Mudslide surface spf particle adaptive processing method and mudslide real-time simulation method

Abstract

The application discloses a debris flow surface SPH particle adaptive processing method and a debris flow real-time simulation method. In view of the defect that the prior art is prone to misjudgment of surface particles, the application provides the debris flow surface SPH particle adaptive processing method. A particle density dynamic threshold is used as a surface particle identification standard, and an optimized dynamic threshold utilizes a real-time velocity gradient, a density gradient and a weight, and dynamically balances the contribution of debris flow characteristics to the surface particle determination. A neighborhood virtual particle is randomly generated around a surface particle centroid to support kernel calculation, and the density and velocity properties of the virtual particle are limited. The debris flow real-time simulation method is realized by using the adaptive processing technology. An optimization scheme adopts SPH-DEM coupling, a unified SPH framework is composed of heterogeneous units, the SPH method is used for calculating hydrodynamic force, the DEM method is used for calculating contact force, and bidirectional coupling force calculation is completed. The Niagara particle system is used for realizing the SPH method, and a digital twin platform foundation of integrated simulation of numerical simulation and visualization is constructed.

Description

TECHNICAL FIELD

[0001] The present application relates to a debris flow surface SPH particle adaptive processing method and a debris flow real-time simulation method, in particular to a debris flow dynamic simulation method based on an SPH model and a surface particle adaptive processing method in the debris flow dynamic simulation method. It belongs to the technical field of digital data processing, computer-aided design, and geological disaster simulation. BACKGROUND

[0002] Smoothed Particle Hydrodynamics (SPH) is a method of discretizing the research object into a system composed of a finite number of particles, which carry the material properties of the research object and provide mutual calculation support to simulate continuous fields. By weighting and superimposing the properties of all particles in the neighborhood of the target particle, the field function and its derivative are estimated, and the motion process of the system is simulated by solving the corresponding fluid equations. SPH method has been widely used in numerical simulation of various fluids.

[0003] Due to the original problem field that the method is developed for, SPH method has inherent defects in boundary condition processing. The core idea of SPH is the approximation of particles in the neighborhood, including kernel interpolation approximation and particle approximation. Therefore, in the fluid boundary part, due to the lack of boundary particles, the required neighborhood particles of the kernel function are insufficient, the kernel support domain is truncated, the interpolation integral is incomplete, the physical quantity calculation is distorted, and other calculation problems, which finally show abnormal boundary behavior. Fluid boundary problems involve two typical scenarios, one is the fluid-structure boundary scenario, and the other is the free surface of fluid. Since SPH method belongs to meshless particle method, when simulating problems involving complex phenomena such as large deformation free surface, liquid surface overlapping and breaking, moving boundary, etc., it is not necessary to introduce complex liquid surface tracking and other calculation techniques to capture these complex physical phenomena. Only the free surface boundary needs to be captured automatically, so in various SPH modeling techniques, there are not many methods to specifically solve the identification of free surface particles, which are mainly suitable for fluid modeling with relatively stable real material composition. The existing technology "Numerical simulation of movement process of pushover mass based on Lagrangian method" (Zheng Xiaogang, Sichuan University, 2021) discloses two methods commonly used to detect and identify fluid free surface particles. One is the identification method based on the particle density condition, that is, if the density of a particle satisfies the condition of being less than a set density value, it is considered as a free surface particle. The second is the identification method based on the number of particles around the fluid particle, that is, if the number of particles around a particle is less than a set number value, it is considered as a free surface particle.

[0004] Compared with the foregoing component stable fluid, the debris flow is a typical mixed fluid with complex and diverse material components, the collisions between the particles of the material components are frequent and different, and the local density and velocity field fluctuate violently. When particle aggregation (such as rolling accumulation) occurs in the free surface area, on the one hand, the particle density and quantity may not decrease significantly, which is easy to cause the free surface particles to be misjudged as the internal particles of the fluid; on the other hand, the short-time density and quantity decrease caused by internal disturbance may also cause misjudgment as the free surface particles. Therefore, the existing technology is not feasible to identify the debris flow free surface particles only by the “local particle information anomaly” such as quantity and density, which restricts the value of the debris flow SPH model in numerical simulation calculation or visual simulation. The existing technology “Debris flow movement simulation based on SPH method” (Wang Yaju, North China University of Water Resources and Electric Power, 2018) discloses a method for grid processing of a problem domain in a debris flow SPH model to find fluid surface particles. Specifically, for each SPH fluid particle, the particles in the surrounding grid are queried by hashing, if there is no particle in a certain grid around it, it is marked as a debris flow slurry surface particle, otherwise it is marked as a debris flow slurry internal particle. The method belongs to the second commonly used detection and identification method.

[0005] The foregoing existing technology is more suitable for fluid modeling of real material components that are relatively stable. Compared with this kind of fluid, the debris flow is a typical mixed fluid with complex and diverse material components, the collisions between the particles of the material components are frequent and different, and the local density and velocity field fluctuate violently. When particle aggregation (such as rolling accumulation) occurs in the free surface area, on the one hand, the particle density and quantity may not decrease significantly, which is easy to cause the free surface particles to be misjudged as the internal particles of the fluid; on the other hand, the short-time density and quantity decrease caused by internal disturbance may also cause misjudgment as the free surface particles. Therefore, the existing technology is not feasible to identify the debris flow free surface particles only by the “local particle information anomaly” such as quantity and density, which restricts the value of the debris flow SPH model in numerical simulation calculation or visual simulation. SUMMARY

[0006] The purpose of the present application is to overcome the shortcomings of the prior art, and provide a surface SPH particle adaptive processing method in debris flow dynamic simulation, and a debris flow simulation scheme realized by using the method.

[0007] To achieve the above-mentioned purpose, the present application first provides a debris flow surface SPH particle adaptive processing method.

[0008] A debris flow surface SPH particle adaptive processing method,

[0009] The smooth particle hydrodynamics method is used to set the parameters of the debris flow SPH model and initialize the model;

[0010] For any SPH particle i: if the particle density p local ( i , t ) meets a particle density dynamic threshold p thd ( i , t ), the particle i is determined as a debris flow surface SPH particle, otherwise, as a debris flow internal SPH particle;

[0011] The particle density dynamic threshold p thd ( i , t ) is expressed by a formula 1 model,

[0012] Formula 1

[0013] In the formula, p 0 - a basic density threshold,

[0014] N - a number of neighborhood particles of the particle i ,

[0015] p local ( i , t ) - a density of the particle i .

