High Reynolds number fluid flow simulation method in near-field dynamics differential operator, program, equipment and storage medium
Patent Information
- Application Number
- CN202511466021.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-14
- Publication Date
- 2026-01-30
Smart Images
Figure CN121435802A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of computational fluid dynamics, specifically relating to a method, program, device, and storage medium for simulating high Reynolds number fluid flow based on peri-field dynamic differential operators. Background Technology
[0002] Peri-field dynamics differential operators provide a nonlocal, meshless method for fluid simulation. However, this method often suffers from numerical instability and computational divergence when simulating medium-to-high Reynolds number fluid flows. This is because the accumulation of numerical discretization errors generates non-physical density fluctuations. These density fluctuations are amplified in medium-to-high Reynolds number fluid flow simulations, potentially leading to severe pressure field oscillations and ultimately causing computational divergence, severely limiting the application of peri-field dynamics differential operator methods in medium-to-high Reynolds number fluid flow problems. Most existing peri-field dynamics differential operator fluid models have only verified the feasibility of simulating low Reynolds number fluid flows.
[0003] In order to extend the near-field dynamics differential operator method to a wider range of fluid flow simulation problems, there is an urgent need to develop improved methods that can effectively suppress density fluctuations and improve the stability of fluid flow simulations at medium and high Reynolds numbers. Summary of the Invention
[0004] The purpose of this invention is to overcome the numerical instability caused by non-physical density fluctuations in existing peri-field dynamics differential operator fluid computation methods for simulating fluid flows at medium to high Reynolds numbers. This problem can be solved by introducing an artificial, conservative density diffusion term into the continuity equation within the standard peri-field dynamics differential operator fluid computation framework. This invention proposes a peri-field dynamics differential operator method for simulating fluid flows at medium to high Reynolds numbers that considers density diffusion correction.
[0005] This invention provides a method for simulating high Reynolds number fluid flow using a peridynamic differential operator. The method involves spatially discretizing the fluid computational domain, classifying particles into flow domain particles and boundary particles, and setting boundary conditions and fluid physical parameters according to the computational conditions. It calculates the relative position vectors of neighboring particles within the neighborhood radius and, combined with the highest differential order, calculates the peridynamic differential operator. Based on the peridynamic differential operator, the fluid control equations are discretized, and a specially constructed density diffusion term is introduced into the fluid control equations. The density, velocity, and pressure of flow domain particles are updated according to the discretized peridynamic differential operator fluid control equations and state equations. Finally, a virtual particle method is used to update the density and pressure of boundary particles.
[0006] Furthermore, the watershed particle physics parameters include initial velocity, initial density, initial pressure, initial volume, kinematic viscosity, and maximum characteristic velocity; the boundary particle physics parameters include initial velocity, initial density, initial pressure, initial volume, and boundary velocity of the boundary particles; and the fluid physics parameters include the initial density of the fluid, the kinematic viscosity of the fluid, and the dynamic viscosity of the fluid.
[0007] Furthermore, the near-field dynamics differential operator for: in, They are respectively The order of the partial differential; The central particle; For the central particle Location; For the central particle near-field neighborhood Neighboring particles within; Neighborhood particles Location; It is the highest differential order; ; The coefficients to be determined; , Relative position vector The amount; For the weight function, , It is a constant; in, It is a shape matrix; The coefficient matrix is determined by the order of the differential. The exponent related to the differential order satisfies ; The symbol for Kronecker; For neighborhood particles The volume.
[0008] Furthermore, the near-field dynamics differential operator is used to construct the fluid control equations as follows: (1) Construct density diffusion term; in, This is a coefficient related to density diffusion intensity; The distance between particles; The index is set manually; The speed of sound in a fluid or the artificial speed of sound; , For particles and The density; The trace of the second-order peri-field dynamical differential operator; For neighborhood particles Volume; (2) By introducing a density diffusion term into the continuity equation of the near-field dynamic differential operator fluid control equation, a new continuity equation is constructed as follows: (3) The momentum conservation equation is expressed in the form of the basic peri-field dynamic differential operator as follows: in, The coefficient of dynamic viscosity; For action on particles Volume force; It is a first-order peri-field dynamical differential operator. ; , For particles and speed; , Particles and The pressure.
