A method for simulating three-dimensional sand ripple formation and evolution

Abstract

This invention discloses a method for simulating the formation and evolution of three-dimensional sand ripples, relating to the field of wind and sand simulation, including the following steps: Step 1: Constructing a simulation environment, setting the total simulation duration, dividing the three-dimensional flow field grid and the two-dimensional surface grid, calculating the initial steady-state flow field, and releasing induced spherical particles in the flow field; Step 2: Tracking the motion trajectory of all spherical particles in the air through a pre-constructed and trained particle trajectory model, and calculating their position and velocity updates under the drag force and gravity of the flow field by solving the particle motion equation; While ensuring the authenticity of physical processes such as sand particle jump, collision, and deposition, the computational complexity is greatly reduced, thus enabling the simulation of large-scale, long-term three-dimensional sand ripple formation, migration, and merging on ordinary computing platforms, and dynamically simulating and outputting the entire process of three-dimensional sand ripples from an initial unstable sand bed to a mature form.

Description

Technical Field

[0001] This invention relates to the field of wind and sand simulation technology, specifically a method for simulating the formation and evolution of three-dimensional sand ripples. Background Technology

[0002] In desert regions, railways, highways, oilfield facilities, and other infrastructure are often threatened by shifting sands. Accurately predicting the movement and deposition of sand ripples is a prerequisite for designing effective sand control projects. Sand ripples are the most common structure on sandy surfaces and are prevalent in major deserts. Currently, the reproduction of the formation of three-dimensional sand ripples is mainly achieved through direct experiments. However, wind tunnel experiments are difficult to control variables, which is not conducive to discovering the formation and evolution mechanism of sand ripples. The numerical simulation technology of sand ripples currently used is relatively simple and mostly in two-dimensional form, which cannot reproduce the entire three-dimensional process of sand ripple development.

[0003] Existing sand ripple texture models neglect the aerial motion of particles, only considering the initial and final positions of particles on the surface. They also lack a description of the wind field, ignoring the impact of surface changes on the wind field. The topography of sand ripple surfaces is obviously constantly evolving and changing. Existing models do not consider the impact of topographic changes on the wind field, which is clearly unreasonable. On the other hand, most existing sand ripple models are two-dimensional representations, which make it difficult to present the structural features of sand ripples. At the same time, current numerical simulations mainly use the discrete element method, which tracks all particles in the system. This simulation method requires a huge amount of computation and is costly. This means that current sand ripple simulations can only be performed in a small area for a short time in two dimensions. In addition, the boundary between surface particles and airborne moving particles in the discrete element method model is unclear, the determination of moving and stationary particles is unclear, and the separation between particle airborne collision and surface splashing processes is unclear. This makes it difficult to carry out some theoretical analysis work based on numerical simulation, and the influence of particle and flow field properties on the results cannot be clearly and directly reflected. Summary of the Invention

[0004] (a) Technical problems to be solved In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for simulating the formation and evolution of three-dimensional sand ripples, which can effectively solve the problems of the existing technology.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: This invention discloses a method for simulating the formation and evolution of three-dimensional sand ripples, comprising the following steps: Step 1: Construct the simulation environment, set the total simulation duration, divide the three-dimensional flow field grid and the two-dimensional surface grid, calculate the initial steady-state flow field, and release induced spherical particles in the flow field; Step 2: Track the motion trajectory of all spherical particles in the air using a pre-built and trained particle trajectory model, and calculate their position and velocity updates under the influence of flow field drag force and gravity by solving the particle motion equations. Step 3: Identify particles with a height lower than the current ground surface height as colliding particles, record their incident position, obtain the rebound recovery coefficient and rebound angle based on the current ground surface properties and the incident kinetic energy of the particles, and calculate the energy of the splashed particles; Step 4: Determine the takeoff behavior. If the energy of the splashed particles is greater than or equal to the minimum takeoff energy threshold, calculate the velocity and position of the particles after rebounding or splashing; otherwise, determine that the particles are deposited at the collision point. Step 5: Count the number of incident, rebound, splash, and deposited particles in each surface grid, and dynamically calculate and update the grid surface height based on particle volume and packing density; Step 6: Count the number and velocity of particles in each flow field grid, calculate the reaction force of the particles on the flow field, and update the steady-state flow field parameters. The specific statistical method for calculating the reaction force of the particles on the flow field is as follows: In each flow field grid cell, the drag force of the fluid on all particles passing through the cell in that time step is vector summed, and the negative value is the total reaction force of the particles on the flow field in that cell. Substitute this reaction force as a source term into the flow field control equation, and then solve and update the steady-state flow field parameters for the next time step. Step 7: Repeat steps 2-6 until the cumulative running time reaches the set value.

