GPU-based parallel acceleration of sediment particle tracking, tracing methods and devices

Abstract

GPU parallel acceleration based on sediment particle tracking, tracing method and equipment, belong to the field of ocean hydrodynamic simulation and Lagrangian particle tracking, in order to overcome the problem that the prior art is difficult to accurately simulate the motion trajectory of sediment in complex water flow, wave, tide and other multi-element dynamic field, the particle is arranged in the calculation domain of two-dimensional hydrodynamic mathematical model, the input information of arranging the particle, including the position coordinates of the particle, the state of the particle in the next time step is judged according to the probability of deposition and siltation, the state includes the starting state and the deposition state, the state of the particle in the next time step is judged according to the starting probability, the state includes the starting state and the deposition state, the present application is fast and accurate analysis of the source and transport path of sediment, which provides solid guarantee for scientific management of sediment accumulation and sustainable development of coastal zone.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of ocean hydrodynamic simulation and Lagrangian particle tracking, and is a particle tracking and tracing method based on GPU (Graphics Processing Unit) parallel acceleration. BACKGROUND

[0002] With the development of economy and population growth, human activities have a great impact on river, ocean and other water environments. The facilities such as beaches, ports and reservoirs are often troubled by sediment deposition. Sediment deposition will reduce the area of the beach. When the source of sediment changes, the beach becomes rough or has an odor, which greatly reduces the tourism value of the beach. For the port, a large amount of sediment deposition in the channel reduces the water depth for navigation, and large ships are restricted to enter and exit the port, which has a profound impact on regional economic exchanges and international trade.

[0003] Traditional sediment tracking often uses isotope tracing and Lagrangian algorithm. However, these methods have many shortcomings. The isotope tracing method can trace the source of sediment to some extent, but the cost is high, and the scope of application is limited. In some complex water environments, its accuracy will be affected. The Lagrangian method mainly focuses on the trajectory of sediment particles, but in practical application, due to the complexity of water flow and the diversity of sediment movement mechanism, it is difficult to accurately simulate the motion state of sediment in complex water flow, wave, tide and other multi-element dynamic field. The simulation effect of large-scale and long-distance sediment transport is not good. These traditional methods often cannot provide comprehensive, accurate and timely sediment tracking and tracing information, and are difficult to meet the needs of practical engineering and environmental management. SUMMARY

[0004] In order to overcome the problem that the prior art is difficult to accurately simulate the motion trajectory of sediment in complex water flow, wave, tide and other multi-element dynamic field, and make it difficult to accurately track sediment, in a first aspect, according to the particle tracking method in some embodiments of the present application, the particles include sediment particles, comprising

[0005] S1. Arranging particles in the calculation domain of a two-dimensional hydrodynamic mathematical model, and inputting information of arranging particles, including position coordinates of particles;

[0006] S2. According to the flow field information of the grid element of the two-dimensional hydrodynamic mathematical model and the position coordinates of the particles, calculating the number of the grid element where the initial particle is located;

[0007] According to the flow field information of the grid element of the two-dimensional hydrodynamic mathematical model and the number of the grid element where the initial particle is located, calculating the flow velocity of the position where the initial particle is located at the current time step;

[0008] S3-1. Calculate the position coordinate of the particle in the next time step according to the flow velocity at the position of the particle in the current time step;

[0009] S3-2. Calculate the position coordinate of the particle in the next time step according to the flow velocity at the position of the particle in the current time step;

[0010] S3-2. Calculate the position coordinate of the particle in the next time step according to the flow velocity at the position of the particle in the current time step;

[0011] S4. Determine the state of the particle in the next time step according to the fall probability, the state including a start state and a fall state:

[0012] S3-2. Calculate the position coordinate of the particle in the next time step according to the flow velocity at the position of the particle in the current time step;

[0013] S3-2. Calculate the position coordinate of the particle in the next time step according to the flow velocity at the position of the particle in the current time step;

[0014] S4. Determine the state of the particle in the next time step according to the fall probability, the state including a start state and a fall state:

[0015] S4. Determine the state of the particle in the next time step according to the fall probability, the state including a start state and a fall state:

[0016] If the state is the fall state, execute step S5;

[0017] If the state is the start state, reach the next time step, the next time step being the current time step, execute step S3-1;

[0018] S5. Determine the state of the particle in the next time step according to the start probability, the state including a start state and a deposition state:

[0019] If the state is the start state, reach the next time step, the next time step being the current time step, execute step S3-1;

[0020] If the state is the deposition state, reach the next time step, the next time step being the current time step, execute step S3-2;

[0021] S4. Determine the state of the particle in the next time step according to the fall probability, the state including a start state and a fall state:

[0022] S4. Determine the state of the particle in the next time step according to the fall probability, the state including a start state and a fall state:

[0023] According to some embodiments of the particle tracking method of the present invention, steps S3-1 and S3-2 further include...

[0024] Determine if the current iteration count has reached the set value. If it has, end the method; otherwise, proceed to step S4.

