permeability coefficient by the permeability coefficient correction coefficient to obtain a corrected soil permeability coefficient for water migration calculation at the next moment.
10. The regional identification-based high-cold high-altitude freeze-thaw side slope risk simulation method according to claim 9, wherein the process of determining the time step of the next calculation time step using the kurroa condition comprises: calculating a mechanical stability critical step length based on the inter-particle distance and the current sound velocity, a thermal stability critical step length based on a thermal diffusion coefficient, and a moisture migration stability critical step length based on a corrected permeability coefficient; Selecting the minimum value of the mechanical stability critical step length, the thermal stability critical step length and the moisture migration stability critical step length, and multiplying the minimum value by a preset safety coefficient to be used as the time step length of the next calculation time step; judging whether the current accumulated time reaches a preset freezing and thawing cycle period, if so, stopping calculating and outputting the slope displacement field, the stress field and the plastic region data.

Description

High-cold high-altitude freeze thawing side slope risk simulation method based on region identification Technical Field The invention relates to the technical field of geotechnical engineering and geological disaster prevention and control in cold areas, in particular to a high-cold high-altitude freeze thawing side slope risk simulation method based on area identification. Background The freeze thawing cycle induced slope destabilization relates to a strong nonlinear coupling process of moisture migration, phase change frost heaving and mechanical damage. The existing numerical simulation mostly adopts a grid method such as a finite element method, grid distortion is easy to occur when the freezing expansion deformation is processed, the moving freezing front is difficult to track efficiently, and the problem of high calculation cost is faced to the global encryption. Although the smooth particle fluid dynamics method can overcome the grid distortion problem, the prior art generally adopts uniform particle distribution, and the resolution cannot be adaptively adjusted according to the physical field evolution characteristics, so that the calculation accuracy in key areas such as a phase change interface or a shear band is insufficient. Meanwhile, the existing method often ignores permeability evolution caused by soil shearing damage, and is difficult to accurately describe moisture enrichment caused by damage and a feedback mechanism for aggravating frost heaving. Therefore, a simulation method capable of adaptively adjusting the calculation accuracy and coupling the damage-seepage effect is needed. Disclosure of Invention Therefore, the invention provides a high-cold high-altitude freeze thawing side slope risk simulation method based on region identification, which is used for solving the problems in the prior art. In order to achieve the above purpose, the invention provides a high-cold high-altitude freeze thawing side slope risk simulation method based on region identification, which comprises the following steps: Step S1, discretizing a calculation domain by utilizing a smooth particle fluid dynamics method according to slope geological survey data, and initializing by gravity to obtain a particle swarm carrying mass, position, temperature, volume unfrozen water content, porosity and effective stress tensor; S2, calculating a physical field evolution index in a particle neighborhood according to the current state variable of the particle swarm, and comparing the physical field evolution index with a preset threshold value to obtain a region identification result; s3, solving a heat conduction equation and a moisture migration equation by adopting an SPH discrete format considering a phase-change latent heat source item according to the area identification result and the environmental boundary condition, and obtaining temperature field update data, phase-change interface position data and moisture field distribution data at the current moment; Step S4, according to the temperature field updating data, the variable interface position data and the area identification result, executing a self-adaptive adjustment strategy on particles of the particle swarm, and calculating a frost heaving strain increment and a stress response by combining a dynamic freeze thawing constitutive model to obtain a current particle resolution, a current stress tensor and a current plastic state; S5, according to the current stress tensor and the current plastic state, solving a momentum conservation equation to obtain particle motion data, and correcting a soil body permeability coefficient according to the current plastic state; and step S6, determining the time step of the next calculation time step by utilizing the Brownian condition according to the particle motion data, and circularly executing the steps S2 to S5 until the preset freeze-thawing cycle is completed. Further, the process of step S2 includes: calculating the temperature gradient modulus in the vicinity of the particles based on the temperature state variables carried by the particles, and identifying the region where the particles with the temperature gradient modulus larger than a first threshold value are located as a phase change active region; Based on the effective stress tensor carried by the particles, calculating equivalent plastic strain increment of the particles, and identifying a region where the particles with the equivalent plastic strain increment larger than a second threshold value are located as a shearing damage region; And identifying the area where the particles which do not belong to the phase change active area and the shear damage area are located as a stable inert area. Further, the process of calculating the temperature gradient modulus in the vicinity of the particle includes: and (3) adopting an SPH kernel function approximation method, calculating to obtain a temperature gradient tensor through a discrete summation format based o