[0016] The above adaptive processing method of the debris flow surface SPH particle uses the particle density dynamic threshold as the surface particle identification standard, the dynamic threshold is determined by the average density of the neighborhood particles in the current time step, and the dynamic density is used to reflect the dynamic characteristics of the debris flow surface, so that the surface particle identification is associated with the dynamic characteristics of the debris flow surface, and the defects of the prior art fixed value comparison are overcome.

[0017] The application also provides the following optimization scheme of the adaptive processing method. The optimization technology improves the technical scheme from multiple technical aspects, and can be implemented independently on the premise of technical independence, or simultaneously on the premise of not causing technical logic conflicts.

[0018] Optimization 1, a fine function model of the particle density dynamic threshold p thd ( i , t ) is constructed.

[0019] In the formula 1 model, the p thd ( i , t ) is limited as a basic density thresholdp 01.2 times of the function, is the experience function combined with theoretical analysis and a large number of early test data summary. According to the data analysis of the results of the early research, under the condition of meeting p thd ( i , t )≤1.2 p 0, the system can avoid the misidentification of non-surface particles in the high-density disturbance area, so as to effectively identify the surface particles under the premise of ensuring numerical stability. However, in some model conditions at different time steps, the constant ratio 1.2 is always used, which may cause p thd ( i , t ) is not accurate for particles at specific coordinate positions, and some of the captured surface particles are distorted. The optimization scheme further introduces particle density and velocity related variables to construct a fine function model of p thd ( i , t ), which is specifically expressed by formula 2 model.

[0020] Formula 2

[0021] In the formula, -real-time density gradient of particle i ,

[0022] -real-time velocity gradient of particle i ,

[0023] -dimensionless particle velocity gradient normalized value, the value range is 0-1,

[0024] -dimensionless particle density gradient normalized value, the value range is 0-1,

[0025] l v and l ρ are the velocity gradient weight coefficient and the density gradient weight coefficient, respectively.

[0026] In formula 2 model, the velocity gradient weight coefficient l v is used to measure the influence of local velocity field change on the boundary threshold. When the velocity gradient is large, the local particle motion state changes rapidly, and it is more likely to appear in the free surface or boundary area, and increasing its weight in the judgment helps to improve the recognition sensitivity of the dynamic boundary. The density gradient weight coefficient l ρThe influence of local density field change on the boundary determination threshold is measured. When the density gradient is large, it indicates that the local particle distribution is sparse or the density change is severe, which is usually related to the physical properties of the boundary region, and increasing its weight helps to identify the surface particles at the density mutation. According to the numerical results of the test, the setting l v With l ρ At the same time, the value 0.1 mainly reflects two technical advantages: first, a good balance is achieved between the contribution of velocity gradient and density gradient to surface determination, that is, on the one hand, the sensitivity of velocity gradient to rapid change of free surface is ensured, and on the other hand, the role of density gradient in surface identification is maintained, thereby improving the robustness of the overall determination. Second, the computational complexity is reduced.

[0027] The model of formula 2 constructed by the optimization scheme introduces the normalized values of particle velocity gradient and density gradient conditions at the same time p thd ( i , t ) relative to p 0 Dynamic ratio relationship, and use the weight coefficient to integrate the change characteristics of velocity field and density field into the boundary determination threshold, realize the cooperative control of multi-physical field gradient, can reduce the misjudgment problem of relying on single index or fixed value only, so that in the numerical model process, for any time step, any particle calculation dynamically satisfies the control condition p thd ( i , t )≤1.2 p 0, realize the dynamic balance of numerical process, and the stability and accuracy of surface particle capture.

[0028] Optimization two, for any SPH particle, use its neighborhood particle properties, calculate the particle density according to formula 3 model p local ( i , t )。

[0029] Formula 3

[0030] In the formula,

[0031] N The number of neighborhood particles of particle i , j

[0032] m j The mass of neighborhood particles j ,

[0033] h ​Smoothed particle radius,

[0034] With respectively the position vector of particle i and particle j .

[0035] Optimization three, adaptive dynamic compensation for surface SPH particles after identification.

[0036] In any time step of the loop iteration, all particles of the SPH model are traversed, and the surface SPH particles of the debris flow and the internal SPH particles of the debris flow are identified, and the surface SPH particle set T sur and the internal SPH particle set T int are constructed respectively.

[0037] The average value of the number of neighborhood particles of all particles in the set T int is calculated n int .

[0038] For any particle in the set T sur , the number of neighborhood particles is calculated n real , and the difference between n int and n real is recorded as the number of neighborhood virtual particles that need to be supplemented in its support domain n need .

[0039] The centroid coordinates of the support domain are calculated , and a number of virtual particles n need are randomly generated around the centroid. The support kernel function calculation of the neighborhood virtual particles satisfies the density p tem and velocity ; after the calculation of the resultant force is completed, the system removes the neighborhood virtual particles.

[0040] Formula 4

[0041] Formula 5

[0042] Formula 6

[0043] In the formula, N ( i ) - the neighborhood particle set of particle i , and

[0044] - the position vector of the particle j

[0045] p local j - the density of the particle j

[0046] W r ij ,h - the smoothing kernel function

[0047] - the velocity vector of the particle j

[0048] The neighborhood virtual particle for supporting the surface particle kernel calculation is set at a position around the center of mass, and the main reason is that the offset of the center of mass relative to the particle itself position can effectively reveal the main direction of the missing particle support domain, that is, the center of mass position can show the completeness and asymmetry of the current particle support domain. Therefore, the process of supplementing the virtual particle around the center of mass is actually a benchmark dynamic adjustment to the support domain, which effectively and quickly overcomes the influence of the support domain defect on the kernel calculation.