[0025] According to some embodiments of the particle tracking method of the present invention, determining that the current iteration number has reached a set value includes determining that the current iteration number has reached the time step required to stop particle tracking.

[0026] According to the particle tracking method in some embodiments of the present invention, in step S4, the state of the particle in the next time step is determined based on the siltation probability, including generating a random number of 0 to 1 for the particle. If the random number is not greater than the siltation probability, the state of the particle in the next time step is the siltation state; if the random number is greater than the siltation probability, the state of the particle in the next time step is the starting state.

[0027] The probability of siltation is given by the following formula:

[0028]

[0029] In the formula, This indicates setting a probability threshold, where N represents the number of settlement events;

[0030] The number of settlement events N is given by the following formula:

[0031]

[0032] In the formula, This indicates water depth, measured in meters (m). This indicates the settling velocity, and the unit is m / s. This represents a time step, measured in seconds (s).

[0033] According to some embodiments of the particle tracking method of the present invention, in step S5, the state of the particle in the next time step is determined according to the activation probability, including generating a random number of 0 to 1 for the particle. If the random number is not greater than the activation probability, the state of the particle in the next time step is the activation state; if the random number is greater than the activation probability, the state of the particle in the next time step is the deposition state.

[0034] The starting probability is given by the following formula:

[0035]

[0036] In the formula, =1 / 7.5, =0.5, Indicates time, Shields number;

[0037] where Shields number is given by

[0038]

[0039] where is the bulk density of the sediment particles, is the clear water density, is the total water depth, is the sediment density, is the water body density, is the acceleration due to gravity, is the bed shear stress;

[0040] where bed shear stress = is given by

[0041]

[0042] where is the bed shear stress, is the bed shear stress in the x-direction, is the bed shear stress in the y-direction;

[0043] is the water body reference density, is the bed drag coefficient, is the average vertical flow velocity in the Cartesian coordinate system; is the average vertical flow velocity in the x-direction in the Cartesian coordinate system, is the average vertical flow velocity in the y-direction in the Cartesian coordinate system;

[0044] where bed drag coefficient is given by

[0045]

[0046] where is the Manning coefficient, is the depth of water below the zero water surface, and M is the roughness coefficient; is the Chezy coefficient.

[0047] According to the particle tracking method in some embodiments of the present invention, the position coordinates of the particle in the next time step are calculated based on the flow velocity at the particle's location in the current time step, as shown in the following formula:

[0048]

[0049] In the formula, Represents particles The position coordinates of a point in two-dimensional space at any given time. Represents particles The position coordinates of a point in two-dimensional space at any given time. This indicates the weighted correction of the Runge-Kutta method. Average vertical velocity in the direction, This indicates the weighted correction of the Runge-Kutta method. The average vertical velocity in the direction of flow. Represents a random number that follows a standard normal distribution. Indicates in Diffusion displacement component in the direction;

[0050] Among them, Diffusion displacement components in the direction As shown in the following formula:

[0051]

[0052] In the formula, The horizontal diffusion coefficient;

[0053] Among them, random numbers from the standard normal distribution As shown in the following formula:

[0054]

[0055] In the formula, Represents a uniformly distributed random number.

[0056] According to some embodiments of the particle tracking method of the present invention, the tracking is source tracing, and the position coordinates of the particle in the next time step are calculated based on the flow velocity at the current time step of the particle's location, as shown in the following formula:

[0057]

[0058] In the formula, Represents particles The position coordinates of a point in two-dimensional space at any given time. Represents particles The position coordinates of a point in two-dimensional space at any given time. denotes the average velocity in the x-direction in the counter flow field, denotes the average velocity in the y-direction in the counter flow field, denotes a random number of a standard normal distribution, denotes the diffusion displacement component in the direction;

[0059] wherein, and are given by

[0060]

[0061] wherein T is the total time length of the calculation, denotes the average vertical flow velocity in the x-direction after the weighted correction of the Runge-Kutta method, denotes the average vertical flow velocity in the y-direction after the weighted correction of the Runge-Kutta method, denotes the average vertical flow velocity in the x-direction after the weighted correction of the Runge-Kutta method, denotes the average vertical flow velocity in the y-direction after the weighted correction of the Runge-Kutta method;

[0062] wherein the diffusion displacement component in the direction is given by

[0063]

[0064] wherein, is the horizontal diffusion coefficient;

[0065] wherein the random number of a standard normal distribution is given by

[0066]

[0067] wherein, denotes a random number of a uniform distribution.

[0068] According to the particle tracking method in some embodiments of the present application, the method further comprises

[0069] S6. According to the number of the grid cell where the particle at the next time step is located, the number of the grid cell where the different particles at each next time step are located is obtained, and the particle average concentration of the first unit in the grid cell of the two-dimensional hydrodynamic mathematical model is calculated, and is given by

[0070]

[0071] wherein, denotes the particle average concentration of the first unit, denotes the particle average concentration of the first the number of particles in a cell; represents the mass represented by a single particle, represents the volume of the calculation unit.