[0006] Furthermore, the process of dividing the three-dimensional flow field grid and the two-dimensional surface grid in step 1 is as follows: A regular orthogonal two-dimensional grid is established in the horizontal plane as the surface grid, and each grid node records the vertical height value; Based on the planar extent of the surface grid, a three-dimensional layered structure is formed by extending vertically to serve as the flow field grid, so that the surface grid cells and the bottom layer cells of the flow field grid completely overlap on the horizontal projection plane.

[0007] Furthermore, in the process of calculating the initial steady-state flow field in step 1, under the condition of no particles, according to the preset inlet boundary wind speed profile and pressure gradient driving conditions, the steady wind speed distribution of each flow field grid node is obtained by solving the momentum balance equation of the incompressible fluid. The wind speed distribution is used as the initial flow field input data for subsequent particle motion tracking.

[0008] Furthermore, the construction and training process of the particle orbital model in step 2 is as follows: The model parameters are initialized based on the preset physical properties of spherical particles. The particle motion is constrained only by the fluid drag force and gravity, and the rotational degree of freedom of the particles is excluded. A particle dynamics calculation framework is established. By analyzing the force relationship between the fluid and the particles, the flow field velocity, particle velocity, particle mass and relaxation time are input into the dynamic equations to calculate the particle acceleration value in real time. The trajectory prediction is performed using a time-step integration method. The particle velocity vector is updated based on the particle acceleration at the current moment, and the spatial displacement of the particle is iteratively calculated based on the updated velocity vector to form a continuous motion trajectory.

[0009] Furthermore, the particle motion equation in step 2 is: ; In the formula, Represents time, Represents particle mass. Represents the velocity of the particles. This represents the drag force exerted by the flow field on the particles. , Represents the flow field velocity. Represents particle velocity. It represents gravitational acceleration.

[0010] Furthermore, in step 3, the energy of the splashed particles follows a log-normal distribution, and its probability density function is: ; In the formula, Represents a single collision event The kinetic energy of the particles splashed up in the middle, Representative collision event Controlling the width of energy distribution, Representative collision event Location parameters of the distribution This represents the logarithmic value of the kinetic energy of the splashed particles.

[0011] Furthermore, the formula for calculating the change in grid surface height in step 5 is: ; In the formula, Represents the change in grid surface height. Represents the total number of particles in the current surface grid. Representing the The volume of each particle This represents the current particle packing density within the bed surface. This represents the total area of ​​the current surface grid.

[0012] Furthermore, the steady-state flow field in step 6 is obtained by simplifying the Reynolds-averaged Navier-Stokes equations, which are expressed as follows: ; in, This represents the wind speed vector after time averaging. This represents the pressure gradient after time averaging. Represents fluid shear stress. This represents the reaction force of particles on the flow field. This represents the fraction of the total volume that particles occupy within the grid. By assuming the flow field is a steady-state, uniform, incompressible flow field, and defining x as the flow direction, y as the expansion term, and z as the vertical direction, the above equation simplifies to: ;in, This represents the reaction force of the particle on the flow field in the x-direction.

[0013] Furthermore, the flow field control equations are simplified based on the Reynolds-averaged Navier-Stokes equations, neglecting the effects of horizontal inertial forces and temperature, and retaining only the vertical momentum balance.

[0014] Furthermore, the particles in step 1 are all spherical and rotation is ignored, and the surface grid nodes continuously store and update elevation data.

[0015] Furthermore, in step 1, when dividing the three-dimensional flow field grid and the two-dimensional surface grid, the grid size is determined based on the following: the side length of the horizontal grid is not greater than a predetermined multiple of the average diameter of sand grains in the simulated environment, and the thickness of the vertical grid layer is determined by the near-surface wind speed gradient variation characteristics, so as to ensure that the flow field resolution can capture the key dynamic processes of particle initiation and transport.