[0009] Furthermore, the state equation employs the Tait equation to express the pressure. With density Connecting them as: in, This is the reference density of the fluid; Bulk modulus of the fluid; Specific heat ratio.
[0010] Furthermore, the watershed particles are updated at each time step. Within this process, time integration and solution are performed, specifically as follows: (1) Calculate watershed particles density diffusion term ; (2) Calculate watershed particles acceleration ; (3) Update speed; in, The unit time step; (4) Update the density according to the continuity equation that introduces the density diffusion term; in, For velocity divergence; (5) Adopt a new density Update the pressure using the equation of state; (6) Calculate the new acceleration and complete the velocity update; .
[0011] Furthermore, the boundary particle update specifically refers to: Virtual particle layers, known as boundary particles, are arranged outwards from the wall surface. The velocity of the boundary particles is the same as the wall velocity. pressure according to Real fluid particles within range Calculations are performed using pressure and fluid acceleration: in, and Boundary particles acceleration and position vectors, For particles at a distance from the wall A collection of particles in the watershed. For real fluid particles The position vector; Boundary particles density The following was obtained by solving the state equations: .
[0012] The present invention also provides a computer device / equipment / system, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the high Reynolds number fluid flow simulation method in the peri-field dynamics differential operator described above.
[0013] The present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the high Reynolds number fluid flow simulation method in peri-field dynamics differential operators described above.
[0014] The present invention also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps of the high Reynolds number fluid flow simulation method in peri-field dynamics differential operators described above.
[0015] The beneficial effects of this invention are as follows: (1) The method of the present invention improves the stability of medium-high Reynolds number fluid flow simulation by introducing a constructed, conservative artificial density diffusion term into the continuity equation: it can significantly suppress or eliminate the density fluctuation and pressure oscillation problems commonly found in medium-high Reynolds number fluid flow simulation, prevent premature calculation divergence, and enable the near-field dynamic differential operator method to stably simulate medium-high Reynolds number fluid flow phenomena. (2) The method of the present invention maintains physical conservation: the proposed density diffusion term construction method aims to approximately satisfy the basic requirements of mass conservation and avoid the introduction of non-physical sources or sinks; (3) The density diffusion correction term of the method of the present invention can be relatively easily integrated into the existing peri-field dynamic differential operator fluid calculation code framework. Its main modification is concentrated in the solution part of the continuity equation, and it is also based on the peri-field dynamic differential operator method, which significantly reduces the difficulty of code modification and system integration. Attached Figure Description
[0016] Figure 1 is a flowchart of the calculation of the near-field dynamics differential operator fluid simulation method considering density diffusion correction proposed in this invention; Figure 2 is a comparison of the effects of the method of the present invention and the prior art in the simulation of the top cover driven square cavity flow, wherein Figure (a) is the result obtained by using the present invention, and Figure (b) is the result obtained by using the basic near-field dynamics differential operator method; Figure 3 shows the flow of the square cavity driven by the method of the present invention in the top cover. A comparison diagram of the simulated solution and the reference solution for the horizontal velocity along the centerline in the Y direction under the working condition; Figure 4 shows the flow of the method of the present invention in the top cover driving cavity. A comparison diagram of the simulated solution and the reference solution for the vertical velocity along the center line in the X direction under the working condition; Figure 5 illustrates the flow in the square cavity driven by the top cover. Under the operating conditions, the complete process of the velocity field smoothly evolving from an initial static state to a stable vortex structure; Figure 6 illustrates the flow in the square cavity driven by the top cover. Under the operating conditions, the complete process of the velocity field smoothly evolving from an initial static state to a stable vortex structure; Figure 7 illustrates the flow in the square cavity driven by the top cover. Under the operating conditions, the complete process of the velocity field smoothly evolving from an initial static state to a stable vortex structure; Figure 8 shows the application of the method of the present invention in the Kuet stream. A comparison diagram of the simulated solution and analytical solution of the velocity profile in the simulation; Figure 9 shows the application of the method of the present invention in the Kuet stream. A comparison diagram of the simulated solution and analytical solution of the velocity profile in the simulation; Figure 10 shows the application of the method of the present invention in the Kuet River. In the simulation, the evolution of the velocity field from the initial state to the final steady state is shown. Figure 11 shows the method of the present invention in the Kuet stream. The simulation shows the evolution of the velocity field from its initial state to its eventual steady state. Detailed Implementation
[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
[0018] This invention discloses a method for simulating high Reynolds number fluid flow in a near-field dynamics differential operator that considers density diffusion correction. Specifically: Step S1: Initialization and Discretization of the Water Domain The watershed is spatially discretized and particle parameters are assigned. The fluid computational domain is discretized into a series of meshless particles. It distinguishes between watershed particles and boundary particles, and sets the value of each particle point. initial position Finally, the initial velocity of the particle point is set according to the type of particle. Initial density Initial pressure and initial volume For internal fluid particles, relevant physical parameters need to be set for subsequent calculations. Besides the evolution of density and pressure through the continuity equation and the equation of state, kinematic viscosity also needs to be provided. and maximum characteristic velocity In calculations involving driving forces, the maximum characteristic velocity is generally the driving velocity. For boundary particles, it is necessary to set the boundary velocity of the boundary particles, which depends on the relationship between the boundary and the fluid, such as the setting of solid wall boundaries and slip boundaries.