[0072] According to the particle tracking method in some embodiments of the present application, the two-dimensional hydrodynamic mathematical model is the Reynolds-averaged Navier-Stokes equation of an incompressible fluid based on the static pressure assumption, which is in The calculation model control equation in the coordinate system includes:

[0073] Continuity equation:

[0074]

[0075] Momentum equation:

[0076]

[0077]

[0078] In the formula:

[0079] represents the water level; t represents time; represents the total water depth, , represents the water depth below the zero water surface;

[0080] represents the average vertical flow velocity in the direction of the Cartesian coordinate system, represents the average vertical flow velocity in the direction of the Cartesian coordinate system;

[0081] represents the coordinates of the Cartesian space, represents the coordinates of the Cartesian space in the direction, represents the coordinates of the Cartesian space in the direction, and z represents the coordinates of the Cartesian space in the direction;

[0082] represents the flux rate of evaporation; represents the flux rate of rainfall; represents the water body density; represents the flux rate of inflow;

[0083] represents the control body area; represents the Coriolis force coefficient; represents the gravitational acceleration; denotes the water body reference density;

[0084] denotes the vertical coordinate;

[0085] denotes the free surface shear stress, denotes the free surface shear stress in the direction, denotes the free surface shear stress in the direction;

[0086] denotes the bed bottom shear stress, denotes the bed bottom shear stress in the direction, denotes the bed bottom shear stress in the direction;

[0087] , denotes the drag term, denotes the drag term in the direction, denotes the drag term in the direction;

[0088] and denotes the horizontal viscous diffusion term, denotes the horizontal viscous diffusion term in the direction, denotes the horizontal viscous diffusion term in the direction.

[0089] In a second aspect, the embodiments of the present application also provide an electronic device, comprising: one or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions, when the instructions are executed by the electronic device, causing the electronic device to perform the first aspect and any possible technical solutions of the first aspect.

[0090] Advantages:

[0091] In the first aspect, based on the GPUOM two-dimensional hydrodynamic model, the Lagrangian particle tracking model is coupled with the sediment suspension-settling mechanism, the movement state of the sediment in the complex flow, wave, tide and other multi-element dynamic field is accurately simulated, the movement trajectory of the sediment in the complex flow, wave, tide and other multi-element dynamic field is accurately simulated, the rapid and accurate analysis of the sediment source and transport path is realized, and the scientific management of sediment accumulation and the sustainable development of coastal zone are provided with solid guarantee.

[0092] In the second aspect, the present application constructs a GPU-based parallel acceleration sediment particle tracking and tracing model, combines the Lagrangian particle tracking algorithm with the sediment suspension-settling mechanism, adds a random walk term, and dynamically analyzes the movement trajectory and source distribution of sediment under the action of complex water flow and tides. Through accurate quantification of sediment transport path and siltation risk area, the problems of traditional methods such as relying on empirical judgment, low calculation efficiency, and insufficient tracing accuracy are solved.

[0093] In the third aspect, based on particle concentration statistics and visualization technology, the present application can quickly lock the main source area of sediment, and generate a key monitoring range and a warning threshold. In engineering applications, the operation department can optimize the layout of sediment retention facilities in advance, dynamically adjust the dredging plan, and quickly start the emergency plan when abnormal sediment input is monitored, effectively avoiding sudden problems such as port channel siltation and water intake blockage, significantly reducing maintenance costs, and ensuring the safe and efficient operation of water conservancy facilities.

[0094] In the fourth aspect, the present application changes from passive response to active prevention and control, providing intelligent decision support for coastal zone management, water resource protection, and long-term operation and maintenance of projects. BRIEF DESCRIPTION OF DRAWINGS

[0095] Figure 1 is the sediment particle distribution map of step 1.

[0096] Figure 2 is the sediment particle distribution map of step 20.

[0097] Figure 3 is the sediment particle distribution map of step 40.

[0098] Figure 4 is the sediment particle concentration map of step 1.

[0099] Figure 5 is the sediment particle concentration map of step 20.

[0100] Figure 6 is the sediment particle concentration map of step 40.

[0101] Figure 7 is the sediment particle tracking and tracing flowchart. DETAILED DESCRIPTION

[0102] In order for those skilled in the art to better understand the technical solutions of the present application, the technical solutions of the present application will be further described below in combination with the drawings and examples, and the drawings will be referred to in the description of the present application. Figure 1 is the sediment particle tracking and tracing flowchart.