[0016] Furthermore, the specific method for obtaining the rebound recovery coefficient and its rebound angle in step 3 is as follows: based on the current surface grid's sand particle size distribution, water content attributes, and particle incident kinetic energy, query the pre-established parameter mapping table or call an empirical function to determine the normal and tangential recovery coefficients and the probability distribution of the rebound angle corresponding to this collision.

[0017] (III) Beneficial Effects Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: By integrating the interaction between the flow field, particles, and the surface, and replacing complex micro-fluid calculations with splash energy calculations based on log-normal distribution, the computational complexity is significantly reduced while ensuring the realism of physical processes such as sand particle transition, collision, and deposition. This enables the simulation of large-scale, long-term three-dimensional sand ripple formation, migration, and merging on ordinary computing platforms.

[0018] By updating the three-dimensional surface grid in real time and feeding back the influence of the wind flow field, it is possible to dynamically simulate and output the entire process of three-dimensional sand ripples from the initial unstable sand bed to the mature form, intuitively showing the evolution law of its three-dimensional spatial characteristics, and providing a powerful tool for the study of aeolian geomorphology.

[0019] By introducing the reaction force of particles on the flow field, the mutual adjustment process between the wind flow field and the sand bed morphology is made more in line with physical reality. The resulting sand ripple morphology data has a higher degree of agreement with real environmental observations or wind tunnel experimental data, thus providing more reliable prediction data for surface environment research such as windbreak and sand fixation projects and sand hazard control. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0021] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] The present invention will be further described below with reference to embodiments.

[0024] This embodiment presents a method for simulating the formation and evolution of three-dimensional sand ripples, such as... Figure 1 As shown, it includes the following steps: Step 1: Construct the simulation environment, set the total simulation duration, divide the 3D flow field grid and the 2D surface grid, calculate the initial steady-state flow field, and release induced spherical particles in the flow field; all particles are spherical and rotation is ignored, and the surface grid nodes continuously store and update elevation data. The grid size is determined based on the following criteria: the horizontal grid side length is no greater than a predetermined multiple of the average diameter of sand grains in the simulation environment, and the vertical grid layer thickness is determined by the near-surface wind speed gradient characteristics to ensure that the flow field resolution can capture the key dynamic processes of particle initiation and transport.

[0025] Step 2: Track the motion trajectory of all spherical particles in the air using a pre-built and trained particle trajectory model, and calculate their position and velocity updates under the influence of flow field drag force and gravity by solving the particle motion equation.

[0026] Step 3: Identify particles with a height lower than the current ground level as colliding particles, record their incident position, obtain the rebound recovery coefficient and rebound angle based on the current ground properties and the incident kinetic energy of the particles, and calculate the energy of the splashed particles; the minimum take-off energy threshold is predetermined through calibration experiments or theoretical analysis, representing the minimum kinetic energy required for sand particles to overcome surface adsorption and enter the airflow. This threshold is related to the material, shape, and ambient humidity of the surface particles; the energy of the splashed particles follows a log-normal distribution, and its probability density function is: ; In the formula, Represents a single collision event The kinetic energy of the particles splashed up in the middle, , Representative collision event Controlling the width of energy distribution, Representative collision event Location parameters of the distribution This represents the logarithmic value of the kinetic energy of the splashed particles. The specific method for obtaining the rebound recovery coefficient and its rebound angle in step 3 is as follows: based on the current surface grid's sand particle size distribution, water content attributes, and particle incident kinetic energy, a pre-established parameter mapping table is queried or an empirical function is called to determine the normal and tangential recovery coefficients and the probability distribution of the rebound angle corresponding to this collision.

[0027] Step 4: Determine the take-off behavior. If the energy of the splashed particles is greater than or equal to the minimum take-off energy threshold, calculate the velocity and position of the particles after rebounding or splashing; otherwise, determine that the particles are deposited at the collision point.

[0028] Step 5: Statistically count the incident, bounce, splash, and deposition quantities of particles within each surface grid. Combined with particle volume and packing density, dynamically calculate and update the grid surface height. The formula for calculating the change in grid surface height is: ; In the formula, Represents the change in grid surface height. Represents the total number of particles in the current surface grid. Representing the The volume of each particle This represents the current particle packing density within the bed surface. This represents the total area of ​​the current surface grid.

