Multi-scale fluid dynamics digital twin model based on coupling algorithm

Abstract

The invention discloses a multi-scale hydrodynamic digital twin model based on a coupling algorithm, which ensures the accuracy and consistency of the model on the overall and local levels through effective information exchange and integration among different scales. The consistency of macroscopic and microscopic variables is ensured on a boundary layer through bridging coupling of different scale models and a Lagrangian multiplier method, and physical behaviors of a complex system are simulated more accurately. Besides, through parameter estimation and data fusion, the model can integrate and utilize real-time data, so that dynamic optimization and prediction are realized, and the practicability and flexibility of the model are greatly enhanced. Therefore, the construction of the multi-scale digital twinborn model not only enhances the description capability of the model to a complex system, but also improves the adaptability and prediction precision of the model in practical application, and provides a more efficient and dynamic method for accurately simulating and predicting behaviors of the complex system.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a multi-scale fluid dynamics digital twin model based on a coupling algorithm. Background Art

[0002] Digital twin is the process of digitally defining and modeling the composition, characteristics, functions and performance of physical entities using information technology. Digital twin technology has achieved rapid development since its birth. It was initially mainly used for equipment monitoring and maintenance in the industrial field, and real-time monitoring and prediction were carried out by establishing digital entity system models. With the continuous maturity of artificial intelligence, Internet of Things and big data technologies, digital twin technology has gradually expanded to fields including production optimization, supply chain management, health care, etc., realizing the comprehensive application of digital transformation. In the early days, digital twin technology was mainly applied in industrial manufacturing and engineering fields, and was used for virtual modeling, simulation and predictive analysis to improve production efficiency and product quality. With the rapid development of data acquisition and processing technology, digital twin technology has gradually expanded to include real-time monitoring and maintenance of large-scale equipment, covering fields such as industrial Internet and intelligent manufacturing. With the vigorous development of artificial intelligence and deep learning technology, digital twin technology continues to strengthen the intelligent analysis and decision support capabilities of complex systems, and has attracted more and more attention and application from more and more industries.

[0003] In recent years, although digital twin technology has been continuously improved, there are still many technical defects, including at least the following aspects: 1. Existing digital twin technology often faces the problems of insufficient accuracy and limited scope of application when dealing with complex system simulations, especially the lack of effective methods in information exchange and integration between different scales, resulting in the inability to accurately simulate the behavior and interaction of the system at the macro and micro levels. 2. The lack of flexible parameter estimation and data fusion mechanisms limits the dynamic optimization and prediction capabilities of the model, reducing the adaptability and prediction accuracy of the model in practical applications. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a multi-scale fluid dynamics digital twin model based on a coupling algorithm in view of the various deficiencies in the prior art.

[0005] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0006] The construction process of a multi-scale fluid dynamics digital twin model based on a coupling algorithm is as follows:

[0007] (1) Multi-scale modeling:

[0008] A. Determine the macroscopic, mesoscopic, and microscopic levels of the system, and establish corresponding mathematical models for each level;

[0009] B. Design algorithms to couple models of different scales through bridging;

[0010] C. Information exchange: In the contact area between the macro model and the micro model, the velocity field and pressure field information obtained by the macro model are used as the boundary conditions of the micro model, and the particle distribution and stress field information calculated by the micro model are fed back to the macro model for fine-tuning the macro parameters;

[0011] D. Coupling strategy: Lagrange multiplier method is used to ensure the consistency and continuity of macroscopic and microscopic variables in the boundary layer;

[0012] (2) Multi-physics coupling;

[0013] (3) Parameter estimation and data fusion.

[0014] As a preferred technical solution of the present invention, the mathematical model established at the macro level is as follows, and the physical phenomenon is described by partial differential equations:

[0015]

[0016] Among them, u is the fluid velocity field, p is the pressure field, ρ is the fluid density, μ is the dynamic viscosity, and f is the external force. is the gradient of the internal pressure of the fluid; to ensure the conservation of the internal mass of the fluid, the continuity equation

[0017] (1) Determine the physical boundary conditions and initial conditions of the problem and the physical models that need to be introduced; create a geometric model of the fluid flow area and discretize the area using a mesh generation algorithm;

[0018] (2) The partial differential equations are solved using methods including the finite difference method, the finite element method, and the finite volume method. For flows with high Reynolds numbers involving turbulence effects, large eddy simulation is used as a coupling for the turbulence model.