[0103] Step S10. Establish a two-dimensional hydrodynamic model of the engineering area. Use GPUs to accelerate the parallel computation of flow field information at the grid nodes in the two-dimensional hydrodynamic model and store it. The two-dimensional hydrodynamic model is based on the Reynolds-averaged Navier-Stokes (RANS) equations for incompressible fluids under the assumption of hydrostatic pressure. The governing equations of the computational model in the coordinate system include:

[0104] Continuity equation:

[0105]

[0106] Momentum equation:

[0107]

[0108]

[0109] in:

[0110] Indicates water level; Represents coordinates in Cartesian space. Represents Cartesian space Directional coordinates Represents Cartesian space The coordinates of the direction, z represents the Cartesian space. Coordinates of direction;

[0111] ( , ( ) represents the average vertical velocity in Cartesian coordinates. Represents the Cartesian coordinate system The average vertical velocity in the direction of flow. Represents the Cartesian coordinate system The average vertical velocity in the direction; , () represents the square of the average vertical velocity;

[0112] The water depth below zero surface; Total water depth; Represents coordinates in Cartesian space. Represents Cartesian space Directional coordinates Represents Cartesian space Coordinates of direction;

[0113] Represents the vertical coordinate, in Location ,exist Location ;

[0114] Evap represents the flux rate of evaporation; Rain represents the flux rate of rainfall; Dens represents the water body density; Inflow represents the flux rate of inflow; Area represents the control volume area; Cor represents the Coriolis force coefficient; g represents the gravitational acceleration; Dref represents the water body reference density;

[0115] Taufs represents the free surface shear stress, in the direction, in the direction;

[0116] Taub represents the bed bottom shear stress, in the direction, in the direction;

[0117] , R represents the resistance term, in the direction, in the direction; and H represents the horizontal viscous diffusion term, in the direction, in the direction.

[0118] where, and are obtained from:

[0119]

[0120]

[0121] where, is the horizontal turbulent viscosity coefficient, which can be given as a constant or using:

[0122]

[0123] wherein, is a constant parameter, is the area of the MCE (momentum control element). Obviously, The value of varies with the model spatial resolution and the horizontal velocity gradient, specifically, decreases with the decrease of the grid scale and the horizontal velocity gradient.

[0124] In an example, a calculation domain is defined as a flume with length L = 3.5 km, width B = 1.6 km, and initial water depth D = 10 m, the left side is an open boundary, and the other three sides are fixed boundaries, the initial water level of the whole domain is 0.0 m, and the flow velocity is 0 m / s. The M2 tidal wave with an amplitude of 1.0 m has a tidal period of 44712 s. Other calculation parameters: model dry-wet discrimination water depth = 10-3 m, and the total simulation duration is 864000 s.

[0125] Step S20, a model supporting random walk of sediment tracking is established, wherein the tracking includes forward tracking, reverse tracking, and tracing, and the tracking of the present application can include tracing in the absence of special instructions. The model of sediment tracking performs the following steps:

[0126] Step S21: particles are arranged in the calculation domain of the two-dimensional hydrodynamic mathematical model, initial positions of the particles, release time, internal model time, and output interval are determined, and in an example of the present application, the input file of arranging the particles includes six parameters, which are, in turn, the total number of particles, the external model time step length of introducing the particle tracking module, the external model time step length of stopping the particle tracking module, the output interval of the particle information, the diffusion coefficients of the particles in the X and Y directions, the starting position information of each particle, and the particle release time. Specifically, in the application example, the particle release mode is as follows: ten particles are released at the position of the horizontal coordinate X = 500 m and the vertical coordinate Y = 450 m, the particle tracking is from the first step to the end of the twenty-first step, the Manning number is 0.28, the model time step length is 30 s, the total time step number is 40, and the output interval is set to 30 s.

[0127] Step S22: the particle information in step S21 is copied from the host end to the device end display memory.

[0128] Step S23: the number of the grid cell where the initial particle is located is calculated according to the flow field information of the grid cell of the two-dimensional hydrodynamic mathematical model and the position coordinates of the particle.

[0129] Step S24: the flow velocity of the position where the particle at the current time step is located is calculated according to the flow field information of the grid cell of the two-dimensional hydrodynamic mathematical model and the number of the grid cell where the initial particle is located, wherein the flow velocity calculation can be obtained by using linear least square method space reconstruction calculation.

[0130] S25. Calculate the position coordinate of the particle in the next time step according to the flow velocity at the position of the particle in the current time step;

[0131] Calculate the number of the grid cell in which the particle is located in the next time step according to the flow field information of the grid cell of the two-dimensional hydrodynamic mathematical model and the position coordinate of the particle in the next time step;

[0132] Calculate the flow velocity at the position of the particle in the next time step according to the flow field information of the grid cell of the two-dimensional hydrodynamic mathematical model and the number of the grid cell in which the particle is located in the next time step;

[0133] Determine whether the current iteration number reaches the set value, if yes, end the method, otherwise, execute step 27, wherein the number of the grid cell in which the particle is located in the next time step is outputted; preferably, determining whether the current iteration number reaches the set value comprises determining whether the current iteration number reaches the time step number at which the particle tracking is stopped.