[0029] Step 6: Count the number and velocity of particles in each flow field grid, calculate the reaction force of the particles on the flow field, and update the steady-state flow field parameters. The specific statistical method for calculating the reaction force of the particles on the flow field is as follows: Within each flow field grid cell, vector sum the fluid drag forces experienced by all particles passing through that cell in that time step; the negative value is the total reaction force of the particles on the flow field within that cell. Substitute this reaction force as a source term into the flow field control equations to solve and update the steady-state flow field parameters for the next time step. The steady-state flow field is obtained by simplifying the Reynolds-averaged Navier-Stokes equations, which are expressed as: ; in, This represents the wind speed vector after time averaging. This represents the pressure gradient after time averaging. Represents fluid shear stress. This represents the reaction force of particles on the flow field. This represents the fraction of the total volume that particles occupy within the grid. By assuming the flow field is a steady-state, uniform, incompressible flow field, and defining x as the flow direction, y as the expansion term, and z as the vertical direction, the above equation simplifies to: ;in, This represents the reaction force of particles on the flow field in the x-direction. It breaks through the limitations of traditional two-dimensional profiles or static simulations and constructs a complete dynamic feedback of flow field, particle motion, and landform. That is, particle motion is driven by the flow field, and its collision and deposition dynamically update the three-dimensional landform. The change in landform affects the flow field structure through boundary conditions. At the same time, the existence of particle groups itself acts as a source term to react on the flow field, ensuring that the simulation results can spontaneously emerge complex three-dimensional spatial evolution characteristics such as the migration of crescent dune ridges and the extension of arms, revealing their inherent formation mechanism.

[0030] Step 7: Repeat steps 2-6 until the cumulative running time reaches the set value; the flow field control equation is based on the Reynolds-averaged Navier-Stokes equations. In the simplification process, the horizontal inertial force and temperature effects are ignored, and only the vertical momentum balance is retained.

[0031] Compared with existing technologies, this approach introduces a pre-trained particle trajectory model to quickly track the movement of a large number of sand particles, and uses a parameterized splash model based on log-normal distribution to efficiently simulate the complex bouncing and splashing process when particles collide with the bed surface. This approach cleverly avoids the extremely time-consuming direct numerical simulation or computational fluid dynamics analysis of each microscopic collision event, thereby greatly reducing the consumption of computing resources and shortening the simulation time while maintaining the authenticity of key physical processes, making it possible to perform large-scale, long-term simulations on ordinary computing platforms.

[0032] This method has excellent engineering applicability and quantitative accuracy. Its physics-based model and parameterization scheme ensure that the key parameters of the simulated sand ripples, such as wavelength, wave height, and movement speed, have a higher degree of agreement with real wind tunnel experiments or field observation data.

[0033] At other levels, this embodiment also provides a process for dividing a three-dimensional flow field grid and a two-dimensional surface grid, specifically as follows: A regular orthogonal two-dimensional grid is established in the horizontal plane as the surface grid, and each grid node records the vertical height value; Based on the planar extent of the surface grid, a three-dimensional layered structure is formed by extending vertically to serve as the flow field grid, so that the surface grid cells and the bottom layer cells of the flow field grid completely overlap on the horizontal projection plane.

[0034] In the process of calculating the initial steady-state flow field, under the condition of no particles, the steady-state wind speed distribution of each flow field grid node is obtained by solving the momentum balance equation of the incompressible fluid by solving the preset inlet boundary wind speed profile and pressure gradient driving conditions, based on the preset inlet boundary wind speed profile and pressure gradient driving conditions. The wind speed distribution is used as the initial flow field input data for subsequent particle motion tracking.

[0035] Compared with existing technologies, by adopting a correlation partitioning strategy of two-dimensional surface grids with completely overlapping horizontal projections and three-dimensional layered flow field grids, and pre-calculating the steady-state wind speed field under particle-free conditions, not only is the accurate correspondence and efficient transfer of particle force calculation and ground performance update in spatial data guaranteed, but the complexity of initial flow field construction is also greatly simplified. This provides a stable and efficient numerical basis for subsequent large-scale bidirectional integrated simulation of particles and flow fields, avoiding interpolation errors and computational overhead caused by grid mismatch in traditional methods.