[0019] As a preferred technical solution of the present invention, the mathematical model established at the mesoscopic level is as follows:

[0020]

[0021] Where M(φ) is the material parameter, δF / δφ is the variational derivative, and φ(x, t) is the phase field variable, which describes the interfacial motion of the two-phase fluid;

[0022] (1) Discretize the space into N grid points, define the grid spacing as Δx, and apply the explicit finite difference method to discretize the time derivative and spatial gradient operator;

[0023] (2) Using an iterative algorithm to solve the discrete form of the two-dimensional phase field equation, the initial condition is a randomly generated initial value field, and the parameters are set according to the physical problem;

[0024] (3) Based on the curvature effect and surface tension, an additional surface tension term is introduced at the interface, and the Young-Laplace equation is used to describe the influence of curvature on the pressure difference, and the interface tension term is processed algorithmically.

[0025] As a preferred technical solution of the present invention, the mathematical model established at the microscopic level is as follows:

[0026] m i *d 2 r i / dt 2 =F i

[0027] Among them, F i It includes the forces between molecules and the forces between molecules and walls. i is the position vector of the ith particle, m i is its mass; the initial particle coordinates and velocities are set based on the Maxwell-Boltzmann distribution of crystal structure, random distribution and temperature, and the time evolution process is realized by integrating Newton's equations of motion:

[0028] Calculate the acceleration:

[0029]

[0030] Update location:

[0031] ri(t+Δt)=ri(t)+vi(t)Δt+0.5ai(t)Δt 2

[0032] Calculate the new force and acceleration:

[0033]

[0034] Update speed:

[0035] vi(t+Δt)=vi(t)+0.5(ai(t)+ai(t+Δt))Δt

[0036] Through the above steps, the position r of each particle is gradually solved. i and velocity vi, satisfying the Newtonian equations of motion it is subject to.

[0037] As a preferred technical solution of the present invention, models of different scales are coupled through bridging. First, a bridging term (Ci) is defined to transfer information between scales i and j. The mathematical expression of the bridging term is set as follows:

[0038] Dynamic consistency condition: σmacro(x,t)=σmeso(x,t)

[0039] Thermodynamic consistency condition: Emeso(X,T)=Emicro(X,T)

[0040] Iteratively solve the model at each level and pass the information of the corresponding level through the bridge term:

[0041] From macro to meso:

[0042] Xmeso(n+1)=Fmacro→meso(Xmacro(n),Cmacro→meso)

[0043] From mesoscopic to microscopic:

[0044] Xmicro(n+1)=Fmeso→micro(Xmeso(n),Cmeso→micro)

[0045] Collect and analyze the results of multi-scale iterative solutions to extract macroscopic, mesoscopic, and microscopic descriptions of system properties.

[0046] As a preferred technical solution of the present invention, the multi-physical field coupling step identifies all physical fields that affect the system, establishes control equations for each physical field, defines appropriate boundary conditions and initial conditions for each physical field, and designs an algorithm for solving multi-physical field coupling problems.

[0047] As a preferred technical solution of the present invention, the parameter estimation and data fusion step identifies the unknown parameters of the model, estimates these parameters from the data using the Bayesian method, and fuses data information from different sources into the model to improve the accuracy of model prediction.

[0048] As a preferred technical solution of the present invention, after the digital twin model is constructed, the output of the model is verified using experimental data and existing benchmark solutions. If the model output does not match the experimental data, calibration is performed by adjusting parameters and updating the model structure.

[0049] As a preferred technical solution of the present invention, real-time data is input into the digital twin model for real-time simulation analysis, and a filtering algorithm is used to estimate the real-time state of the system based on the deviation of the real-time simulation results.

[0050] As a preferred technical solution of the present invention, system definition and demand analysis are required before model construction to establish the boundary conditions of the target system including input, output and their interactions, and to describe the characteristic length and time scale of the physical phenomena that the model should capture.