[0134] S26. Take the position coordinate of the particle in the current time step as the position coordinate of the particle in the next time step, and take the number of the grid cell in which the particle is located in the current time step as the number of the grid cell in which the particle is located in the next time step;

[0135] Calculate the flow velocity at the position of the particle in the next time step according to the flow field information of the grid cell of the two-dimensional hydrodynamic mathematical model and the number of the grid cell in which the particle is located in the next time step;

[0136] Determine whether the current iteration number reaches the set value, if yes, end the method, otherwise, execute step S27, wherein the number of the grid cell in which the particle is located in the next time step is outputted; preferably, determining whether the current iteration number reaches the set value comprises determining whether the current iteration number reaches the time step number at which the particle tracking is stopped.

[0137] S27. Determine the state of the particle in the next time step according to the fall probability, the state comprising a start state and a fall state:

[0138] If the state is the fall state, execute step 28;

[0139] If the state is the start state, reach the next time step, the next time step being the current time step, and execute step S25;

[0140] S28. Determine the state of the particle in the next time step according to the start probability, the state comprising a start state and a deposition state:

[0141] If the state is the start state, reach the next time step, the next time step being the current time step, and execute step S25;

[0142] If the deposition state, to the next time step, the next time step is the current time step, step S26 is performed.

[0143] State definition:

[0144] Starting state: refers to the state of the sediment particles being carried by the flow from the bed surface or deposition position into the suspended or moving state.

[0145] Deposition state: refers to the state of the sediment particles not being carried by the flow, staying on the bed surface, dry-wet boundary or other position.

[0146] Fall and deposit state: refers to the state of the sediment particles falling from the starting state (suspended or moving state) to the bed surface and becoming deposition.

[0147] In step S27, the state of the particle in the next time step is determined according to the fall and deposit probability, including: generating a random number between 0 and 1 for each grid unit of the sediment particle. If the random number is less than or equal to the fall and deposit probability, the next time step of the sediment particle is predicted to be in the fall and deposit state, and the particle state information is updated. If the random number is greater than the fall and deposit probability, the next time step of the sediment particle is predicted to be in the starting state, and the particle state information is updated.

[0148] In step S27, the state of the particle in the next time step is determined according to the fall and deposit probability, including: generating a random number between 0 and 1 for each grid unit of the sediment particle. If the random number is less than or equal to the fall and deposit probability, the next time step of the sediment particle is predicted to be in the fall and deposit state, and the particle state information is updated. If the random number is greater than the fall and deposit probability, the next time step of the sediment particle is predicted to be in the starting state, and the particle state information is updated.

[0149]

[0150] wherein is a set probability threshold (e.g. 0.01); N is the definition of the number of times of settling, which is obtained by the following formula:

[0151]

[0152] wherein represents the water depth, the unit is m, represents the settling velocity, the unit is m / s, represents the time step, the unit is s.

[0153] In step S28, the state of the particle in the next time step is determined according to the starting probability, including: generating a random number between 0 and 1 for each grid unit of the sediment particle. If the random number is less than or equal to the starting probability, the next time step of the sediment particle is predicted to be in the starting state, and the particle state information is updated. If the random number is greater than the starting probability, the next time step of the sediment particle is predicted to be in the deposition state, and the particle state information is updated.

[0154] In step S28, the state of the particle in the next time step is determined according to the starting probability, including: generating a random number between 0 and 1 for each grid unit of the sediment particle. If the random number is less than or equal to the starting probability, the next time step of the sediment particle is predicted to be in the starting state, and the particle state information is updated. If the random number is greater than the starting probability, the next time step of the sediment particle is predicted to be in the deposition state, and the particle state information is updated.

[0155]

[0156] wherein, = 1 / 7.5, = 0.5, denotes time, Shields number The calculation equation is as follows:

[0157]

[0158] wherein, denotes the solid volume weight of the sediment particle, denotes the clear water volume weight, denotes the total water depth, denotes the sediment density, denotes the water body density, denotes the gravity acceleration, denotes the bed surface bottom shear force.

[0159] wherein, the bed surface bottom shear force = is shown by the following equation:

[0160]

[0161]

[0162] wherein, denotes the bed surface bottom shear force, denotes the bed surface bottom shear force in the direction, denotes the bed surface bottom shear force in the direction; denotes the water body reference density, denotes the bottom drag coefficient, denotes the average vertical flow velocity in the Cartesian coordinate system; denotes the average vertical flow velocity in the direction in the Cartesian coordinate system, denotes the average vertical flow velocity in the direction in the Cartesian coordinate system.

[0163] wherein, = 0.28 is the Manning coefficient, denotes the water depth below the zero water surface, M is the roughness coefficient, is the bottom drag coefficient, is the Chezy coefficient.

[0164] In step S25, the position coordinates of the particle in the next time step are calculated based on the flow velocity at the particle's location in the current time step, using the fourth-order Runge-Kutta method (CLRK). This predicts the endpoint of the particle's motion in the next time step.