[0049] The above-mentioned mudflow surface SPH particle adaptive processing method can be applied to mudflow simulation of a mudflow numerical model based on an SPH method. The mudflow numerical model based on the SPH method can be a mudflow model constructed by using the SPH method, or can be a mudflow model constructed by using an SPH-DEM coupling method, wherein the SPH method simulates a continuous fluid phase part in the mudflow, and a discrete element method (DEM method) simulates a discrete solid phase part in the mudflow.

[0050] Based on the above-mentioned mudflow surface SPH particle adaptive processing method of the present application, the present application simultaneously provides a mudflow real-time simulation method realized by using the technical solution, and the technical solution is as follows.

[0051] A mudflow real-time simulation method, a mudflow numerical model based on an SPH method is constructed, the mudflow numerical model based on the SPH method is a mudflow model constructed by using the SPH method, and the following implementation is further performed.

[0052] Step S100, defining model global parameters, creating all model micro-units and setting basic attributes according to calculation needs, and registering all model micro-units to a unified SPH framework, wherein the model micro-unit is an SPH particle;

[0053] Step S200, constructing a global acceleration structure of the SPH framework, performing neighborhood particle search, and completing model initialization.​​​​​

[0054] Step S300 comprises:

[0055] Step S310, calculate all model micro unit density, pressure;

[0056] Step S320, using the above mud flow surface SPH particle adaptive processing method processing mud flow free interface;

[0057] Step S330, calculate model micro unit force except gravity;

[0058] Step S340, calculate all model micro unit acceleration;

[0059] Step S350, update all model micro unit state;

[0060] Step S400, collision detection and processing;

[0061] Step S500, detect all model micro unit whether beyond the acceleration structure range, if not, enter the next time step calculation, otherwise end simulation calculation.

[0062] The application also provides an optimized technical scheme of the above-mentioned mud flow real-time simulation method.

[0063] Optimization I, optimization of the mud flow numerical model based on the SPH method.

[0064] The SPH-DEM coupling method is used to construct the mud flow numerical model based on the SPH method, wherein the SPH method is used to simulate the continuous fluid phase part in the mud flow, and the DEM method is used to simulate the discrete solid phase part in the mud flow.

[0065] In the coupling model, the mud flow body is simulated by SPH particles and DEM particles, so the model micro units registered in the unified SPH framework are SPH particles or DEM particles.

[0066] The optimization scheme utilizes the SPH-DEM coupling method to consider the contact force between the components of the debris flow in the debris flow model, and the mutual action between the components is realized through the hydrodynamic force and the contact force. By registering the heterogeneous model micro-units of the SPH particles and the DEM particles to the global SPH framework, a calculation platform for the bidirectional coupling force is obtained, and the mechanical implementation problem of the bidirectional coupling model is solved by using the SPH method to calculate the hydrodynamic force and the DEM method to calculate the contact force. In step S330, when the adaptive processing method for the SPH particles on the surface of the debris flow is implemented to process the free interface of the debris flow, the fluid particles in the global SPH framework (i.e., all the SPH particles of the model micro-units) are taken as the object particles i , and it is judged whether each object particle belongs to the surface particles. i The SPH particles and the DEM particles in the support domain are jointly marked as the neighborhood particles j of the object particles, that is, the number N of the object particles and the number of the neighborhood particles of the object particles are both contained in the support domain.

[0067] Secondly, the optimization design of the contact force calculation between the model micro-units is optimized.

[0068] In the step of calculating the contact force between the model micro-units by using the DEM method, the calculation idea of regarding the two heterogeneous model micro-units as isomorphic members is provided. The specific scheme is as follows: when the contact force between the DEM particles is calculated, the conventional DEM method is used to calculate the contact force between the DEM particles; when the contact force between the SPH particles is calculated, for the target SPH particle, the candidate SPH particles for the overlap calculation are all the SPH particles in the support domain of the SPH model, and then the conventional DEM method is used according to the properties of the SPH particles; when the contact force of the SPH particle on the DEM particle is calculated, the position coordinates of the target SPH particle in the DEM model space are calculated first, and the DEM particles within the search radius of the position coordinates are marked as the candidate DEM particles for the overlap calculation of the target SPH particle, and then the conventional DEM method is used according to the properties of the SPH particles and the DEM particles; when the contact force of the DEM particle on the SPH particle is calculated, the position coordinates of the target DEM particle in the SPH model space are calculated first, and all the SPH particles within the support domain of the SPH model are marked as the candidate SPH particles for the overlap calculation of the target DEM particle, and then the conventional DEM method is used according to the properties of the DEM particles and the SPH particles.

[0069] Thirdly, it is implemented in the Niagara particle system of the Unreal Engine (UE).

[0070] Since the real-time simulation method of debris flow adopts the global SPH framework with the global particle attribute, it is combined with the Niagara particle system of Unreal Engine to realize the simulation of SPH fluid (independent SPH fluid or SPH-DEM coupled fluid) by using the Niagara system, and a debris flow visualization simulation framework integrating numerical simulation and real-time visualization is obtained. The framework has high efficiency and expandability, and is an ideal digital twin platform that can support data-driven debris flow monitoring and early warning schemes.