[0051] The beneficial effect of adopting the above technical solution is that the process of constructing a multi-scale digital twin model significantly improves the accuracy and scope of application of the model compared with the existing technology through the technical content of multi-scale modeling, multi-physical field coupling, parameter estimation and data fusion. Specifically, the present invention allows the model to effectively communicate and integrate information between different scales, thereby ensuring the accuracy and consistency of the model at the overall and local levels; through the bridging coupling of different scale models and the Lagrange multiplier method, the consistency of macroscopic and microscopic variables is ensured on the boundary layer, and the physical behavior of complex systems can be simulated more accurately. In addition, through parameter estimation and data fusion, the model can integrate and utilize real-time data, thereby realizing dynamic optimization and prediction, which greatly enhances the practicality and flexibility of the model. Therefore, the construction of a multi-scale digital twin model not only enhances the model's ability to describe complex systems, but also improves the adaptability and prediction accuracy of the model in practical applications, providing a more efficient and dynamic method for accurately simulating and predicting the behavior of complex systems.

[0052] The following embodiments describe in detail the technical advantages and beneficial effects of various technical details of the present invention. DETAILED DESCRIPTION

[0053] The following examples describe the present invention in detail. In the description of the following examples, specific details such as specific system structures, technologies, etc. are proposed for illustration rather than limitation, so as to provide a thorough understanding of the present application embodiments. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices, circuits and methods is omitted to prevent unnecessary details from hindering the description of the present application.

[0054] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0055] As used in the specification of this application and the appended claims, the term "if" may be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" may be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context. In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0056] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0057] Example 1

[0058] The construction process of the multi-scale fluid dynamics digital twin model based on the coupling algorithm is as follows:

[0059] (1) Multiscale modeling: First, determine the macroscopic, mesoscopic, and microscopic levels of the system and establish corresponding mathematical models for each level; second, design algorithms to couple models of different scales through bridging; third, information exchange: in the contact area between the macroscopic model and the microscopic model, the velocity field and pressure field information obtained by the macroscopic model are used as the boundary conditions of the microscopic model, and the particle distribution and stress field information calculated by the microscopic model are fed back to the macroscopic model for fine-tuning the macroscopic parameters; fourth, coupling strategy: the Lagrange multiplier method is used to ensure the consistency and continuity of macroscopic and microscopic variables in the boundary layer.

[0060] (2) Multi-physics field coupling:

[0061] Identify all physical fields that affect the system, establish control equations for each physical field, define appropriate boundary conditions and initial conditions for each physical field, and design algorithms to solve multi-physics coupling problems.

[0062] First, identify and define all relevant physical fields that play a role in a given system or problem, which may include but are not limited to mechanical fields, thermal fields, electromagnetic fields, chemical fields, etc.

[0063] Secondly, for each physical field, the corresponding governing equations are established according to the principles of physics, which describe how the physical quantity changes with space and time. Fluid dynamics usually uses the Navier-Stokes equations.

[0064] Third, define appropriate boundary conditions and initial conditions. Boundary conditions describe the behavior of the physical field at the boundary of the system, such as fixed temperature, fixed pressure, no-slip conditions, etc. Initial conditions provide the state of the physical field at the beginning of the simulation, which is especially important for time-dependent problems.

[0065] Fourth, designing algorithms to solve multi-physics coupled problems requires the ability to handle multiple physical fields and their interactions simultaneously. Iterative solution strategies are required, where the solution of one field is used as the boundary condition of another field and vice versa. Numerical methods including finite elements, finite differences, and finite volumes are used to solve the governing equations.

[0066] (3) Parameter estimation and data fusion:

[0067] Identify the unknown parameters of the model, use Bayesian methods to estimate these parameters from the data, and integrate data information from different sources into the model to improve the accuracy of model predictions.

[0068] In the modeling process, we first need to determine the unknown parameters in the model and estimate the parameters by combining prior knowledge and observed data. Prior knowledge is the previous understanding or assumption about the parameters, and observed data is the data collected from experiments or the real world.

[0069] The posterior probability distribution of the parameter given the data is calculated using the Bayesian formula. This posterior distribution provides rich information about the possible values ​​of the parameter, including the most likely value of the parameter and the uncertainty of the parameter value.

[0070] Fusion of data from different sources into a model to improve the accuracy of decisions or estimates. Fusion of data in a model can enhance the predictive power and robustness of the model in many ways. Different methods are used to perform the fusion process based on the contribution and reliability of different data sources.

[0071] The combined use of parameter estimation and data fusion can maximize the performance and application value of the model in the presence of uncertainty and incomplete information.