[0165] To simulate the diffusion effect of sediment particles, a random walk term is introduced to correct for particle positions. The equation for calculating the random walk term is as follows:

[0166]

[0167] In the formula, Represents particles The position coordinates of a point in two-dimensional space at any given time. Represents particles The position coordinates of a point in two-dimensional space at any given time. This represents the average vertical velocity after weighted correction using the Runge-Kutta method. This indicates the weighted correction of the Runge-Kutta method. Average vertical velocity in the direction, This indicates the weighted correction of the Runge-Kutta method. The average vertical velocity in the direction of flow. Represents a random number that follows a standard normal distribution. Indicates in The diffusion displacement component in the direction.

[0168] The diffusion displacement component is given by the following equation:

[0169]

[0170] In the formula, is the horizontal diffusion coefficient.

[0171] The above describes the method of forward tracking to calculate the particle position at the next time step. In sediment particle source tracing, the velocity field is reversed by actual measurement or simulation, while other steps remain unchanged. The path of the particle back from the current deposition location to the source area is calculated. Combined with topographic and hydrological data, the source area of ​​sediment can be determined, such as the source area of ​​estuary sediment transport and coastal erosion.

[0172] When tracing back to the source, the formula above will be used to... The speed conversion is as follows:

[0173]

[0174] T represents the total calculation time; , , respectively, are the average velocities in the x and y directions of the reverse flow field.

[0175] Wherein, the generation of standard normal random number adopts MT19937 (Matteis-Pagnutti) algorithm, a series of vectors are generated as pseudo-random integers uniformly distributed between 0 and 1, which are divided by Then each vector becomes a real number between 0 and 1. The obtained uniformly distributed random number is denoted by , and finally, through the central limit theorem, the final two independent distribution and orthogonal standard normal distribution random numbers are generated:

[0176]

[0177] As a preferred solution, it also includes judging whether the particle is within the boundary, and the particle on the dry-wet boundary is in the deposition state. The position of the particle is reflected by the boundary. Different types of boundaries are implemented with appropriate processing means. The mirror reflection technology is used at the horizontal reflection boundary to make the particle movement more consistent with the actual physical law. The particle is removed at the horizontal open boundary. The ground reflection or water surface residence operation is performed according to the position of the particle at the vertical boundary.

[0178] Step S29: The particle information is copied from the video memory to the device memory, and the particle information is output. In the output file, two loops are needed for reading. The outer loop counts the number of output times, and the inner loop counts the number of particles. The order of particle output is the same as the order of input. The particle output result at the same time consists of the following four parameters: the output position of the particle in the X and Y directions, the time of particle release in the outer mode time step, and the unit number of the particle;

[0179] Step 30. Post-processing is performed on the particle output result, and the distribution of particles in different grid cells is counted. The final result of the particle tracking model is the two-dimensional coordinates of the particles. The model outputs the grid cell number where each particle is located at each time step, which can be used for statistics. The distribution information of these particles is equivalent to the concentration information. Here, the commonly accepted concentration statistical method is used. That is, each particle is considered to represent the same volume of water, and the equivalence calculation between the particle and the concentration is represented by the following formula:

[0180]

[0181] Wherein, represents the average concentration of the i-th unit; is the number of particles in the i-th unit; is the number of particles in the i-th unit; ​​The amount of matter represented by a single particle is generally calculated by dividing the total amount of matter at the initial time by the total number of particles initially released, The volume of the calculation unit. The unit average concentration C can convert the discrete particle distribution into continuous concentration information, intuitively and quantitatively reflect the aggregation degree of sediment in different regions, and overcome the problem that it is difficult to directly measure the sediment density by only the number of particles. Thus, according to the motion trajectory and distribution of the particles, the migration path and source of the particles are determined, and the tracking and tracing of the particles are realized.

[0182] The particle distribution diagram and the particle concentration diagram obtained by post-processing the output file of the sediment tracking and tracing module in the application example by matlab are as follows, and the motion trajectory and the particle concentration diagram of the sediment can be clearly seen. Finally, it should be pointed out that the above is only used to illustrate the technical solutions of the present application and is not limited. Although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that the technical solutions and embodiments of the present application (such as the selection of the model, the use of the formula, the division method of the sub-area, etc.) can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

[0183] Based on the above embodiments, the embodiments of the present application also provide a computer program, which, when running on a computer, causes the computer to execute the method provided in the above embodiments.

[0184] Based on the above embodiments, the embodiments of the present application also provide a computer storage medium, which stores a computer program, and the computer program is executed by a computer to cause the computer to execute the method provided in the above embodiments.

[0185] The storage medium can be any available medium that can be accessed by a computer. For example, but not limited to: the computer readable medium can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer.

[0186] Based on the above embodiments, the embodiments of the present application also provide a chip for reading a computer program stored in a memory, which realizes the method provided in the above embodiments.

[0187] Based on the above embodiments, the embodiments of the present application provide a computer program product, which, when running on an electronic device, realizes the method provided in the above embodiments.