[0071] Compared with the prior art, the beneficial effects of the present application are: (1) The prior art for the treatment of the free surface of debris flow only relies on the basic technical setting of the SPH method, and adopts the comparison method based on the number of particles around the fluid particles to identify the free surface particles. These methods are not strong in adaptability to the typical debris flow free surface movement characteristics, and are prone to misjudgment. The present application provides a self-adaptive treatment method for the surface SPH particles of debris flow to solve two levels of technical problems. The first level is the problem of self-adaptive identification of debris flow free surface particles. The method adopts a dynamic threshold of particle density as the identification standard of surface particles, and the dynamic threshold is determined by the average density of the neighborhood particles in the current time step, so as to link the identification of surface particles with the dynamic fluid characteristics of debris flow, overcoming the defects of fixed numerical comparison in the prior art. In the optimization scheme, the method constructs a dynamic threshold model of particle density expressed by real-time velocity gradient and real-time density gradient, and based on the analysis of the previous experimental data, the method uses a two-weight coefficient to realize the coordinated dynamic balance of the gradients of multiple physical fields to control the contribution size of the characteristics of debris flow to the surface judgment, and to improve the stability and accuracy of the surface particle capture. The second level is the self-adaptive dynamic compensation problem of the surface SPH particles after identification. For the identified free surface particles, the method generates a neighborhood virtual particle to support the kernel calculation of the compensation properties around the center of mass at random, and after the calculation of the resultant force is completed, the system automatically removes the calculation neighborhood virtual particles. The optimization scheme also specifically designs the density attribute and velocity attribute of the neighborhood virtual particles. (2) The real-time simulation method of debris flow provided by the present application realizes the real-time simulation technology of debris flow by using the self-adaptive treatment method for the surface SPH particles of debris flow. In the optimization scheme, the dynamic simulation method of debris flow based on the SPH-DEM coupling method is adopted, the unified SPH framework composed of SPH particles and DEM particle heterogeneous models is used as the calculation platform for bidirectional coupling, and the mechanical implementation problem of bidirectional coupling model is solved by using the SPH method to calculate the water power and the DEM method to calculate the contact force. In the coupling framework, the surface SPH particles are adaptively processed for the fluid part, and the solid particles are collectively identified as the neighborhood particles of the fluid particles. In the further optimization scheme, for different types of contact forces between the SPH particles and the DEM particles, methods for searching for contact force calculation candidate micro units in the SPH support domain or in the DEM search radius are designed, and the coupling effect of mechanical calculation in the unified SPH framework is optimized. The method improves the technical value of the surface adaptive processing method of the present application applied to the debris flow simulation model. (3) The method for realizing the SPH fluid (independent SPH fluid or SPH-DEM coupled fluid) by using the Niagara particle system based on the Unreal Engine is provided, the real-time simulation scheme of numerical simulation and visualization integration is realized, the function expansion is easy to realize, and it is an ideal digital twin platform for debris flow monitoring and early warning in the field of data-driven debris flow. BRIEF DESCRIPTION OF DRAWINGS

[0072] Figure 1is a schematic diagram of main steps of a self-adaptive treatment process of SPH particles on a surface of a debris flow.

[0073] Figure 2 is a free surface and abnormal flow state of a SPH model of a debris flow.

[0074] Figure 3 is a local surface particle determination example of a SPH model of a debris flow, (a) shows a fixed threshold method, and (b) shows a self-adaptive dynamic threshold method.

[0075] Figure 4 is a surface particle determination example of a SPH model of a debris flow in a debris flow overturning area, (a) shows a fixed threshold method, and (b) shows a self-adaptive dynamic threshold method.

[0076] Figure 5 is a surface particle compensation effect diagram of a SPH model of a debris flow.

[0077] Figure 6 is a simulation dam-break experimental device diagram, (a) is a lateral view of a device in the example, (b) is a front view of the device in the example, (c) is a lateral view of a device in document 1, and (d) is a front view of the device in document 1.

[0078] Figure 7 is a schematic diagram of a SPH-DEM coupling mode.

[0079] Figure 8 is a schematic diagram of main implementation steps of a SPH-DEM coupling numerical simulation of a debris flow.

[0080] Figure 9 is a diagram of overall flow states of a debris flow at four observation moments in a first simulation dam-break test of an experimental group.

[0081] Figure 10 is a comparison diagram of front displacement of a debris flow body between a simulation dam-break and a physical dam-break experimental snapshot. DETAILED DESCRIPTION

[0082] The preferred embodiments of the present application are further described below with reference to the accompanying drawings.

[0083] Figure 1 is a schematic diagram of main steps of a self-adaptive treatment process of SPH particles on a surface of a debris flow.

[0084] Embodiment One

[0085] The self-adaptive treatment of the surface SPH particles in the dynamic simulation model of the debris flow is performed by using the method of the present application, and the dynamic self-adaptive determination of the particle type is performed.

[0086] 1. Debris flow numerical model setting and initialization

[0087] A numerical model of debris flow is constructed using the smoothed particle hydrodynamics (SPH) method. The semi-implicit Euler method is used for time integration of SPH, i.e., the velocity is updated first, and then the particle position is calculated using the updated velocity. This method has the characteristics of simple calculation process, easy GPU parallel implementation, and good numerical stability under the condition of small time step. The physical parameters of the SPH model are comprehensively referenced from existing technologies and previous research experience, and the main parameters are shown in Table 1.

[0088] Table 1 Main setting parameters of the SPH model of debris flow

[0089]

[0090] Prior art document 1: Xiaosong Sun et al , Three-dimensional simulation of a solid-liquid flow by the DEM-SPH method. Journal of Computational Physics , 2013, 248: 147-176 (Chinese translation of the title of the paper: Three-dimensional simulation of a solid-liquid flow by the DEM-SPH method).

[0091] Prior art document 2: Chun Liu et al , Consideration of maximum impact force design for a rock shed against dry granular flow. European Journal of Environmental and Civil Engineering , 2022, 9: 1-22 (Chinese translation of the title of the paper: Consideration of maximum impact force design for a rock shed against dry granular flow).

[0092] Prior art document 3: Wang Yaru, Simulation of debris flow movement based on the SPH method, North China University of Water Resources and Electric Power, Dissertation, 2018.

[0093] The model initialization is completed.

[0094] In this example, the Niagara particle system of Unreal Engine is used to construct the debris flow simulation model, and parallel implementation on GPU is performed.

[0095] After the initialization of the SPH model, the steps of cyclic iteration calculation such as neighborhood search, density calculation, related force calculation, time integration, particle update, and the steps of boundary processing and output analysis (such as Figure 1 ) are performed according to the conventional numerical simulation process of the model. The content of the conventional steps is omitted in this example, and the related operation content of the surface SPH particle adaptive processing method of the present application is described in detail.

[0096] 2. Debris flow SPH particle type determination

[0097] In the iterative computation, at any time step, for any SPH particle i a neighborhood search is performed to establish its neighborhood particle set. With the neighborhood particle properties, the following computations are performed.

[0098] Its particle density is computed according to the model of equation 3 p local i , t ).

[0099] If the particle density p local i , t ) is less than the particle density dynamic threshold p thd i , t , the particle is determined as a debris flow surface SPH particle, otherwise it is determined as a debris flow internal SPH particle.