[0072] Before building the model, it is necessary to perform system definition and demand analysis, establish the boundary conditions of the target system including input, output and their interactions, and describe the characteristic length and time scale of the physical phenomena that the model should capture. After the digital twin model is built, the output of the model is verified using experimental data and existing benchmark solutions. If the model output does not match the experimental data, it is calibrated by adjusting parameters or updating the model structure. Real-time data is input into the digital twin model for real-time simulation analysis. Based on the deviation of the real-time simulation results, a filtering algorithm is used to estimate the real-time state of the system.

[0073] Example 2

[0074] In the process of establishing mathematical models at the macro level, the goal is to describe and analyze fluid motion and related physical phenomena by solving a set of partial differential equations that can reflect the changes in fluid velocity, pressure, and other related physical quantities. First, it is necessary to clarify the specific physical environment of the problem, including the properties of the fluid, such as density and viscosity, the shape and size of the flow area, and the external forces on the fluid. On this basis, the boundary conditions and initial states of the flow problem are determined, which reflect the behavior of the fluid at the starting time and on the boundary. Secondly, a geometric model of the fluid flow area is created, which is the basis for the mathematical description of the problem. Then, the flow area is divided into small, finite units or volumes using mesh generation technology, which is the basis for subsequent numerical solution steps. Third, numerical methods such as finite difference method, finite element method or finite volume method are applied to solve the fluid dynamics equations, converting the continuous physical equations into a set of discrete equations that can be solved on a computer. Fourth, for flows with high Reynolds numbers, the fluid motion becomes extremely complex and exhibits turbulent characteristics. In this case, by introducing turbulence models such as large eddy simulation, eddy and turbulent effects in the fluid can be simulated, thereby more accurately predicting the behavior of the fluid. Through the above process, the mathematical model at the macro level can accurately describe and predict the fluid motion. It not only includes the basic dynamic equations of the fluid, but also ensures the accuracy and practicality of the model by introducing appropriate boundary conditions, initial conditions and specific physical models, as well as using advanced numerical solution techniques.

[0075] In the construction of mathematical models at the mesoscopic level, we focus on describing the dynamic process of the motion of the interface of the two-phase fluid. This process is modeled by the phase field method, in which the phase field variables are used to represent the interface between different phases. The core of the model is to consider how to drive the evolution of the interface through the properties and free energy of the material. First, the entire space is divided into multiple small grids, and each grid point has a value of a phase field variable, which describes the state of the phase interface at that point. By introducing the time step and the spatial grid size, we use the finite difference method to approximate the time derivative and the spatial gradient. This method allows us to convert the continuous physical equations into a discrete form that can be solved on a computer. Secondly, through an iterative algorithm, we solve the discrete form of the phase field equation. This process starts with a randomly generated initial state, which represents the initial configuration of the interface of the two-phase fluid. Through iterative calculations, the model gradually updates the value of the phase field variable at each grid point, thereby simulating the evolution of the interface over time. Third, in order to more accurately describe the physical phenomena at the interface, the model specifically considers the effects of curvature and surface tension on fluid motion. By adding the surface tension term to the equation, a relationship similar to the Young-Laplace equation is used to describe the relationship between the interface curvature and the pressure difference. This approach enables the model to simulate the changes in interfacial tension caused by differences in curvature, thereby more accurately predicting the movement and evolution of the two-phase interface. Through the above steps, the mesoscopic model is able to dynamically simulate and predict the behavior of the two-phase fluid interface, not only taking into account the intrinsic properties of the material and the changes in free energy, but also carefully dealing with the physical effects at the interface, such as surface tension and curvature.

[0076] The mathematical model established at the microscopic level is mainly used to describe and analyze the motion behavior of particles. The core is to consider the interaction forces between particles and the forces between particles and the walls of the container. First, the model sets the initial position and velocity of the particle based on the physical environment of the particle, such as the crystal structure, the random distribution of particles, and the Maxwell-Boltzmann velocity distribution determined by temperature. Next, the model calculates the acceleration of the particle at any time through Newton's second law. This calculation is based on the total external force on the particle, that is, the interaction force between molecules and the force between molecules and the wall of the container. With the acceleration, the model then updates the position of the particle, taking into account the displacement of the particle at the current speed and the change in displacement caused by acceleration. Subsequently, the model recalculates the force and corresponding acceleration acting on the particle based on the new particle position. This step ensures that the calculation of force and acceleration can reflect the latest changes in the particle position. Finally, the model updates the particle's velocity, taking into account the average effect of acceleration at the current and next moments. Through the above steps, the model can gradually solve the position and velocity of each particle changing with time, thereby simulating in detail the dynamic behavior of particles at the microscopic level. It not only takes into account the complex interactions between particles, but also captures the subtle changes in particle motion caused by these interactions.