[0036] This embodiment provides a process for constructing and training a particle orbital model as follows: The model parameters are initialized based on the preset physical properties of spherical particles. The particle motion is constrained only by the fluid drag force and gravity, and the rotational degree of freedom of the particles is excluded. A particle dynamics calculation framework is established. By analyzing the force relationship between the fluid and the particles, the flow field velocity, particle velocity, particle mass and relaxation time are input into the dynamic equations to calculate the particle acceleration value in real time. The trajectory prediction is performed using a time-step integration method. The particle velocity vector is updated based on the particle acceleration at the current moment, and the spatial displacement of the particle is iteratively calculated based on the updated velocity vector to form a continuous motion trajectory.

[0037] The equation of motion for the particles is: ; In the formula, Represents time, Represents particle mass. Represents the velocity of the particles. This represents the drag force exerted by the flow field on the particles. , Represents the flow field velocity. Represents particle velocity. It represents gravitational acceleration.

[0038] Compared with existing technologies, a particle dynamics framework is established under the premise of pre-defined physical properties of spherical particles, only subject to fluid drag force and gravity, and excluding the rotational degree of freedom of particles. By taking the flow field velocity, particle mass and relaxation time as inputs, the particle acceleration is calculated in real time and the velocity and displacement are updated by time stepping method to form a continuous trajectory.

[0039] Compared to high-degree-of-freedom models that require correlation of the entire field CFD and consideration of particle rotation and multiple forces, this method significantly reduces the computational load and calibration difficulty, supports online real-time trajectory prediction and rapid training, has a clearer structure, more direct parameter calibration, can efficiently connect to existing flow field data interfaces, and has better interpretability and fault tolerance under the condition of known particle physical properties.

[0040] In summary, this invention achieves a leap from two-dimensional planar simulation to full three-dimensional dynamic evolution simulation. While ensuring computational efficiency, it enhances the physical realism of the simulation process and the credibility of the results. Through a fully integrated dynamic interaction mechanism of flow field, particles, and terrain, it overcomes the limitations of traditional models that can only simulate two-dimensional profiles or rely on static flow fields. By tracking only moving particles rather than all sand particles and parameterizing the splashing process, it simplifies complex group motion into computable physical events, greatly reducing the massive computational overhead of three-dimensional simulation and resolving the inherent contradiction between computational efficiency and model complexity. Secondly, by updating the surface morphology in real time and synchronously feeding it back to the flow field calculation, it introduces the reaction force of particles on the flow field, accurately depicting the key physical process of dynamic interaction between wind and sand flow and terrain during the formation of sand ripples, ensuring the scientific nature of evolution dynamics. The final generated data is three-dimensional sand ripple morphology data with spatial continuity, which can realistically reproduce the non-uniform development of sand ripples in length, width and height, as well as complex lateral connection and merging phenomena, providing an unprecedented high-fidelity numerical simulation tool for scientific research and engineering applications in fields such as aeolian geomorphology and desert engineering prevention. This invention considers the coupling mechanism of flow field, particles, and terrain, and reproduces the three-dimensional formation and evolution process of sand ripples through numerical simulation. It establishes a new model that takes into account the necessary physical mechanisms required for sand ripple simulation, saves more computing resources, is more efficient, and has a lower cost. It can realize three-dimensional numerical simulation of sand ripples over a longer period of time and on a larger scale.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of simulating three-dimensional sand ripple formation and evolution, characterized by, Includes the following steps: Step 1: Construct the simulation environment, set the total simulation duration, divide the three-dimensional flow field grid and the two-dimensional surface grid, calculate the initial steady-state flow field, and release induced spherical particles in the flow field; Step 2: Track the motion trajectory of all spherical particles in the air using a pre-built and trained particle trajectory model, and calculate their position and velocity updates under the influence of flow field drag force and gravity by solving the particle motion equations. Step 3: Identify particles with a height lower than the current ground surface height as colliding particles, record their incident position, obtain the rebound recovery coefficient and rebound angle based on the current ground surface properties and the incident kinetic energy of the particles, and calculate the energy of the splashed particles; Step 4: Determine the takeoff behavior. If the energy of the splashed particles is greater than or equal to the minimum takeoff energy threshold, calculate the velocity and position of the particles after rebounding or splashing; otherwise, determine that the particles are deposited at the collision point. Step 5: Count the number of incident, rebound, splash, and deposited particles in each surface grid, and dynamically calculate and update the grid surface height based on particle volume and packing density; Step 6: Count the number and velocity of particles in each flow field grid, calculate the reaction force of the particles on the flow field, and update the steady-state flow field parameters; Step 7: Repeat steps 2-6 until the cumulative running time reaches the set value; In step 1, during the calculation of the initial steady-state flow field, under the condition of no particles, the steady-state wind speed distribution of each flow field grid node is obtained by solving the momentum balance equation of the incompressible fluid based on the preset inlet boundary wind speed profile and pressure gradient driving conditions. The wind speed distribution is used as the initial flow field input data for subsequent particle motion tracking. In step 3, the energy of the splashed particles follows a log-normal distribution, and its probability density function is: ； wherein representing a single collision event representing the kinetic energy of the ejected particles, representing a collision event controlling the width of the energy distribution, representing a collision event a position parameter of the distribution, representing a logarithmic value of the kinetic energy of the ejected particles; The formula for calculating the change in grid surface height in step 5 is: ； wherein, represents the amount of change in the height of the grid surface, represents the total number of particles in the current grid surface, represents the volume of the th particle, represents the packing density of the particles in the current bed surface, represents the total area of the current grid surface; The steady-state flow field in step 6 is obtained by simplifying the Reynolds-averaged Navier-Stokes equations, which are expressed as follows: ； wherein, represents the time-averaged wind velocity vector, represents the time-averaged pressure gradient, represents the fluid shear stress, represents the reaction force of the particles on the flow field, represents the fraction of the volume of the particles in the grid to the total volume; By assuming that the flow field is a steady uniform incompressible flow field, x is the flow direction, y is the spanwise direction, and z is the vertical direction, the above equation is simplified as: ; wherein, represents the reaction force of the particle on the flow field in the x direction; The specific statistical method for calculating the reaction force of particles on the flow field is as follows: within each flow field grid cell, the drag force of the fluid on all particles passing through the cell in that time step is vector-summed, and the negative value is the total reaction force of the particles on the flow field in that cell; this reaction force is substituted into the flow field control equation as a source term, and then the steady-state flow field parameters for the next time step are solved and updated; the flow field control equation is based on the Reynolds-averaged Navier-Stokes equations, and the horizontal inertial force and temperature effects are ignored during simplification, only the vertical momentum balance is retained.