[0100] As a preferred embodiment, the determination of p thd i , t ) in this example is performed using an optimization scheme, i.e. using the fine dynamic model expressed in equation 2.

[0101] To obtain the non-dimensional particle velocity gradient normalized value and the non-dimensional particle density gradient normalized value, the particle density gradient and the velocity gradient are first computed respectively.

[0102] Equation 7

[0103] Equation 8

[0104] Taking particle t 1, i 2 in the computation of time step i = 0.2 s as an example, the particle type adaptive determination related data are shown in Table 2.

[0105] Table 2. Particle type adaptive determination example

[0106]

[0107] ​​​​Extracting a part of the simulation domain of the debris flow SPH model as an example. The particle emission source is set in the central region of the scene container. The particles are initially concentrated in the center, and then spread outward under the action of the gradient force. When encountering the container boundary, they bounce back, form a turbulent flow, and produce a backflow, eventually causing the particles in the central region to re-aggregate, resulting in a significant increase in local density.

[0108] Figure 2 is the free surface and abnormal flow state of the debris flow SPH model. Figure 2 As shown, due to the lack of particle density on the free surface, the bottom particles surge upward, and the central particles spread outward under the action of the gradient force. The boxed part in the figure shows the part of the debris flow in an abnormal flow state.

[0109] Figure 3 is a local surface particle determination example of the debris flow SPH model, (a) shows the existing fixed threshold method, and (b) shows the adaptive dynamic threshold method of the present application. Figure 3 In the figure, the pink particles are the surface SPH particles determined by the method, and the white particles are the internal SPH particles. Figure 3 In (a), the determination result of the existing technology fixed threshold method is shown (for the specific operation of the existing technology fixed threshold, refer to formula (3-21) in the existing technology document 4: p i <α p 0, p i is any i particle density, p 0 is the initial density, and α is a fixed threshold coefficient generally taken as 0.8-0.96. In this example, the fixed threshold coefficient is set to 0.95), and it is shown that the fixed threshold method can effectively determine the surface particles in the diffusion stage, but when the central particles aggregate, the real surface particles are not mistaken for internal SPH particles due to the fact that the density does not fall below the fixed threshold. The determination result of the adaptive dynamic threshold method of the present application Figure 3 In (b), it is shown that the use of a local dynamic adaptive threshold based on the real-time density gradient and velocity gradient effectively improves the accurate determination of the surface particles in the particle aggregation area and avoids simulation distortion.

[0110] Existing technology document 4: Zheng Xiaogang, Numerical simulation of sediment transport process based on Lagrangian method, Sichuan University, doctoral thesis, 2021.

[0111] Figure 4 is a debris flow SPH model debris flow turbulent flow area surface particle determination example, (a) shows the existing fixed threshold method, and (b) shows the adaptive dynamic threshold method of the present application. Figure 4 In the figure, the pink particles and white particles have the same meaning as Figure 3The same. In the model, when the particles hit the container boundary to produce the turbulence, the middle turbulence area is prone to particle aggregation, in which case: Figure 4 Fig. 6 (a) shows that due to the detection of the surface particle density of the debris flow not significantly decreased, the prior art cannot accurately identify it as the surface particle; Figure 4 Fig. 6 (b) shows that even if the surface particle density is still high, the threshold dynamic balance control method of the present application can still accurately determine it as the surface particle.

[0112] Example Two

[0113] The adaptive processing of the surface SPH particle in the debris flow dynamic simulation model is carried out by using the method of the present application. Specifically, on the basis of example one, the adaptive dynamic compensation of the surface SPH particle is carried out. The same as example one is not repeated, and the description of the content of the conventional steps of the SPH model is omitted, and the related operation content of the adaptive processing method of the surface SPH particle of the present application is described.

[0114] 1. Constructing type particle set

[0115] In any time step, all particles of the SPH model are traversed, and the type particle set is constructed according to the particle determination result of example one, including the surface SPH particle set T sur , the internal SPH particle set T int .

[0116] 2. Randomly generating neighborhood virtual particles

[0117] In any time step, the following is performed:

[0118] Calculate the average value of the number of neighborhood particles of all particles in the set T int . n int .

[0119] Formula 9

[0120] For any particle in the set T sur , calculate the number of neighborhood particles thereof n real , the difference between n int and n real is recorded as the number of neighborhood virtual particles needed to be supplemented in the support domain n need ; calculate the centroid coordinates of the support domain (Formula 4), and randomly generate a number of nneed Virtual particle support kernel function calculation; neighborhood virtual particle properties satisfy density p tem (Equation 5) Speed (Formula 6).

[0121] Once the resultant force calculation is complete, the system clears the neighboring virtual particles.

[0122] In time step t Taking a calculation of 0.2s as an example, the SPH global total has 3928 particles, and the set T sur With sets T int The number of particles are 236 and 3692 respectively. For the set... T int , n int =21.3. (Based on sets) T sur SPH particles on any surface i Taking 3 as an example, its neighborhood particle number n real =14, and a number of random additions need to be made around its centroid coordinates. n need =21.3-14=7.3≈7 (rounded) The neighborhood virtual particles are used as the supporting kernel function for calculation. i The attribute data of the 14 neighboring particles of 3 are shown in Table 3.

[0123] Table 3 Set T sur Neutron i 3's 14 neighborhood particle attribute data

[0124]

[0125] Utilizing neighboring particles j Attribute data interpolation calculation determined, particles i 3. Centroid coordinates ≈(4.970, 4.894, 4.975), the properties of randomly generated neighborhood virtual particles around the centroid should satisfy density. p tem =1497.9kg / m 3 ,speed = (2.01, 2.01, 2.02) m / s.

[0126] Figure 5 This is a diagram showing the surface particle compensation effect of the debris flow SPH model. Figure 5 and Figure 2The comparison shows that, after compensating for the deletion of surface SPH particles, the fluid avoids unstable phenomena such as the center particles rushing to the periphery and the bottom particles rushing to the top, and the system can effectively simulate the stable state of the fluid.