[0188] Those skilled in the art will appreciate that embodiments of the present application can be devised for a variety of applications. FIG. 1 illustrates an example of a system 100 that can employ an embodiment of the present application. As shown in FIG. 1, system 100 can include a host computer 110 that is configured to communicate via one or more wired or wireless communication links 120 with one or more client devices 130. Host computer 110 can include a processor 112, a storage 114, and a communications interface 116. Processor 112 can include one or more processors, such as one or more general purpose processors (e.g., as described below in connection with FIG. 2). Storage 114 can include one or more non-transitory computer-readable storage medium, such as one or more hard disk drives, flash memories, or the like. Storage 114 can store instructions 118 that are executable by processor 112 to implement a method, such as the method described below in connection with FIG. 3. Communications interface 116 can include one or more communications interfaces, such as an Ethernet interface, a Bluetooth interface, a Wi-Fi interface, or the like. Communications interface 116 can be configured to communicate with one or more client devices 130 via one or more wired or wireless communication links 120, such as one or more Ethernet links, one or more Bluetooth links, one or more Wi-Fi links, or the like.

[0189] The present application is described below in reference to flowcharts and / or block diagrams that illustrate the method, apparatus (system) and computer program product according to the present application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing element or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0190] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that the instructions which execute on the computer or other programmable device provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0192] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A particle tracking method, wherein the particles include sediment particles, characterized in that, include S1. Arrange particles in the computational domain of the two-dimensional hydrodynamic mathematical model, and arrange the input information of the particles, including the position coordinates of the particles; S2. Based on the flow field information of the grid cells in the two-dimensional hydrodynamic mathematical model and the position coordinates of the particles, calculate the initial grid cell number where the particles are located; Based on the flow field information of the grid cells in the two-dimensional hydrodynamic mathematical model and the initial grid cell number of the particle, calculate the flow velocity at the particle's location at the current time step. S3-1. Calculate the position coordinates of the particle in the next time step based on the flow velocity at the current time step's particle location; Based on the flow field information of the grid cells in the two-dimensional hydrodynamic mathematical model and the position coordinates of the particles in the next time step, calculate the number of the grid cell where the particles are located in the next time step. Based on the flow field information of the grid cells in the two-dimensional hydrodynamic mathematical model and the grid cell number of the particle in the next time step, calculate the flow velocity at the location of the particle in the next time step. Execute step S4, where the output includes the grid cell number where the particle is located in the next time step; S3-2. Use the position coordinates of the particle in the current time step as the position coordinates of the particle in the next time step, and use the grid cell number of the particle in the current time step as the grid cell number of the particle in the next time step. Based on the flow field information of the grid cells in the two-dimensional hydrodynamic mathematical model and the grid cell number of the particle in the next time step, calculate the flow velocity at the location of the particle in the next time step. Execute step S4, where the output includes the grid cell number where the particle is located in the next time step; S4. Based on the siltation probability, determine the state of the particle in the next time step, including the starting state and the siltation state: If the area is silted up, proceed to step S5; If it is in the start-up state, upon reaching the next time step, where the next time step is the current time step, step S3-1 is executed; S5. Based on the activation probability, determine the state of the particle in the next time step, including the activation state and the deposition state: If it is in the start-up state, upon reaching the next time step, where the next time step is the current time step, step S3-1 is executed; If it is in the deposition state, the next time step is reached. The next time step is the current time step. Step S3-2 is executed. The probability of siltation is calculated based on water depth, settling velocity, and time step. The starting probability is calculated based on the solid bulk density of the particles, the bulk density of the water, the density of the silt, the density of the water, and the shear force on the bed surface.

2. The particle tracking method according to claim 1, characterized in that, Steps S3-1 and S3-2 also include Determine if the current iteration count has reached the set value. If it has, end the method; otherwise, proceed to step S4.

3. The particle tracking method according to claim 2, characterized in that, in, Determine if the current iteration count has reached the set value, including the time step required to stop particle tracking.

4. The particle tracking method according to claim 1, characterized in that, in, In step S4, the state of the particle in the next time step is determined based on the siltation probability, including the generation of a random number between 0 and 1 for the particle. If the random number is not greater than the siltation probability, the state of the particle in the next time step is the siltation state; if the random number is greater than the siltation probability, the state of the particle in the next time step is the starting state. The probability of siltation is given by the following formula: In the formula, This indicates setting a probability threshold, where N represents the number of settlement events; The number of settlement events N is given by the following formula: In the formula, This indicates water depth, measured in meters (m). This indicates the settling velocity, and the unit is m / s. This represents a time step, measured in seconds (s).