2. The method of claim 1, wherein, The process of dividing the three-dimensional flow field grid and the two-dimensional surface grid in step 1 is as follows: A regular orthogonal two-dimensional grid is established in the horizontal plane as the surface grid, and each grid node records the vertical height value; Based on the planar extent of the surface grid, a three-dimensional layered structure is formed by extending vertically to serve as the flow field grid, so that the surface grid cells and the bottom layer cells of the flow field grid completely overlap on the horizontal projection plane.

3. The method of claim 1, wherein, The process of constructing and training the particle orbital model in step 2 is as follows: The model parameters are initialized based on the preset physical properties of spherical particles. The particle motion is constrained only by the fluid drag force and gravity, and the rotational degree of freedom of the particles is excluded. A particle dynamics calculation framework is established. By analyzing the force relationship between the fluid and the particles, the flow field velocity, particle velocity, particle mass and relaxation time are input into the dynamic equations to calculate the particle acceleration value in real time. The trajectory prediction is performed using a time-step integration method. The particle velocity vector is updated based on the particle acceleration at the current moment, and the spatial displacement of the particle is iteratively calculated based on the updated velocity vector to form a continuous motion trajectory.

4. The method of claim 1, wherein, In step 1, all particles are spherical and rotation is ignored, and the surface grid nodes continuously store and update elevation data.

5. The method of claim 1, wherein, In step 1, when dividing the three-dimensional flow field grid and the two-dimensional surface grid, the grid size is determined based on the following: the side length of the horizontal grid is not greater than a predetermined multiple of the average diameter of sand grains in the simulated environment, and the thickness of the vertical grid layer is determined by the near-surface wind speed gradient variation characteristics.

6. The method of claim 1, wherein, The specific method for obtaining the rebound recovery coefficient and its rebound angle in step 3 is as follows: based on the current surface grid's sand particle size distribution, water content attributes, and particle incident kinetic energy, query the pre-established parameter mapping table or call an empirical function.

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