[0019] Step S2: Construct the peri-field dynamic differential operator for a two-dimensional function. Its partial differential expression can be transformed into the following form using the near-field dynamics differential operator method: ,in They are respectively The order of the partial differential; The central particle; For the central particle Location; For the central particle The near-field neighborhood; Representing the central particle near-field neighborhood Neighboring particles within; Neighborhood particles Location; For the point The function at the location; It is a relative position vector; For neighborhood particles The volume.
[0020] Set neighborhood radius Search for all belonging to this neighborhood radius particle Determine the central particle Given a set of neighboring particles, obtain the corresponding relative position vector. Finally, calculate the corresponding The specific steps are as follows: (1) Calculate the weight function for each particle pair: (1) in, Let 2 be a constant. (2) Calculate the shape matrix of each particle: (2) in, The exponent related to the differential order satisfies ; ; (3) Calculate the coefficient matrix for each particle. : (3) in, , The symbol for Kronecker; (4) Calculate the peri-field dynamic differential operator for each particle pair. : (4) Step S3: Watershed Particle State Update (1) Establish the modified fluid control equations.
[0021] Modified continuity equation: An artificial density diffusion term is introduced into the continuity equation in the form of the basic peri-field dynamics differential operator. The format is as follows: (5) in, For particles The density; , For particles and speed; It is a first-order peri-field dynamical differential operator. ; density diffusion term Construction: In order to ensure mass conservation and effectively suppress density fluctuations, a second-order peri-field dynamic differential operator is used for construction.
[0022] For the central particle Its density diffusion term is calculated as follows: (6) in, The coefficient is related to the density diffusion intensity, and its value is preferably in the range of 0.01 to 0.2; The distance between particles; The index is set manually, and the preferred value range is 1 to 2; The speed of sound in a fluid or the artificial speed of sound; It is a second-order peri-field dynamical differential operator. ; It is the trace of the second-order near-field dynamics differential operator.
[0023] The momentum conservation equation is still expressed in the form of the basic peri-field dynamics differential operator: (7) in, For particles speed; Particles and The pressure; It is a first-order peri-field dynamical differential operator; The coefficient of dynamic viscosity; For action on particles The volume force.
[0024] (2) Equations of state The state equation uses the Tait equation to express the pressure. With density Connecting them: (8) in, This is the reference density of the fluid; For fluid bulk modulus, For specific heat ratio, it is usually taken as 7 for water.
[0025] (3) Perform time integration and solve. At each time step Within, perform the following calculations in sequence: (3.1) Calculate all particles density diffusion term According to formula (6), the current time is used. density and second-order peri-field dynamics differential operator traces calculate, Choose a value of 0.1. Choose value 2.
[0026] (3.2) Calculate acceleration .use Physical quantities at time and peri-field dynamic differential operators , Computational watershed particles acceleration , .
[0027] (3.3) Update speed. First part of the execution time integral: Update speed: ,in The unit time step.
[0028] (3.4) Update density. Use Density of time Density diffusion term Calculate the velocity divergence: (9) Then update the density according to the modified continuity equation. time: (10) (3.5) Update pressure. Use the new density. The new pressure is calculated using the state equation (8). .
[0029] (3.6) Calculate the new acceleration and complete the velocity update. Use equation (7) to calculate the acceleration. Then complete the speed update: Repeat the time integration step until the total simulation time is reached.