[0077] When coupling models of different scales, bridge terms are introduced to transfer information between models at different levels. This information transfer involves the process from macroscopic scale to mesoscopic scale and then to microscopic scale. Each scale has its own specific bridge term, which ensures the dynamic consistency and thermodynamic consistency of information transfer between different scales.

[0078] Dynamic consistency means that no matter at which scale the system is observed, the stress state of the system should look the same. Thermodynamic consistency focuses on energy consistency, that is, the energy representation of the system should be equal at the mesoscopic and microscopic scales. It ensures that the energy conversion and transfer between different scales will not lead to energy loss or sudden increase, thus ensuring the physical authenticity of the model.

[0079] The model solution process follows a layer-by-layer refinement method from macro to micro. The specific steps are as follows: First, the state of the mesoscopic model is updated according to the current state of the macroscopic model and the corresponding bridge term. Then, in a similar way, the state of the microscopic model is updated according to the state of the mesoscopic model and its bridge term. This iterative process involves not only the update of the state, but also the transmission and update of information between different scales.

[0080] Through this method, a dynamic information feedback loop can be established between different scales, which can not only analyze the physical phenomena at each scale, but also achieve the organic fusion and consistency verification of information between the macroscopic, mesoscopic and microscopic scales, and extract a comprehensive description of the system properties from different levels from macroscopic to microscopic.

[0081] In each embodiment, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone machines, floor-standing stand-alone machines, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS networks, World Wide Internet, etc. It can be clearly understood by technicians in the relevant field that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the above-mentioned method embodiment, and will not be repeated here. In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal equipment and method can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. The functional units in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0082] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A multi-scale fluid dynamics digital twin model based on a coupling algorithm, characterized by: The construction process of the multi-scale digital twin model is as follows: (1) Multi-scale modeling: A. Determine the macroscopic, mesoscopic, and microscopic levels of the system, and establish corresponding mathematical models for each level; B. Design algorithms to couple models of different scales through bridging; C. Information exchange: In the contact area between the macro model and the micro model, the velocity field and pressure field information obtained by the macro model are used as the boundary conditions of the micro model, and the particle distribution and stress field information calculated by the micro model are fed back to the macro model for fine-tuning the macro parameters; D. Coupling strategy: Lagrange multiplier method is used to ensure the consistency and continuity of macroscopic and microscopic variables in the boundary layer; (2) Multi-physics coupling; (3) Parameter estimation and data fusion.

2. The multi-scale fluid dynamics digital twin model based on the coupling algorithm according to claim 1 is characterized in that: The mathematical model established at the macro level is as follows, and the physical phenomenon is described by partial differential equations: Among them, u is the fluid velocity field, p is the pressure field, ρ is the fluid density, μ is the dynamic viscosity, and f is the external force. is the rate of change of fluid velocity with time, is the rate at which the fluid changes with its own velocity field, represents the Laplace operator of the velocity field v, is the gradient of the internal pressure of the fluid; to ensure the conservation of the internal mass of the fluid, the continuity equation (1) Determine the physical boundary conditions and initial conditions of the problem and the physical models that need to be introduced; create a geometric model of the fluid flow area and discretize the area using a mesh generation algorithm; (2) The partial differential equations are solved using methods including the finite difference method, the finite element method, and the finite volume method. For flows with high Reynolds numbers involving turbulence effects, large eddy simulation is used as a coupling for the turbulence model.

3. The multi-scale fluid dynamics digital twin model based on coupling algorithm according to claim 1, characterized in that: The mathematical model established at the mesoscopic level is as follows: Where M(φ) is the material parameter, δF / δφ is the variational derivative, and φ(x,t) is the phase field variable, which describes the interface motion of the two-phase fluid. is the gradient operator, which means the spatial gradient operation of the phase field variable. is the divergence operator, which means the divergence operation on the gradient field; (1) Discretize the space into N grid points, define the grid spacing as Δx, and apply the explicit finite difference method to discretize the time derivative and spatial gradient operator; (2) Using an iterative algorithm to solve the discrete form of the two-dimensional phase field equation, the initial condition is a randomly generated initial value field, and the parameters are set according to the physical problem; (3) Based on the curvature effect and surface tension, an additional surface tension term is introduced at the interface, and the Young-Laplace equation is used to describe the influence of curvature on the pressure difference, and the interface tension term is processed algorithmically.