[0127] Embodiment three

[0128] In the Niagara particle system of the Unreal Engine, a debris flow simulation model is built to simulate the completion process of the debris flow dam break, and two tasks are completed: 1. The adaptive processing method of the debris flow surface SPH particles is applied to the complete motion process simulation model of the debris flow, and the effect of the application scheme is verified through experimental data; 2. In the Niagara particle system, real-time rendering is superimposed on the numerical simulation model of the debris flow to realize real-time visual effects in the complete motion of the debris flow.

[0129] 1. Simulation test system

[0130] 1.1 Simulation dam break experimental device

[0131] The prior art document 1 discloses a solid-liquid two-phase debris flow dam break experimental device built in a real environment, and uses test data to verify the effectiveness of the constructed debris flow numerical model. In this example, the test research system (hardware and test process) of document 1 is reproduced in a virtual environment using the Unreal Engine, and a debris flow dam break experimental virtual test system (simulated dam break system for short) is obtained. Then the same test in document 1 is carried out in the simulated dam break system, and the simulation test result data is compared with the experimental data to verify the accuracy and reliability of the simulated dam break system.

[0132] According to the disclosure content of the prior art document 1, a simulated dam break experimental device is built in the Niagara virtual system. Figure 6 Figure is a simulated dam break experimental device, (a) is a lateral view of the device built in this example, (b) is a front view of the device built in this example, (c) is a lateral view of the device in document 1, and (d) is a front view of the device in document 1. The device in document 1 is set as a unit of a = 5 cm, and the complete domain specification is 4a x 3a x 2a. The simulated dam break experimental device built in this example maps this specification to the grid number as the proportional unit of the virtual space.

[0133] 1.2 Debris flow SPH-DEM numerical model setting

[0134] As a preferred embodiment, a smooth particle hydrodynamics method (SPH) and a discrete element method (DEM) are coupled to construct a debris flow SPH-DEM coupled numerical model. The SPH method simulates the continuous fluid phase part in the debris flow, representing the flow and wave motion of the fluid. The DEM method simulates the discrete solid phase part in the debris flow, which is suitable for block stones, sand and gravel in the debris flow, which have obvious particle characteristics and can move independently.

[0135] Figure 7 is a schematic diagram of the SPH-DEM coupling mode. In the left figure, the curved arrow shows the connection between the fluid phase part, and the solid phase part is shown by the solid circle. The coupling calculation of SPH and DEM is realized through the bidirectional mechanical interaction between the continuous fluid phase and the solid phase. The DEM particles are introduced into the SPH particle system to become a unified SPH framework system composed of heterogeneous model micro-units (DEM particles and SPH particles). The SPH method is used to calculate the hydrodynamic force between the model micro-units, and the DEM method is used to calculate the contact force between the model micro-units. That is, the SPH particles and the DEM particles jointly participate in the calculation of the hydrodynamic force (gradient force and viscous force) through the SPH kernel function, and also jointly participate in the calculation of the contact force through the DEM method.

[0136] For the calculation of the contact force between the model micro-units, the following distinction can be made according to the different properties of the contact force.

[0137] (1) Calculation of the contact force between solid particles f SScol When the condition is met, the conventional DEM method is used to calculate the contact force between the DEM particles.

[0138] (2) Calculation of the contact force between fluid particles f FFcol When the condition is met, for the target SPH particle, the candidate SPH particles for calculating the overlap amount are all the SPH particles within the SPH model support domain of the SPH particle, and the conventional DEM method is used to calculate according to the properties of the SPH particles.

[0139] (3) Calculation of the contact force on the fluid particles by the solid particles f SFcol When the condition is met, first, the position coordinates of the target SPH particle in the DEM model space are calculated, and the DEM particles within the search radius of the position coordinates are marked as candidate DEM particles for calculating the overlap amount of the target SPH particle, and then the conventional DEM method is used to calculate according to the properties of the SPH particles and the DEM particles.

[0140] (4) Calculation of the contact force on the solid particles by the fluid particles f FScol When the condition is met, first, the position coordinates of the target DEM particle in the SPH model space are calculated, and all the SPH particles within the SPH model support domain of the position coordinates are marked as candidate SPH particles for calculating the overlap amount of the target DEM particle, and then the conventional DEM method is used to calculate according to the properties of the DEM particles and the SPH particles.

[0141] Considering the mechanical properties between the components of the debris flow material, in this embodiment, the contact force of the fluid particles on the solid particles is not considered. The force calculation of the model micro-units is as follows: F fThe resultant force on a fluid particle (SPH particle) includes gradient forces between fluid particles f FFpre , viscous forces f FFvis , contact forces f FFcol , and gradient forces from solid particles f SFpre , viscous forces f SFvis , contact forces f SFcol (Formula 10) F s The resultant force on a solid particle (DEM particle) includes gradient forces from the continuous fluid f FSpre , viscous forces f FSvis , and contact forces between solid particles f SScol (Formula 11)

[0142] Formula 10

[0143] Formula 11

[0144] The contact force includes a normal contact force F n and a tangential contact force F t , whose magnitudes F n |, F t | are calculated by Formula 12 and Formula 13 respectively, where d , k , e , v respectively represent the contact overlap, the stiffness coefficient, the damping coefficient, and the relative velocity, and subscripts n and t respectively represent the normal and the tangential, m is the tangential friction coefficient.

[0145] Formula 12

[0146] Formula 13

[0147] The physical parameters of the SPH-DEM model are comprehensively referenced from the prior art and previous research experience. The SPH model parameters are set the same as in Embodiment One, and the DEM model adopts a spring-damping model. The main parameter settings of the model are shown in Table 4.

[0148] Table 4 Main setting parameters of debris flow SPH-DEM model

[0149]

[0150] 2. Debris flow SPH-DEM coupling numerical implementation

[0151] Figure 8 is a schematic diagram of the main implementation steps of debris flow SPH-DEM coupling numerical simulation.

[0152] Introduce DEM particles into the SPH particle system, complete the initialization of the SPH-DEM model, start the time step loop, and perform numerical simulation operations. The following omits the description of the conventional steps of the SPH-DEM coupling model simulation, and only describes the main steps. Since the DEM particles are introduced into the SPH particle system, the two types of model micro-units participate in numerical operations with the same identity, so in the following description, the three types of calculation objects are referred to as SPH particles, DEM particles, and model micro-units.