[0030] Step S4: Boundary Particle State Update (1) Boundary condition processing. For solid wall boundaries, the virtual particle method is used. A virtual particle layer, i.e., boundary particles, is arranged outside the wall surface. The velocity of the boundary particles is usually set to the wall velocity. Boundary particles pressure According to its scope Real fluid particles inside The pressure and fluid acceleration are extrapolated for calculation, for example, using the following formula: (11) in, Boundary particles The pressure; and Boundary particles acceleration and position vectors, For particles at a distance from the wall A collection of particles in the watershed. For fluid particles The position vector. Then, the boundary particles. The density is determined by its pressure through the equation of state (8). The reverse calculation yields: (12) Example 1 This implementation uses a simulated two-dimensional top-cover driven square cavity flow problem as an example. The target Reynolds number for this example is set to be... .
[0031] Specifically: Step S1: Initialization and Discrete Water Domain (1) Setting fluid physical parameters Set the initial density of the fluid to be Set the kinematic viscosity of the fluid. The kinematic viscosity Based on the target Reynolds number Top cover drive speed and the side length of the square cavity To determine, the calculation formula is: The dynamic viscosity of the fluid is... .
[0032] (2) Set the parameters of the state equation The Tait equation of state (8) is used to correlate pressure and density. A reference density needs to be defined in the equation of state. ,parameter and .parameter In this example, option 7 is selected. To ensure the weak compressibility assumption in the simulation, the artificial speed of sound is... Typically, a velocity much larger than the maximum flow velocity in the flow field is required; in this embodiment, it can be set... .
[0033] (3) Define the computational domain as a region with a side length of 1. A square region. Discretize this computational domain into... Uniformly distributed particle points In this embodiment, it is set At this point, the initial spacing between the particles .
[0034] (4) Set initial and boundary conditions At the initial moment All fluid particles within the square cavity speed Set to 0. The boundary conditions are as follows: particles on the top wall of the square cavity have a constant horizontal velocity. ,Right now The other three walls, including the left wall, the right wall, and the bottom wall, are all stationary walls with a velocity of 0.
[0035] Step S2: Calculation of near-field dynamic differential operators Step 2.1: Determine the peri-field dynamic neighborhood radius, weighting function, and highest differential order. Peri-field radius With particle spacing The relationship is Among them, the scaling factor Typically, a value of around 3 is used; in this embodiment, a value of [value] is used. ,Right now The weighting function is Gaussian. The highest order of differentiation is chosen to be 2.
[0036] Step 2.2: Search for the neighboring particles of the central particle based on the neighborhood radius and form a corresponding set of neighboring particles. Calculate the relative position vector of each particle pair. .
[0037] Step 2.3: Calculate the shape matrix for each particle. : .
[0038] Step 2.4: Calculate the coefficient matrix for each particle. : .
[0039] Step 2.5: Calculate the peri-field dynamic differential operator for each particle pair. : .
[0040] Step S3: Watershed Particle State Update Step 3.1: Set the density diffusion term parameters introduced in this invention. Set the diffusion coefficient. Sum of Indices Particle spacing The initial particle spacing is usually taken as . , This has already been set in the preceding steps. Parameters Control the intensity of density diffusion. Based on experience, The value of needs to be adjusted to strike a balance between effectively suppressing density fluctuations and avoiding introducing excessive numerical dissipation. In this embodiment, is set as follows: .
[0041] Step 3.2: Calculate the density diffusion term. Calculate the density diffusion term for each internal flow domain particle according to formula (6). Density diffusion correction term .
[0042] Step 3.3: Calculate acceleration. Use the current time. pressure ,density ,speed Calculate each internal fluid particle acceleration vector , .
[0043] Step 3.4: Update speed. Update speed to half the time step. .
[0044] Step 3.5: Update density. First, calculate the velocity divergence according to formula (9). Then, use formula (10) to... Density of time Density diffusion term and velocity divergence Update density to .
[0045] Step 3.6: Update pressure. Use the density obtained from the previous step. The new pressure is calculated using the state equation formula (8). .
[0046] Step 3.7: Calculate the new acceleration. (Use...) The pressure of every moment ,density and half-time step velocity To calculate acceleration at any moment .
[0047] Step 3.8: Complete the speed update. Complete the speed update to... time: .