5. The particle tracking method according to claim 1, characterized in that, in, In step S5, the state of the particle in the next time step is determined based on the activation probability. This includes generating a random number between 0 and 1 for the particle. If the random number is not greater than the activation probability, the state of the particle in the next time step is the activation state; if the random number is greater than the activation probability, the state of the particle in the next time step is the deposition state. The starting probability is given by the following formula: In the formula, =1 / 7.5, =0.5, Indicates time, Represents the number of Shields; Among them, the number of Shields As shown in the following formula: In the formula, This indicates the solid bulk density of sediment particles. Indicates the specific gravity of clear water. Indicates the total water depth. Indicates the density of sediment. Indicates the density of water. Represents gravitational acceleration. Indicates the shear force at the bottom of the bed surface; Among them, the shear force at the bottom of the bed surface = As shown in the following formula: In the formula, Indicates the shear force at the bottom of the bed surface. express Shear force at the bottom of the bed surface in the direction of the direction. express Shear force at the bottom of the bed surface in the direction of the direction; Indicates the reference density of the water body. Indicates the bottom drag coefficient. This represents the average vertical velocity in the Cartesian coordinate system. Represents the Cartesian coordinate system The average vertical velocity in the direction of flow. Represents the Cartesian coordinate system The average vertical velocity in the direction of flow; Among them, the bottom drag coefficient As shown in the following formula: In the formula, This represents the Manning coefficient. This indicates the water depth below zero water level, and M represents the roughness coefficient. This represents the gratuitousness coefficient.

6. The particle tracking method according to claim 1, characterized in that, Based on the flow velocity at the particle's location in the current time step, the particle's position coordinates in the next time step are calculated, as shown in the following formula: In the formula, Represents particles The position coordinates of a point in two-dimensional space at any given time. Represents particles The position coordinates of a point in two-dimensional space at any given time. This indicates the weighted correction of the Runge-Kutta method. Average vertical velocity in the direction, This indicates the weighted correction of the Runge-Kutta method. The average vertical velocity in the direction of flow. Represents a random number that follows a standard normal distribution. Indicates in Diffusion displacement component in the direction; Among them, Diffusion displacement components in the direction As shown in the following formula: In the formula, The horizontal diffusion coefficient; Among them, random numbers from the standard normal distribution As shown in the following formula: In the formula, Represents a uniformly distributed random number.

7. The particle tracking method according to claim 1, characterized in that, The tracing is source tracing. Based on the flow velocity at the particle's location in the current time step, the position coordinates of the particle in the next time step are calculated, as shown in the following formula: In the formula, Represents particles The position coordinates of a point in two-dimensional space at any given time. Represents particles The position coordinates of a point in two-dimensional space at any given time. This represents the average velocity in the x-direction of the reverse flow field. This represents the average velocity in the y-direction of the reverse flow field. Represents a random number that follows a standard normal distribution. Indicates in Diffusion displacement component in the direction; in, and As shown in the following formula: In the formula, T is the total calculation time. This indicates the weighted correction of the Runge-Kutta method. Average vertical velocity in the direction, This indicates the weighted correction of the Runge-Kutta method. The average vertical velocity in the direction of flow; Among them, Diffusion displacement components in the direction As shown in the following formula: In the formula, The horizontal diffusion coefficient; Among them, random numbers from the standard normal distribution As shown in the following formula: In the formula, Represents a uniformly distributed random number.

8. The particle tracking method according to claim 1, characterized in that, Also includes S6. Based on the grid cell number of the particle in the next time step, obtain the grid cell number of different particles in each next time step, and calculate the number of the first particle in the grid cell of the two-dimensional hydrodynamic mathematical model. The average particle concentration per unit is given by the following formula: In the formula, Indicates the first Average particle concentration per unit, Indicates the first The number of particles in each unit; It represents the mass represented by a single particle. This indicates the volume of the computational unit.

9. The particle tracking method according to claim 1, characterized in that, Two-dimensional hydrodynamic mathematical models are Reynolds-averaged Navier-Stokes equations for incompressible fluids based on the hydrostatic assumption. The governing equations of the computational model in the coordinate system include: Continuity equation: Momentum equation: In the formula: t represents water level; t represents time. Indicates the total water depth. , Indicates the water depth below zero. Represents the Cartesian coordinate system The average vertical velocity in the direction of flow. Represents the Cartesian coordinate system The average vertical velocity in the direction of flow; Represents coordinates in Cartesian space. Represents Cartesian space Directional coordinates Represents Cartesian space The coordinates of the direction, z represents the Cartesian space. Coordinates of direction; Indicates the evaporation flux rate; Indicates the flux rate of rainfall; Indicates the density of water; Indicates the inflow flux rate; Indicates the area of ​​the control volume; Indicates the Coriolis force coefficient; Represents gravitational acceleration; Indicates the reference density of the water body; Represents the vertical coordinate; Represents the shear force on the free surface. express Free surface shear force in the direction of the direction express Free surface shear force in the direction of the direction; Indicates the shear force at the bottom of the bed surface. express Shear force at the bottom of the bed surface in the direction of the direction. express Shear force at the bottom of the bed surface in the direction of the direction; , Represents the resistance term. express The resistance term in the direction, express The resistance term in the direction; and This represents the horizontal viscous diffusion term. express The horizontal viscous diffusion term in the direction, express The horizontal viscous diffusion term in the direction.

10. An electronic device, the electronic device comprising: One or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions that, when executed by the electronic device, cause the electronic device to perform the method of any one of claims 1-9.

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