[0153] For ease of description, the main steps of coupling numerical implementation are represented as four stages:

[0154] Stage I: Model definition

[0155] Define the global parameters of the model such as the calculation domain, smoothing length, time step, etc.

[0156] Create all model micro-units and set basic attributes according to the calculation needs;

[0157] Register all model micro-units in the unified SPH framework.

[0158] Stage II: Model initialization

[0159] Build the global acceleration structure of the SPH framework: build the acceleration structure based on the current positions of all model micro-units. To integrate the SPH method into the Niagara particle system, a spatial grid type global acceleration structure is used. In this example, a spatial hash grid is selected;

[0160] Perform neighborhood search on all model micro-units to complete model initialization.

[0161] Stage III: Mechanical and particle property calculation

[0162] Calculate the density and pressure of the model micro-units;

[0163] For the debris flow fluid part (i.e., the object particle is any SPH particle), use the method of embodiment two to adaptively identify the debris flow free interface particle and perform density compensation. In the calculation, the SPH particles and DEM particles within the support domain are collectively marked as the neighborhood particles of the object SPH particle i .j i.e. the number of neighboring particles N comprising supporting the SPH particles and DEM particles within the domain;

[0164] calculating the resultant force of the model microelement except gravity F f with F s comprising: calculating the gradient force and viscous force between the model microelements by the SPH method, and calculating the normal contact force and tangential contact force between the model microelements by the DEM method;

[0165] calculating the acceleration of the model microelement;

[0166] updating the tangential component of the local coordinate system and calculating the angular acceleration of the DEM particle according to the tangential contact force between the DEM particles;

[0167] updating the state of the model microelement, comprising: updating the velocity and position of the SPH particle, and updating the velocity, position and rotation of the DEM particle;

[0168] collision detection and processing: if the updated position of the model microelement is within the content of the scene object, it is confirmed that the collision occurs in this time step and needs to be processed. In this case, the velocity of the model microelement after the collision is corrected by introducing a restitution coefficient, and the restitution coefficient is set to 0.35-0.45 according to the previous research results, preferably 0.4. That is, the magnitude of the velocity of the colliding particle is changed to 40% of the original velocity, and the direction is opposite, while the horizontal velocity remains unchanged.

[0169] Stage IV: simulation domain detection

[0170] detecting whether the model microelement exceeds the range of the acceleration structure, if not, entering the next time step calculation, otherwise ending the numerical simulation calculation.

[0171] 3. Simulation dam break test and results

[0172] Setting up experimental groups and control groups. The experimental groups are subjected to adaptive processing of surface SPH particles (including identification and compensation) according to the method of Example Two, and the control groups are not subjected to any special processing of surface SPH particles. The following omits the description of the contents of the regular steps of the SPH-DEM coupled model simulation, as well as the description of the same contents in Examples One and Two.

[0173] The experimental groups and the control groups respectively carried out 3 times of simulation dam break tests, and recorded the displacement values of the debris flow front from t=0.0s to t=0.2s at 12 observation times (i.e. 12 observation values) each time.

[0174] Figure 9Fig. 1 is the flow state diagram of the debris flow in the experimental group at four observation times in the first simulation dam-break test, namely t = 0.05s, t = 0.10s, t = 0.15s, and t = 0.20s. The white and blue particles in the figure are SPH particles and DEM particles, respectively.

[0175] The average value of each observation value in the test result data of the experimental group and the control group was calculated respectively, and the result data and average value of the experimental group and the control group were compared with the physical dam-break test snapshot in the prior art document 1. The results are shown in Figure 10 and Table 5.

[0176] Figure 10 Fig. 2 is a comparison diagram of the displacement of the debris flow front in the experimental group simulation dam-break and physical dam-break snapshot. The curve name in the figure means: SR1-ADC, SR2-ADC, SR3-ADC, ASR-ADC, which are the first, second, third simulation observation values and their average values of the experimental group after density compensation, respectively; SR1-BDC, SR2-BDC, SR3-BDC, ASR-BDC, which are the first, second, third simulation observation values and their average values of the experimental group before density compensation, respectively; and the experimental snapshot is the data of the control group. Figure 10 The data is shown in Table 5.

[0177] Table 5 Debris flow front displacement data of experimental group simulation dam-break and physical dam-break snapshot

[0178]

[0179] Taking the average value of the result data of the experimental group and the control group as the benchmark, the mean absolute percentage error (MAPE) index was used to evaluate the simulation error. MAPE is calculated according to formula 14, wherein n is the total number of samples, E i is the first observation value of the experimental snapshot, i S i is the average value of the first observation value. i

[0180] Formula 14

[0181] MAPE evaluation shows that the MAPE of the control group is 12.3507%, and the MAPE of the experimental group is 8.552%.

[0182] Figure 10 ​​And the data in Table 5 and the MAPE result show that: (1) the adaptive treatment method of surface SPH particles of the debris flow dynamic simulation model can be well applied to the numerical model of debris flow based on the SPH method, and support the dynamic simulation of the whole process of the dam-break movement of the debris flow; (2) in the numerical model of debris flow based on the SPH method, by adopting the adaptive treatment method of surface SPH particles of the debris flow dynamic simulation model, the simulation of the front displacement of the dam-break debris flow at each time node is more consistent with the overall snapshot of the physical dam-break experiment. Compared with the prior art, the adaptive method can improve the numerical simulation accuracy and system stability.

[0183] The above specific embodiments of the present application realize the scheme of SPH fluid (independent SPH fluid or SPH-DEM coupled fluid) by adopting the Niagara particle system based on the Unreal Engine, realize the real-time simulation of the debris flow integrated with numerical simulation and visualization. The technical framework is easy to expand the front-end real-time monitoring data access function and the overall closed-loop control function, and is connected with the current data real-time processing, simulation analysis and real-time visual presentation, and is an ideal debris flow digital twin platform in the field of data-driven debris flow monitoring and early warning.