[0048] Step S4: Boundary Particle State Update For each boundary particle The pressure is updated according to formulas (11) and (12). and density .
[0049] By repeatedly executing steps S1 to S4 above, the flow of the top cover driving square cavity can be obtained. Simulation results under the given conditions.
[0050] Results Analysis: In order to evaluate the effectiveness of the method of the present invention, the results obtained by the method of the present invention are compared with the results obtained by the basic near-field dynamics differential operator method without the correction term. The comparison results are shown in Figure 2. Figure 2 (a) shows the results obtained using the present invention, demonstrating a smooth flow field, clear physical vortex structure, and stable computation. Figure 2(b) shows the results obtained using the basic near-field dynamics differential operator method, where severe non-physical pressure oscillations and numerical noise appear in the flow field. This comparison strongly demonstrates the significant technical effect of the present invention in suppressing numerical instability in medium-to-high Reynolds number fluid flows. Meanwhile, the density diffusion term not used in the present invention... The simulation calculation of the driving flow of the top cover cavity under operating conditions directly diverges. To further quantitatively verify the accuracy of the method of the present invention, Figures 3 and 4 will use the method of the present invention. The velocity distribution along the center line of the top cover driven square cavity under the working condition was compared with the benchmark solution, and the results showed good agreement, proving that the method of the present invention can not only guarantee stability, but also has a certain degree of calculation accuracy.
[0051] To further demonstrate the applicability of the method of the present invention in fluid flow simulations at different Reynolds numbers, simulations were performed on the flow of the top cover-driven square cavity under different Reynolds number conditions. For example... Figure 5 As shown in Figures 6 and 7, this method in ,exist and The invention maintains long-term computational stability under various operating conditions and clearly captures the evolution of the flow field, demonstrating its stability and robustness in handling high Reynolds number flows in fluids.
[0052] Furthermore, to further verify the accuracy and stability of the method of this invention for simulating fluid flows at medium to high Reynolds numbers, this invention also performed simulations of Cuyet flow examples. For example... Figure 8 and Figure 9 As shown, the velocity profiles obtained by this method at different Reynolds numbers are in high agreement with the analytical solutions. Furthermore, as shown in Figures 10 and 11, the fluid velocity field can smoothly evolve from the initial state to the steady state.
[0053] Therefore, the embodiments of this invention verify the effectiveness of the proposed near-field dynamic differential operator method considering density diffusion correction in simulating high Reynolds number fluid flow problems from different perspectives through two classic benchmark examples: top-cover driven square cavity flow and Couette flow. The results show that this method can significantly improve the stability and robustness of the calculation and obtain accurate and reliable physical simulation results.
[0054] In summary, this invention first initializes and discretizes the flow domain, distinguishing particles into flow domain particles and boundary particles. Based on the computational conditions, it sets the boundary conditions and the physical parameters of the fluid. Then, it sets the neighborhood radius of the near-field dynamics differential operator. The highest differential order and weight function, for watershed particles Based on neighborhood radius To search for neighboring particles And calculate its relative position vector The peri-field dynamic differential operator is calculated based on the highest differential order and the relative position vector. Secondly, the fluid control equations are discretized based on the peri-field dynamic differential operator, and a specially constructed density diffusion term is introduced into the continuity equation. The density, velocity, and pressure of the flow field particles are updated according to the discretized peri-field dynamic differential operator fluid control equations and state equations. Finally, the physical parameters of the boundary particles are updated. Based on the initial boundary conditions, the density and pressure of the boundary particles are updated using the virtual particle method.
[0055] In particular, in some preferred embodiments of the present invention, a computer device is also provided, including a memory and a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the high Reynolds number fluid flow simulation method in the peri-field dynamic differential operator described in any of the above embodiments.
[0056] In some other preferred embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program / instruction is stored, wherein when the computer program is executed by a processor, it implements the steps of the high Reynolds number fluid flow simulation method in the peri-field dynamic differential operator described in any of the above embodiments.
[0057] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the high Reynolds number fluid flow simulation method in near-field dynamics differential operators, which will not be repeated here.
[0058] The foregoing has only described certain exemplary embodiments of this invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of this invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of this invention.