4. The multi-scale fluid dynamics digital twin model based on the coupling algorithm according to claim 1, characterized in that: The mathematical model established at the micro level is as follows: m i *d 2 r i / dt 2 =F i Among them, F i It includes the forces between molecules and the forces between molecules and the wall; among them, r i is the position vector of the ith particle, m i is its mass; the initial particle coordinates and velocities are set based on the Maxwell-Boltzmann distribution of crystal structure, random distribution and temperature, and the time evolution process is realized by integrating Newton's equations of motion: Calculate the acceleration: Update location: r i (t+Δt)=r i (t)+v i (t)Δt+0.5a i (t)Δt 2 Calculate the new force and acceleration: Update speed: v i (t+Δt)=v i (t)+0.5(a i (t)+a i (t+Δt))Δt Among them, Δt is the time step, which represents the time interval between each simulation. represents the potential energy function U with respect to position r i The gradient of represents the force on the particle; through the above steps, the position r of each particle is gradually solved i and velocity vi, satisfying the Newtonian equations of motion it is subject to.

5. The multi-scale fluid dynamics digital twin model based on coupling algorithm according to claim 1, characterized in that: Models of different scales are coupled through bridging. First, the bridging term (Ci) is defined to transfer information between scales i and j. The mathematical expression of the bridging term is set as: Dynamic consistency condition: σmacro(x,t)=σmeso(x,t) Thermodynamic consistency condition: Emeso(X,T)=Emicro(X,T) Iteratively solve the model at each level and pass the information of the corresponding level through the bridge term: From macro to meso: Xmeso(n+1)=Fmacro→meso(Xmacro(n),Cmacro→meso) From mesoscopic to microscopic: Xmicro(n+1)=Fmeso→micro(Xmeso(n),Cmeso→micro) Among them, σmacro represents the stress and deformation field at the macroscopic scale, σmeso represents the stress and deformation field at the mesoscopic scale, Emeso represents the energy and thermodynamic quantities at the mesoscopic scale, Emicro represents the energy and thermodynamic quantities at the microscopic scale, Xmeso represents the state variable at the mesoscopic scale, Fmacro represents the external action at the macroscopic scale, Cmacro→meso represents the bridging term from macroscopic to mesoscopic, Xmicro represents the state variable at the microscopic scale, Fmeso represents the external action at the mesoscopic scale, and Cmeso→micro represents the bridging term from mesoscopic to microscopic; Collect and analyze the results of multi-scale iterative solutions to extract macroscopic, mesoscopic, and microscopic descriptions of system properties.

6. The multi-scale fluid dynamics digital twin model based on coupling algorithm according to claim 1, characterized in that: The multi-physics coupling step identifies all physical fields that affect the system, establishes control equations for each physical field, defines appropriate boundary conditions and initial conditions for each physical field, and designs an algorithm for solving the multi-physics coupling problem.

7. The multi-scale fluid dynamics digital twin model based on coupling algorithm according to claim 1, characterized in that: The parameter estimation and data fusion step identifies unknown parameters of the model, estimates these parameters from the data using a Bayesian approach, and fuses data information from different sources into the model to improve the accuracy of model prediction.

8. The multi-scale fluid dynamics digital twin model based on coupling algorithm according to claim 1, characterized in that: After the digital twin model is built, the model output is verified using experimental data and existing benchmark solutions. If the model output does not match the experimental data, calibration is performed by adjusting parameters and updating the model structure.

9. The multi-scale digital twin model based on coupling algorithm according to claim 1, characterized in that: Real-time data is input into the digital twin model for real-time simulation analysis, and the real-time state of the system is estimated using a filtering algorithm based on the deviation of the real-time simulation results.

10. The multi-scale fluid dynamics digital twin model based on coupling algorithm according to claim 1, characterized in that: Before building the model, it is necessary to perform system definition and requirements analysis to establish the boundary conditions of the target system, including input, output and their interactions, and to describe the characteristic length and time scale of the physical phenomena that the model should capture.

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