Claims

1. An adaptive treatment method for SPH particles on the surface of debris flows, characterized in that: The parameters of the SPH model for debris flow were set using the smoothed particle hydrodynamics method, and the model was initialized. For any SPH particle i If the particle density is satisfied ρ local ( i , t Particle density dynamic threshold ρ thd ( i , t ), determine the particle i If the particles are SPH particles on the surface of the debris flow, then they are determined to be SPH particles inside the debris flow. The particle density dynamic threshold ρ thd ( i , t Expressed according to Equations 2 and 3, Formula 2 Formula 3 In the formula, ρ 0 – Basic density threshold -particle i Real-time density gradient, -particle i Real-time velocity gradient, - Dimensionless particle velocity gradient normalized value, ranging from 0 to 1. - Dimensionless normalized particle density gradient, ranging from 0 to 1. λ v and λ ρ These are the velocity gradient weighting coefficient and the density gradient weighting coefficient, respectively. N -particle i The number of neighboring particles, ρ local ( i , t )-particle i density, h - Smooth kernel radius, m j -particle i Neighborhood particles j quality -particle i With particles j The position vector difference.

2. The adaptive treatment method for SPH particles on the debris flow surface according to claim 1, characterized in that: The ρ thd ( i , t According to the model in Equation 1, Formula 1.

3. The adaptive treatment method for SPH particles on the debris flow surface according to claim 1 or 2, characterized in that: At any time step of the loop iteration, execute: Iterate through all particles in the SPH model, identify SPH particles on the debris flow surface and SPH particles inside the debris flow, and construct separate sets of surface SPH particles. T sur Internal SPH particle collection T int ; Compute set T int The average number of neighboring particles of all particles n int ; For sets T sur For any particle, calculate the number of its neighborhood particles. n real ,Will n int and n real The difference is denoted as the number of neighborhood virtual particles that need to be added within its support domain. n need Calculate the centroid coordinates of the support domain. Randomly generate a number around the centroid. n need The virtual particle supporting kernel function is calculated, and the neighborhood virtual particle properties satisfy the density... ρ tem With speed , Formula 4 Formula 5 Formula 6 In the formula, N ( i )-particle i The set of neighborhood particles, -particle j The position vector, ρ local ( j )-particle j density, W ( r ij ,h - Smoothing kernel function, -particle j The velocity vector; Once the resultant force calculation is complete, the system clears the neighboring virtual particles.

4. The adaptive treatment method for SPH particles on the debris flow surface according to claim 3, characterized in that: It is applied to debris flow simulation in the numerical model of debris flow based on the SPH method.

5. A real-time simulation method for debris flow, characterized in that: A debris flow numerical model based on the SPH method is constructed, wherein the debris flow numerical model based on the SPH method is a debris flow model constructed using the SPH method, and then implemented as follows: Step S100: Define global parameters for the model, create all model micro-units and set basic properties according to computational needs, and register all model micro-units into a unified SPH framework. The model micro-units are SPH particles. Step S200: Construct the global acceleration structure of the SPH framework, perform neighborhood particle search, and complete model initialization; Step S300 includes: Step S310: Calculate the density and pressure of all model micro-units; Step S320: The free interface of the debris flow is treated using the SPH particle adaptive treatment method for debris flow surface as described in claim 3. Step S330: Calculate the resultant force of the model micro-element excluding gravity; Step S340: Calculate the acceleration of all model micro-elements; Step S350: Update the state of all model micro-units; Step S400: Perform collision detection and processing; Step S500: Check whether all model micro-units exceed the range of the acceleration structure. If they do not exceed the range, proceed to the next time step for calculation; otherwise, end the simulation calculation.

6. The real-time simulation method for debris flow according to claim 5, characterized in that: The debris flow numerical model based on the SPH method is a debris flow model constructed using the SPH-DEM coupling method, wherein the SPH method simulates the continuous fluid phase part of the debris flow, and the DEM method simulates the discrete solid phase part of the debris flow. In step S100, the model micro-unit is an SPH particle or a DEM particle. In step S320, debris flow free interface processing calculation is performed with any SPH particle as the object particle. In the calculation, SPH particles in the support domain of the SPH particle and DEM particles are jointly marked as their neighboring particles. In step S330, the gradient force and viscous force between model micro-units are calculated using the SPH method, and the contact force between model micro-units is calculated using the DEM method to obtain the resultant force of the model micro-units excluding gravity. In step S340, calculating the acceleration of all model micro-units includes: updating the tangential components of the local coordinate system and calculating the angular acceleration based on the tangential contact force between DEM particles; In step S350, updating the state of all model micro-units includes: updating the velocity and position of SPH particles, and updating the velocity, position, and rotation of DEM particles.

7. The real-time debris flow simulation method according to claim 6, characterized in that: Step S330, in the calculation of the contact force between micro-elements of the model using the DEM method, When calculating the interparticle contact force in DEM, the conventional DEM method is used. When calculating the contact force between SPH particles, for the target SPH particle, the candidate SPH particles for calculating the overlap amount are all SPH particles in the SPH model support domain of the SPH body particle, and then the conventional DEM method is used to calculate based on the SPH particle properties. When calculating the contact force between SPH particles and DEM particles, first calculate the position coordinates of the target SPH particle in the DEM model space, and record the DEM particles whose position coordinates are within the search radius of the DEM model as candidate DEM particles for calculating the overlap of the target SPH particles. Then, calculate the force using the conventional DEM method based on the properties of SPH particles and DEM particles. When calculating the contact force of SPH particles on DEM particles, the position coordinates of the target DEM particles in the SPH model space are first calculated, and all SPH particles in the SPH model support domain with these position coordinates are marked as candidate SPH particles for calculating the overlap of the target DEM particles. Then, the conventional DEM method is used to calculate the overlap based on the properties of DEM particles and SPH particles.

8. The real-time simulation method for debris flow according to claim 7, characterized in that: Step S330, the resultant forces of the model micro-units other than gravity are: The net forces acting on SPH particles, besides gravity, include gradient forces, viscous forces, and contact forces between SPH particles, as well as gradient forces, viscous forces, and contact forces from DEM particles. The combined forces acting on DEM particles, besides gravity, include gradient forces from the fluid, viscous forces, and contact forces between DEM particles.

9. The real-time simulation method for debris flow according to any one of claims 5 to 8, characterized in that: Implemented within the Niagara particle system of Unreal Engine, the global acceleration structure is a spatial grid-type global acceleration structure.

Images