Claims
1. A method for simulating fluid flow at high Reynolds number in near-field dynamic differential operator, characterized in that, a fluid calculation domain is spatially discretized, and particles are divided into flow domain particles and boundary particles, and boundary conditions and physical parameters of the fluid are set according to a calculation condition; relative position vectors of neighborhood particles within a neighborhood radius are calculated, and a near-field dynamic differential operator is calculated in combination with a highest differential order; a fluid control equation is discretized based on the near-field dynamic differential operator, and a specially-structured density diffusion term is introduced into the fluid control equation; density, velocity and pressure of the flow domain particles are updated according to the discretized near-field dynamic differential operator fluid control equation and a state equation; and density and pressure of the boundary particles are updated by using a virtual particle method.
2. The method of claim 1, wherein the high Reynolds number fluid flow simulation in a near field dynamic differential operator is characterized by, The physical parameters of the flow domain particles include initial velocity, initial density, initial pressure, initial volume, kinematic viscosity and maximum characteristic velocity; the physical parameters of the boundary particles include initial velocity, initial density, initial pressure, initial volume and boundary velocity of the boundary particles; and the physical parameters of the fluid include initial density of the fluid, kinematic viscosity of the fluid and dynamic viscosity of the fluid.
3. The method of claim 1, wherein the high Reynolds number flow simulation in a near field dynamic differential operator is characterized by, The near field dynamics differential operator is: wherein are partial differential orders; are partial differential orders; is a central particle; is a central particle is a position of a neighborhood particle; is a central particle is a neighborhood particle within a near field neighborhood of a central particle; is a neighborhood particle is a position of a neighborhood particle; is a highest differential order; ; is a coefficient to be determined; , is a component of a relative position vector ; is a weight function, , is a constant; wherein is a shape matrix; is a coefficient matrix determined by the differential order; is an index related to the differential order, satisfying ; is a Kronecker symbol; is a volume of a neighboring particle .
4. The method of claim 1, wherein the method is used for simulating fluid flow at high Reynolds number in a near field dynamic micro-actuator. The near-field dynamic differential operator is specifically constructed for the fluid control equation as follows: (1) a density diffusion term is constructed; wherein, is a coefficient related to the density diffusion strength; is the inter-particle distance; is an artificial set index; is the speed of sound in the fluid or an artificial speed of sound; , is the density of the particles and ; is the trace of the second order near-field dynamics differential operator; is the volume of the neighboring particles ; (2) the density diffusion term is introduced into a continuity equation of the near-field dynamic differential operator fluid control equation, and a new continuity equation is constructed as: (3) a momentum conservation equation is expressed in a basic form of the near-field dynamic differential operator as: wherein is the dynamic viscosity coefficient; is the volume force acting on the particles ; is the first order near field dynamics differential operator, ; , is the velocity of the particles and ; , are the pressures of the particles and , respectively.
5. The method of claim 1, wherein the near field dynamic micro- scale operator for simulating fluid flow at high Reynolds number is characterized by, The equation of state employs a Tait equation to relate pressure to density wherein, is the reference density of the fluid; is the bulk modulus of the fluid; is the specific heat ratio.
6. The method of claim 1, wherein the near field dynamic micro- scale operator for simulating fluid flow at high Reynolds numbers is characterized by, The flow basin particle update is at each time step The time integration and solution are performed, in particular: (1) calculating a density diffusion term of the basin particle ; (2) calculating the acceleration of the basin particle ; (3) velocity is updated; wherein, is the unit time step; (4) density is updated according to the continuity equation with the introduced density diffusion term; wherein is the velocity divergence; (5) Update pressure using new density and equation of state. (6) new acceleration is calculated, and velocity is updated; 。 7. The method of claim 1, wherein the method is used for simulating fluid flow at high Reynolds numbers in near field dynamic micro-divisional operators. The boundary particle updating is specifically as follows: A virtual particle layer is arranged outside the normal direction of the wall surface as boundary particles, and the velocity of the boundary particles is the wall surface velocity; the boundary particles pressure According to the pressure and fluid acceleration of real fluid particles in the range calculation: in, and Boundary particles acceleration and position vectors, For particles at a distance from the wall A collection of particles in the watershed. For real fluid particles The position vector; Boundary particles Density of the particles By the equation of state 。 8. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-7. The processor executes the computer program to realize the steps of the method in any one of claims 1 to 7.
9. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to realize the steps of the method in any one of claims 1 to 7.
10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to realize the steps of the method in any one of claims 1 to 7.